diff --git a/RATapi/examples/bayes_benchmark/bayes_benchmark.ipynb b/RATapi/examples/bayes_benchmark/bayes_benchmark.ipynb new file mode 100644 index 0000000..2b59a90 --- /dev/null +++ b/RATapi/examples/bayes_benchmark/bayes_benchmark.ipynb @@ -0,0 +1,2124 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian parameter estimation for low-dimensional examples\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Bayesian procedures available in RAT ([Nested Sampler (NS)](https://en.wikipedia.org/wiki/Nested_sampling_algorithm) and [DREAM](https://doi.org/10.1016/j.envsoft.2015.08.013)) estimate the parameters of our reflectivity model - that is, they find the maximum value of \n", + "$$P((X_1, X_2, ...) = (x_1, x_2, ...) | I)$$\n", + "where $P$ is a probability measure, $X_1, X_2, ...$ are our parameters, $x_1, x_2, ...$ are proposed values of these parameters, and $I$ is our background information on the model (e.g. our data), over a range of values of $x_1, x_2, ...$ between given minimum and maximum values. It can be shown that under some weak assumptions about our data, this probability is proportional to $\\exp(-\\chi^2/2)$, where $\\chi^2$ is the chi-squared measure of fit given by the least-squares solution. [1]\n", + "\n", + "If we want to calculate $\\chi^2$ directly for a sample of $N$ values between some given minimum and maximum values for each parameter, we would have to perform $N^P$ calculations, where $P$ is the number of parameters. Of course, for large numbers of parameters, this is infeasible, hence why algorithms such as NS and DREAM have been developed to do so more efficiently. However, for a small number of parameters, it is feasible for us to perform this direct calculation and compare it to the results of NS and DREAM. Here we will do so for an example of 2 and 3 parameters.\n", + "\n", + "*[1] D. S. Sivia, J. R. P. Webster,\n", + " \"The Bayesian approach to reflectivity data\",\n", + " Physica B: Condensed Matter,\n", + " Volume 248, June 1998, pages 327-337 \n", + " DOI: 10.1016/S0921-4526(98)00259-2 \n", + " URL: https://bayes.wustl.edu/sivia/98_20feb03.pdf*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Two-parameter example\n", + "We will start with a two-dimensional example on a simple project. This project represents a bare D2O substrate, and we will estimate the true values of the substrate roughness and background signal for this project." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/alexhroom/Code/Python/python-RAT/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "import RATapi as RAT\n", + "from RATapi.models import Parameter, Background, Resolution, Data, Contrast" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "project = RAT.Project(\n", + " name=\"Bare D2O Substrate\",\n", + " calculation=\"normal\",\n", + " model=\"standard layers\",\n", + " geometry=\"air/substrate\",\n", + " absorption=\"False\",\n", + " parameters=[Parameter(name=\"Substrate Roughness\", min=3.0, value=4.844363132849221, max=8.0, fit=True)],\n", + " background_parameters=[\n", + " Parameter(name=\"Background parameter 1\", min=5e-08, value=3.069003361230152e-06, max=7e-06, fit=True)\n", + " ],\n", + " scalefactors=[Parameter(name=\"Scalefactor 1\", min=0.07, value=0.10141560336360426, max=0.13, fit=False)],\n", + " bulk_in=[Parameter(name=\"Air\", min=0.0, value=0.0, max=0.0, fit=False)],\n", + " bulk_out=[Parameter(name=\"D2O\", min=6.3e-06, value=6.35e-06, max=6.4e-06, fit=False)],\n", + " resolution_parameters=[Parameter(name=\"Resolution parameter 1\", min=0.01, value=0.03, max=0.05, fit=False)],\n", + " backgrounds=[Background(name=\"Background 1\", type=\"constant\", value_1=\"Background parameter 1\")],\n", + " resolutions=[Resolution(name=\"Resolution 1\", type=\"constant\", value_1=\"Resolution parameter 1\")],\n", + " data=[\n", + " Data(name=\"Simulation\", data=np.empty([0, 3]), simulation_range=[0.005, 0.7]),\n", + " Data(\n", + " name=\"f82395c\",\n", + " data=np.array(\n", + " [\n", + " [4.8866e-02, 1.2343e-04, 1.3213e-06],\n", + " [5.1309e-02, 1.0063e-04, 1.0803e-06],\n", + " [5.3874e-02, 8.2165e-05, 8.8779e-07],\n", + " [5.6568e-02, 6.4993e-05, 7.2018e-07],\n", + " [5.9396e-02, 5.3958e-05, 6.0015e-07],\n", + " [6.2366e-02, 4.3590e-05, 5.0129e-07],\n", + " [6.5485e-02, 3.5780e-05, 4.1957e-07],\n", + " [6.8759e-02, 2.9130e-05, 3.5171e-07],\n", + " [7.2197e-02, 2.3481e-05, 3.0586e-07],\n", + " [7.5807e-02, 1.8906e-05, 2.6344e-07],\n", + " [7.9597e-02, 1.4642e-05, 2.2314e-07],\n", + " [8.3577e-02, 1.1589e-05, 1.8938e-07],\n", + " [8.7756e-02, 9.5418e-06, 1.6220e-07],\n", + " [9.2143e-02, 7.5694e-06, 1.3809e-07],\n", + " [9.6751e-02, 6.3831e-06, 1.2097e-07],\n", + " [1.0159e-01, 5.0708e-06, 1.0333e-07],\n", + " [1.0667e-01, 4.1041e-06, 8.9548e-08],\n", + " [1.1200e-01, 3.4253e-06, 7.9830e-08],\n", + " [1.1760e-01, 2.8116e-06, 7.1554e-08],\n", + " [1.2348e-01, 2.3767e-06, 6.3738e-08],\n", + " [1.2966e-01, 1.9241e-06, 5.6586e-08],\n", + " [1.3614e-01, 1.5642e-06, 5.2778e-08],\n", + " [1.4294e-01, 1.2922e-06, 4.9730e-08],\n", + " [1.5009e-01, 1.1694e-06, 5.1175e-08],\n", + " [1.5760e-01, 9.7837e-07, 5.0755e-08],\n", + " [1.6548e-01, 8.9138e-07, 5.3542e-08],\n", + " [1.7375e-01, 7.9420e-07, 5.4857e-08],\n", + " [1.8244e-01, 7.9131e-07, 5.8067e-08],\n", + " [1.9156e-01, 6.5358e-07, 5.7717e-08],\n", + " [2.0114e-01, 6.2970e-07, 5.7951e-08],\n", + " [2.1119e-01, 5.0130e-07, 5.5262e-08],\n", + " [2.2175e-01, 5.0218e-07, 5.6461e-08],\n", + " [2.3284e-01, 3.9299e-07, 5.0685e-08],\n", + " [2.4448e-01, 3.5324e-07, 5.0194e-08],\n", + " [2.5671e-01, 4.4475e-07, 5.6485e-08],\n", + " [2.6954e-01, 5.1338e-07, 6.2247e-08],\n", + " [2.8302e-01, 3.4918e-07, 4.9745e-08],\n", + " [2.9717e-01, 4.3037e-07, 5.5488e-08],\n", + " [3.1203e-01, 4.0099e-07, 5.3591e-08],\n", + " [3.2763e-01, 3.8397e-07, 5.1303e-08],\n", + " [3.4401e-01, 3.0995e-07, 4.5965e-08],\n", + " [3.6121e-01, 3.9357e-07, 5.0135e-08],\n", + " [3.7927e-01, 3.0997e-07, 4.3680e-08],\n", + " [3.9824e-01, 2.9656e-07, 4.2432e-08],\n", + " [4.1815e-01, 2.1909e-07, 3.6117e-08],\n", + " [4.3906e-01, 2.3153e-07, 3.6307e-08],\n", + " [4.6101e-01, 3.3428e-07, 4.3874e-08],\n", + " [4.8406e-01, 2.3441e-07, 3.7488e-08],\n", + " [5.0826e-01, 1.5496e-07, 3.0585e-08],\n", + " [5.3368e-01, 2.4708e-07, 3.9376e-08],\n", + " [5.6036e-01, 2.2157e-07, 3.8258e-08],\n", + " [5.8838e-01, 2.2798e-07, 4.6976e-08],\n", + " [6.1169e-01, 6.0272e-07, 2.3239e-07],\n", + " ]\n", + " ),\n", + " data_range=[0.048866, 0.61169],\n", + " simulation_range=[0.048866, 0.61169],\n", + " ),\n", + " ],\n", + " contrasts=[\n", + " Contrast(\n", + " name=\"Chain-d, acmw\",\n", + " data=\"f82395c\",\n", + " background=\"Background 1\",\n", + " background_action=\"add\",\n", + " bulk_in=\"Air\",\n", + " bulk_out=\"D2O\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " resolution=\"Resolution 1\",\n", + " resample=False,\n", + " model=[],\n", + " )\n", + " ],\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Firstly, we will run calculations using nested sampling and DREAM." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "\n", + "Running Nested Sampler\n", + "\n", + "log(Z): -7.16104e+01, tol = 6.98054e+01, K = 1, iteration = 1, H = -nan\n", + "log(Z): -7.06250e+01, tol = 6.88180e+01, K = 1, iteration = 2, H = 3.23599e+00\n", + "log(Z): -6.94628e+01, tol = 6.76538e+01, K = 1, iteration = 3, H = 4.66504e+00\n", + "log(Z): -6.88422e+01, tol = 6.70312e+01, K = 1, iteration = 4, H = 4.69445e+00\n", + "log(Z): -6.39816e+01, tol = 6.21686e+01, K = 1, iteration = 5, H = 6.16640e+00\n", + "log(Z): -6.29837e+01, tol = 6.11687e+01, K = 1, iteration = 6, H = 5.54555e+00\n", + "log(Z): -6.21650e+01, tol = 6.03481e+01, K = 1, iteration = 7, H = 5.24064e+00\n", + "log(Z): -6.09968e+01, tol = 5.91778e+01, K = 1, iteration = 8, H = 5.30228e+00\n", + "log(Z): -5.93002e+01, tol = 5.74792e+01, K = 1, iteration = 9, H = 5.58492e+00\n", + "log(Z): -5.84434e+01, tol = 5.66204e+01, K = 1, iteration = 10, H = 5.27649e+00\n", + "log(Z): -5.77264e+01, tol = 5.59014e+01, K = 1, iteration = 11, H = 5.07451e+00\n", + "log(Z): -5.65044e+01, tol = 5.46774e+01, K = 1, iteration = 12, H = 5.28867e+00\n", + "log(Z): -5.59357e+01, tol = 5.41067e+01, K = 1, iteration = 13, H = 5.01676e+00\n", + "log(Z): -5.45097e+01, tol = 5.26787e+01, K = 1, iteration = 14, H = 5.39596e+00\n", + "log(Z): -5.35224e+01, tol = 5.16894e+01, K = 1, iteration = 15, H = 5.26748e+00\n", + "log(Z): -5.27293e+01, tol = 5.08943e+01, K = 1, iteration = 16, H = 5.11441e+00\n", + "log(Z): -5.17430e+01, tol = 4.99060e+01, K = 1, iteration = 17, H = 5.16449e+00\n", + "log(Z): -5.07905e+01, tol = 4.89515e+01, K = 1, iteration = 18, H = 5.16419e+00\n", + "log(Z): -4.97794e+01, tol = 4.79385e+01, K = 1, iteration = 19, H = 5.20036e+00\n", + "log(Z): -4.91386e+01, tol = 4.72956e+01, K = 1, iteration = 20, H = 5.00699e+00\n", + "log(Z): -4.86684e+01, tol = 4.68235e+01, K = 1, iteration = 21, H = 4.81374e+00\n", + "log(Z): -4.83054e+01, tol = 4.64584e+01, K = 1, iteration = 22, H = 4.63872e+00\n", + "log(Z): -4.80180e+01, tol = 4.61690e+01, K = 1, iteration = 23, H = 4.48149e+00\n", + "log(Z): -4.76536e+01, tol = 4.58027e+01, K = 1, iteration = 24, H = 4.40975e+00\n", + "log(Z): -4.73167e+01, tol = 4.54638e+01, K = 1, iteration = 25, H = 4.34144e+00\n", + "log(Z): -4.70563e+01, tol = 4.52013e+01, K = 1, iteration = 26, H = 4.24423e+00\n", + "log(Z): -4.68331e+01, tol = 4.49762e+01, K = 1, iteration = 27, H = 4.14850e+00\n", + "log(Z): -4.66075e+01, tol = 4.47485e+01, K = 1, iteration = 28, H = 4.07382e+00\n", + "log(Z): -4.62590e+01, tol = 4.43980e+01, K = 1, iteration = 29, H = 4.11456e+00\n", + "log(Z): -4.59649e+01, tol = 4.41019e+01, K = 1, iteration = 30, H = 4.09717e+00\n", + "log(Z): -4.56511e+01, tol = 4.37861e+01, K = 1, iteration = 31, H = 4.10126e+00\n", + "log(Z): -4.51133e+01, tol = 4.32463e+01, K = 1, iteration = 32, H = 4.32759e+00\n", + "log(Z): -4.46854e+01, tol = 4.28164e+01, K = 1, iteration = 33, H = 4.36087e+00\n", + "log(Z): -4.42422e+01, tol = 4.23712e+01, K = 1, iteration = 34, H = 4.39627e+00\n", + "log(Z): -4.39218e+01, tol = 4.20488e+01, K = 1, iteration = 35, H = 4.32633e+00\n", + "log(Z): -4.34158e+01, tol = 4.15408e+01, K = 1, iteration = 36, H = 4.43256e+00\n", + "log(Z): -4.29709e+01, tol = 4.10939e+01, K = 1, iteration = 37, H = 4.44582e+00\n", + "log(Z): -4.26287e+01, tol = 4.07497e+01, K = 1, iteration = 38, H = 4.37817e+00\n", + "log(Z): -4.22037e+01, tol = 4.03227e+01, K = 1, iteration = 39, H = 4.39560e+00\n", + "log(Z): -4.16343e+01, tol = 3.97514e+01, K = 1, iteration = 40, H = 4.53507e+00\n", + "log(Z): -4.11915e+01, tol = 3.93445e+01, K = 1, iteration = 41, H = 4.51283e+00\n", + "log(Z): -4.08860e+01, tol = 3.90370e+01, K = 1, iteration = 42, H = 4.40624e+00\n", + "log(Z): -4.06354e+01, tol = 3.87844e+01, K = 1, iteration = 43, H = 4.29693e+00\n", + "log(Z): -4.04204e+01, tol = 3.85674e+01, K = 1, iteration = 44, H = 4.19356e+00\n", + "log(Z): -4.02428e+01, tol = 3.83877e+01, K = 1, iteration = 45, H = 4.09277e+00\n", + "log(Z): -4.00432e+01, tol = 3.81861e+01, K = 1, iteration = 46, H = 4.02035e+00\n", + "log(Z): -3.98310e+01, tol = 3.79720e+01, K = 1, iteration = 47, H = 3.96968e+00\n", + "log(Z): -3.96305e+01, tol = 3.77694e+01, K = 1, iteration = 48, H = 3.92089e+00\n", + "log(Z): -3.94612e+01, tol = 3.75981e+01, K = 1, iteration = 49, H = 3.86069e+00\n", + "log(Z): -3.92707e+01, tol = 3.74057e+01, K = 1, iteration = 50, H = 3.82481e+00\n", + "log(Z): -3.90306e+01, tol = 3.71636e+01, K = 1, iteration = 51, H = 3.83797e+00\n", + "log(Z): -3.88306e+01, tol = 3.69615e+01, K = 1, iteration = 52, H = 3.81417e+00\n", + "log(Z): -3.86521e+01, tol = 3.67811e+01, K = 1, iteration = 53, H = 3.77833e+00\n", + "log(Z): -3.84905e+01, tol = 3.66174e+01, K = 1, iteration = 54, H = 3.73652e+00\n", + "log(Z): -3.83495e+01, tol = 3.64745e+01, K = 1, iteration = 55, H = 3.68747e+00\n", + "log(Z): -3.82252e+01, tol = 3.63481e+01, K = 1, iteration = 56, H = 3.63519e+00\n", + "log(Z): -3.81010e+01, tol = 3.62219e+01, K = 1, iteration = 57, H = 3.58916e+00\n", + "log(Z): -3.79368e+01, tol = 3.60558e+01, K = 1, iteration = 58, H = 3.57892e+00\n", + "log(Z): -3.77337e+01, tol = 3.58507e+01, K = 1, iteration = 59, H = 3.60777e+00\n", + "log(Z): -3.75611e+01, tol = 3.56761e+01, K = 1, iteration = 60, H = 3.60263e+00\n", + "log(Z): -3.74010e+01, tol = 3.55140e+01, K = 1, iteration = 61, H = 3.58784e+00\n", + "log(Z): -3.72300e+01, tol = 3.53410e+01, K = 1, iteration = 62, H = 3.58508e+00\n", + "log(Z): -3.70840e+01, tol = 3.51930e+01, K = 1, iteration = 63, H = 3.56193e+00\n", + "log(Z): -3.69481e+01, tol = 3.50550e+01, K = 1, iteration = 64, H = 3.53439e+00\n", + "log(Z): -3.68065e+01, tol = 3.49115e+01, K = 1, iteration = 65, H = 3.51502e+00\n", + "log(Z): -3.66289e+01, tol = 3.47318e+01, K = 1, iteration = 66, H = 3.53152e+00\n", + "log(Z): -3.64677e+01, tol = 3.45686e+01, K = 1, iteration = 67, H = 3.53002e+00\n", + "log(Z): -3.63201e+01, tol = 3.44191e+01, K = 1, iteration = 68, H = 3.51702e+00\n", + "log(Z): -3.61844e+01, tol = 3.42813e+01, K = 1, iteration = 69, H = 3.49632e+00\n", + "log(Z): -3.60552e+01, tol = 3.41502e+01, K = 1, iteration = 70, H = 3.47327e+00\n", + "log(Z): -3.59176e+01, tol = 3.40105e+01, K = 1, iteration = 71, H = 3.46019e+00\n", + "log(Z): -3.57812e+01, tol = 3.38721e+01, K = 1, iteration = 72, H = 3.44805e+00\n", + "log(Z): -3.56456e+01, tol = 3.37346e+01, K = 1, iteration = 73, H = 3.43691e+00\n", + "log(Z): -3.55186e+01, tol = 3.36056e+01, K = 1, iteration = 74, H = 3.42026e+00\n", + "log(Z): -3.54048e+01, tol = 3.34898e+01, K = 1, iteration = 75, H = 3.39549e+00\n", + "log(Z): -3.52724e+01, tol = 3.33553e+01, K = 1, iteration = 76, H = 3.38901e+00\n", + "log(Z): -3.51508e+01, tol = 3.32318e+01, K = 1, iteration = 77, H = 3.37420e+00\n", + "log(Z): -3.50424e+01, tol = 3.31213e+01, K = 1, iteration = 78, H = 3.35092e+00\n", + "log(Z): -3.48973e+01, tol = 3.29742e+01, K = 1, iteration = 79, H = 3.36298e+00\n", + "log(Z): -3.47115e+01, tol = 3.27864e+01, K = 1, iteration = 80, H = 3.41829e+00\n", + "log(Z): -3.45145e+01, tol = 3.25874e+01, K = 1, iteration = 81, H = 3.47749e+00\n", + "log(Z): -3.43449e+01, tol = 3.24158e+01, K = 1, iteration = 82, H = 3.49695e+00\n", + "log(Z): -3.41349e+01, tol = 3.22039e+01, K = 1, iteration = 83, H = 3.55754e+00\n", + "log(Z): -3.39546e+01, tol = 3.20215e+01, K = 1, iteration = 84, H = 3.57564e+00\n", + "log(Z): -3.37971e+01, tol = 3.18621e+01, K = 1, iteration = 85, H = 3.56956e+00\n", + "log(Z): -3.36551e+01, tol = 3.17180e+01, K = 1, iteration = 86, H = 3.55151e+00\n", + "log(Z): -3.35027e+01, tol = 3.15637e+01, K = 1, iteration = 87, H = 3.54494e+00\n", + "log(Z): -3.33649e+01, tol = 3.14239e+01, K = 1, iteration = 88, H = 3.52716e+00\n", + "log(Z): -3.32431e+01, tol = 3.13000e+01, K = 1, iteration = 89, H = 3.49953e+00\n", + "log(Z): -3.31214e+01, tol = 3.11764e+01, K = 1, iteration = 90, H = 3.47512e+00\n", + "log(Z): -3.29944e+01, tol = 3.10473e+01, K = 1, iteration = 91, H = 3.45800e+00\n", + "log(Z): -3.28685e+01, tol = 3.09195e+01, K = 1, iteration = 92, H = 3.44220e+00\n", + "log(Z): -3.27539e+01, tol = 3.08029e+01, K = 1, iteration = 93, H = 3.41952e+00\n", + "log(Z): -3.26434e+01, tol = 3.06903e+01, K = 1, iteration = 94, H = 3.39640e+00\n", + "log(Z): -3.25388e+01, tol = 3.05838e+01, K = 1, iteration = 95, H = 3.37146e+00\n", + "log(Z): -3.24425e+01, tol = 3.04854e+01, K = 1, iteration = 96, H = 3.34335e+00\n", + "log(Z): -3.23538e+01, tol = 3.03948e+01, K = 1, iteration = 97, H = 3.31288e+00\n", + "log(Z): -3.22676e+01, tol = 3.03065e+01, K = 1, iteration = 98, H = 3.28361e+00\n", + "log(Z): -3.21809e+01, tol = 3.02179e+01, K = 1, iteration = 99, H = 3.25720e+00\n", + "log(Z): -3.21005e+01, tol = 3.01355e+01, K = 1, iteration = 100, H = 3.22904e+00\n", + "log(Z): -3.20186e+01, tol = 3.00516e+01, K = 1, iteration = 101, H = 3.20418e+00\n", + "log(Z): -3.19414e+01, tol = 2.99723e+01, K = 1, iteration = 102, H = 3.17837e+00\n", + "log(Z): -3.18657e+01, tol = 2.98947e+01, K = 1, iteration = 103, H = 3.15355e+00\n", + "log(Z): -3.17800e+01, tol = 2.98070e+01, K = 1, iteration = 104, H = 3.13802e+00\n", + "log(Z): -3.16959e+01, tol = 2.97208e+01, K = 1, iteration = 105, H = 3.12270e+00\n", + "log(Z): -3.16176e+01, tol = 2.96406e+01, K = 1, iteration = 106, H = 3.10425e+00\n", + "log(Z): -3.15389e+01, tol = 2.95599e+01, K = 1, iteration = 107, H = 3.08766e+00\n", + "log(Z): -3.14588e+01, tol = 2.94777e+01, K = 1, iteration = 108, H = 3.07362e+00\n", + "log(Z): -3.13767e+01, tol = 2.93937e+01, K = 1, iteration = 109, H = 3.06238e+00\n", + "log(Z): -3.12919e+01, tol = 2.93069e+01, K = 1, iteration = 110, H = 3.05455e+00\n", + "log(Z): -3.12053e+01, tol = 2.92183e+01, K = 1, iteration = 111, H = 3.04908e+00\n", + "log(Z): -3.10807e+01, tol = 2.90917e+01, K = 1, iteration = 112, H = 3.08484e+00\n", + "log(Z): -3.09484e+01, tol = 2.89574e+01, K = 1, iteration = 113, H = 3.12589e+00\n", + "log(Z): -3.08289e+01, tol = 2.88358e+01, K = 1, iteration = 114, H = 3.14744e+00\n", + "log(Z): -3.07147e+01, tol = 2.87197e+01, K = 1, iteration = 115, H = 3.16094e+00\n", + "log(Z): -3.05908e+01, tol = 2.85937e+01, K = 1, iteration = 116, H = 3.18382e+00\n", + "log(Z): -3.04773e+01, tol = 2.84783e+01, K = 1, iteration = 117, H = 3.19314e+00\n", + "log(Z): -3.03692e+01, tol = 2.83682e+01, K = 1, iteration = 118, H = 3.19635e+00\n", + "log(Z): -3.02596e+01, tol = 2.82565e+01, K = 1, iteration = 119, H = 3.20093e+00\n", + "log(Z): -3.01591e+01, tol = 2.81541e+01, K = 1, iteration = 120, H = 3.19655e+00\n", + "log(Z): -3.00353e+01, tol = 2.80282e+01, K = 1, iteration = 121, H = 3.21636e+00\n", + "log(Z): -2.99187e+01, tol = 2.79096e+01, K = 1, iteration = 122, H = 3.22644e+00\n", + "log(Z): -2.97994e+01, tol = 2.77884e+01, K = 1, iteration = 123, H = 3.23837e+00\n", + "log(Z): -2.96655e+01, tol = 2.76525e+01, K = 1, iteration = 124, H = 3.26491e+00\n", + "log(Z): -2.95247e+01, tol = 2.75097e+01, K = 1, iteration = 125, H = 3.29610e+00\n", + "log(Z): -2.93982e+01, tol = 2.73811e+01, K = 1, iteration = 126, H = 3.30819e+00\n", + "log(Z): -2.92736e+01, tol = 2.72546e+01, K = 1, iteration = 127, H = 3.31703e+00\n", + "log(Z): -2.91386e+01, tol = 2.71175e+01, K = 1, iteration = 128, H = 3.33595e+00\n", + "log(Z): -2.90160e+01, tol = 2.69929e+01, K = 1, iteration = 129, H = 3.34014e+00\n", + "log(Z): -2.88873e+01, tol = 2.68623e+01, K = 1, iteration = 130, H = 3.35005e+00\n", + "log(Z): -2.87696e+01, tol = 2.67426e+01, K = 1, iteration = 131, H = 3.34844e+00\n", + "log(Z): -2.86607e+01, tol = 2.66316e+01, K = 1, iteration = 132, H = 3.33932e+00\n", + "log(Z): -2.85311e+01, tol = 2.65000e+01, K = 1, iteration = 133, H = 3.35103e+00\n", + "log(Z): -2.84076e+01, tol = 2.63745e+01, K = 1, iteration = 134, H = 3.35550e+00\n", + "log(Z): -2.82928e+01, tol = 2.62577e+01, K = 1, iteration = 135, H = 3.35148e+00\n", + "log(Z): -2.81890e+01, tol = 2.61520e+01, K = 1, iteration = 136, H = 3.33848e+00\n", + "log(Z): -2.80923e+01, tol = 2.60533e+01, K = 1, iteration = 137, H = 3.32119e+00\n", + "log(Z): -2.80019e+01, tol = 2.59609e+01, K = 1, iteration = 138, H = 3.30083e+00\n", + "log(Z): -2.79136e+01, tol = 2.58706e+01, K = 1, iteration = 139, H = 3.28085e+00\n", + "log(Z): -2.78283e+01, tol = 2.57833e+01, K = 1, iteration = 140, H = 3.26049e+00\n", + "log(Z): -2.77360e+01, tol = 2.56890e+01, K = 1, iteration = 141, H = 3.24745e+00\n", + "log(Z): -2.76515e+01, tol = 2.56025e+01, K = 1, iteration = 142, H = 3.22953e+00\n", + "log(Z): -2.75646e+01, tol = 2.55135e+01, K = 1, iteration = 143, H = 3.21513e+00\n", + "log(Z): -2.74845e+01, tol = 2.54315e+01, K = 1, iteration = 144, H = 3.19674e+00\n", + "log(Z): -2.74097e+01, tol = 2.53547e+01, K = 1, iteration = 145, H = 3.17611e+00\n", + "log(Z): -2.73374e+01, tol = 2.52804e+01, K = 1, iteration = 146, H = 3.15535e+00\n", + "log(Z): -2.72585e+01, tol = 2.51994e+01, K = 1, iteration = 147, H = 3.14117e+00\n", + "log(Z): -2.71741e+01, tol = 2.51130e+01, K = 1, iteration = 148, H = 3.13274e+00\n", + "log(Z): -2.70804e+01, tol = 2.50174e+01, K = 1, iteration = 149, H = 3.13367e+00\n", + "log(Z): -2.69889e+01, tol = 2.49238e+01, K = 1, iteration = 150, H = 3.13270e+00\n", + "log(Z): -2.69018e+01, tol = 2.48348e+01, K = 1, iteration = 151, H = 3.12779e+00\n", + "log(Z): -2.68192e+01, tol = 2.47502e+01, K = 1, iteration = 152, H = 3.11954e+00\n", + "log(Z): -2.67201e+01, tol = 2.46491e+01, K = 1, iteration = 153, H = 3.12785e+00\n", + "log(Z): -2.66141e+01, tol = 2.45411e+01, K = 1, iteration = 154, H = 3.14283e+00\n", + "log(Z): -2.65172e+01, tol = 2.44421e+01, K = 1, iteration = 155, H = 3.14715e+00\n", + "log(Z): -2.64240e+01, tol = 2.43470e+01, K = 1, iteration = 156, H = 3.14757e+00\n", + "log(Z): -2.63374e+01, tol = 2.42583e+01, K = 1, iteration = 157, H = 3.14204e+00\n", + "log(Z): -2.62535e+01, tol = 2.41724e+01, K = 1, iteration = 158, H = 3.13474e+00\n", + "log(Z): -2.61748e+01, tol = 2.40917e+01, K = 1, iteration = 159, H = 3.12374e+00\n", + "log(Z): -2.60654e+01, tol = 2.39803e+01, K = 1, iteration = 160, H = 3.14412e+00\n", + "log(Z): -2.59602e+01, tol = 2.38732e+01, K = 1, iteration = 161, H = 3.15797e+00\n", + "log(Z): -2.58622e+01, tol = 2.37732e+01, K = 1, iteration = 162, H = 3.16320e+00\n", + "log(Z): -2.57719e+01, tol = 2.36808e+01, K = 1, iteration = 163, H = 3.16080e+00\n", + "log(Z): -2.56741e+01, tol = 2.35810e+01, K = 1, iteration = 164, H = 3.16599e+00\n", + "log(Z): -2.55793e+01, tol = 2.34843e+01, K = 1, iteration = 165, H = 3.16787e+00\n", + "log(Z): -2.54920e+01, tol = 2.33950e+01, K = 1, iteration = 166, H = 3.16277e+00\n", + "log(Z): -2.54059e+01, tol = 2.33068e+01, K = 1, iteration = 167, H = 3.15722e+00\n", + "log(Z): -2.53262e+01, tol = 2.32252e+01, K = 1, iteration = 168, H = 3.14666e+00\n", + "log(Z): -2.52434e+01, tol = 2.31404e+01, K = 1, iteration = 169, H = 3.13975e+00\n", + "log(Z): -2.51586e+01, tol = 2.30535e+01, K = 1, iteration = 170, H = 3.13543e+00\n", + "log(Z): -2.50800e+01, tol = 2.29729e+01, K = 1, iteration = 171, H = 3.12609e+00\n", + "log(Z): -2.50058e+01, tol = 2.28968e+01, K = 1, iteration = 172, H = 3.11394e+00\n", + "log(Z): -2.49335e+01, tol = 2.28224e+01, K = 1, iteration = 173, H = 3.10130e+00\n", + "log(Z): -2.48649e+01, tol = 2.27519e+01, K = 1, iteration = 174, H = 3.08670e+00\n", + "log(Z): -2.47887e+01, tol = 2.26737e+01, K = 1, iteration = 175, H = 3.07957e+00\n", + "log(Z): -2.47131e+01, tol = 2.25961e+01, K = 1, iteration = 176, H = 3.07250e+00\n", + "log(Z): -2.46346e+01, tol = 2.25156e+01, K = 1, iteration = 177, H = 3.06877e+00\n", + "log(Z): -2.45573e+01, tol = 2.24363e+01, K = 1, iteration = 178, H = 3.06433e+00\n", + "log(Z): -2.44669e+01, tol = 2.23438e+01, K = 1, iteration = 179, H = 3.07335e+00\n", + "log(Z): -2.43836e+01, tol = 2.22585e+01, K = 1, iteration = 180, H = 3.07452e+00\n", + "log(Z): -2.43061e+01, tol = 2.21791e+01, K = 1, iteration = 181, H = 3.07022e+00\n", + "log(Z): -2.42327e+01, tol = 2.21036e+01, K = 1, iteration = 182, H = 3.06287e+00\n", + "log(Z): -2.41590e+01, tol = 2.20280e+01, K = 1, iteration = 183, H = 3.05636e+00\n", + "log(Z): -2.40798e+01, tol = 2.19468e+01, K = 1, iteration = 184, H = 3.05551e+00\n", + "log(Z): -2.40024e+01, tol = 2.18673e+01, K = 1, iteration = 185, H = 3.05328e+00\n", + "log(Z): -2.39304e+01, tol = 2.17933e+01, K = 1, iteration = 186, H = 3.04640e+00\n", + "log(Z): -2.38621e+01, tol = 2.17231e+01, K = 1, iteration = 187, H = 3.03690e+00\n", + "log(Z): -2.37953e+01, tol = 2.16542e+01, K = 1, iteration = 188, H = 3.02706e+00\n", + "log(Z): -2.37279e+01, tol = 2.15849e+01, K = 1, iteration = 189, H = 3.01835e+00\n", + "log(Z): -2.36588e+01, tol = 2.15138e+01, K = 1, iteration = 190, H = 3.01186e+00\n", + "log(Z): -2.35920e+01, tol = 2.14450e+01, K = 1, iteration = 191, H = 3.00396e+00\n", + "log(Z): -2.35283e+01, tol = 2.13792e+01, K = 1, iteration = 192, H = 2.99410e+00\n", + "log(Z): -2.34645e+01, tol = 2.13134e+01, K = 1, iteration = 193, H = 2.98505e+00\n", + "log(Z): -2.33988e+01, tol = 2.12458e+01, K = 1, iteration = 194, H = 2.97823e+00\n", + "log(Z): -2.33364e+01, tol = 2.11814e+01, K = 1, iteration = 195, H = 2.96924e+00\n", + "log(Z): -2.32770e+01, tol = 2.11200e+01, K = 1, iteration = 196, H = 2.95845e+00\n", + "log(Z): -2.32197e+01, tol = 2.10606e+01, K = 1, iteration = 197, H = 2.94676e+00\n", + "log(Z): -2.31652e+01, tol = 2.10041e+01, K = 1, iteration = 198, H = 2.93367e+00\n", + "log(Z): -2.31095e+01, tol = 2.09465e+01, K = 1, iteration = 199, H = 2.92225e+00\n", + "log(Z): -2.30511e+01, tol = 2.08861e+01, K = 1, iteration = 200, H = 2.91379e+00\n", + "log(Z): -2.29921e+01, tol = 2.08251e+01, K = 1, iteration = 201, H = 2.90640e+00\n", + "log(Z): -2.29364e+01, tol = 2.07674e+01, K = 1, iteration = 202, H = 2.89685e+00\n", + "log(Z): -2.28815e+01, tol = 2.07105e+01, K = 1, iteration = 203, H = 2.88727e+00\n", + "log(Z): -2.28262e+01, tol = 2.06532e+01, K = 1, iteration = 204, H = 2.87858e+00\n", + "log(Z): -2.27718e+01, tol = 2.05967e+01, K = 1, iteration = 205, H = 2.86983e+00\n", + "log(Z): -2.27193e+01, tol = 2.05423e+01, K = 1, iteration = 206, H = 2.85999e+00\n", + "log(Z): -2.26687e+01, tol = 2.04896e+01, K = 1, iteration = 207, H = 2.84938e+00\n", + "log(Z): -2.26178e+01, tol = 2.04368e+01, K = 1, iteration = 208, H = 2.83951e+00\n", + "log(Z): -2.25694e+01, tol = 2.03863e+01, K = 1, iteration = 209, H = 2.82839e+00\n", + "log(Z): -2.25219e+01, tol = 2.03368e+01, K = 1, iteration = 210, H = 2.81717e+00\n", + "log(Z): -2.24751e+01, tol = 2.02880e+01, K = 1, iteration = 211, H = 2.80602e+00\n", + "log(Z): -2.24256e+01, tol = 2.02365e+01, K = 1, iteration = 212, H = 2.79761e+00\n", + "log(Z): -2.23770e+01, tol = 2.01859e+01, K = 1, iteration = 213, H = 2.78896e+00\n", + "log(Z): -2.23305e+01, tol = 2.01374e+01, K = 1, iteration = 214, H = 2.77917e+00\n", + "log(Z): -2.22841e+01, tol = 2.00891e+01, K = 1, iteration = 215, H = 2.76978e+00\n", + "log(Z): -2.22372e+01, tol = 2.00402e+01, K = 1, iteration = 216, H = 2.76137e+00\n", + "log(Z): -2.21911e+01, tol = 1.99921e+01, K = 1, iteration = 217, H = 2.75276e+00\n", + "log(Z): -2.21470e+01, tol = 1.99460e+01, K = 1, iteration = 218, H = 2.74305e+00\n", + "log(Z): -2.21005e+01, tol = 1.98975e+01, K = 1, iteration = 219, H = 2.73583e+00\n", + "log(Z): -2.20514e+01, tol = 1.98464e+01, K = 1, iteration = 220, H = 2.73119e+00\n", + "log(Z): -2.19993e+01, tol = 1.97923e+01, K = 1, iteration = 221, H = 2.72965e+00\n", + "log(Z): -2.19485e+01, tol = 1.97394e+01, K = 1, iteration = 222, H = 2.72715e+00\n", + "log(Z): -2.18974e+01, tol = 1.96863e+01, K = 1, iteration = 223, H = 2.72508e+00\n", + "log(Z): -2.18477e+01, tol = 1.96347e+01, K = 1, iteration = 224, H = 2.72184e+00\n", + "log(Z): -2.17980e+01, tol = 1.95829e+01, K = 1, iteration = 225, H = 2.71898e+00\n", + "log(Z): -2.17490e+01, tol = 1.95319e+01, K = 1, iteration = 226, H = 2.71567e+00\n", + "log(Z): 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K = 1, iteration = 238, H = 2.69900e+00\n", + "log(Z): -2.11058e+01, tol = 1.88627e+01, K = 1, iteration = 239, H = 2.69477e+00\n", + "log(Z): -2.10616e+01, tol = 1.88166e+01, K = 1, iteration = 240, H = 2.68952e+00\n", + "log(Z): -2.10146e+01, tol = 1.87675e+01, K = 1, iteration = 241, H = 2.68721e+00\n", + "log(Z): -2.09690e+01, tol = 1.87199e+01, K = 1, iteration = 242, H = 2.68372e+00\n", + "log(Z): -2.09241e+01, tol = 1.86730e+01, K = 1, iteration = 243, H = 2.67988e+00\n", + "log(Z): -2.08807e+01, tol = 1.86276e+01, K = 1, iteration = 244, H = 2.67500e+00\n", + "log(Z): -2.08375e+01, tol = 1.85824e+01, K = 1, iteration = 245, H = 2.67018e+00\n", + "log(Z): -2.07958e+01, tol = 1.85387e+01, K = 1, iteration = 246, H = 2.66440e+00\n", + "log(Z): -2.07548e+01, tol = 1.84958e+01, K = 1, iteration = 247, H = 2.65831e+00\n", + "log(Z): -2.07145e+01, tol = 1.84535e+01, K = 1, iteration = 248, H = 2.65197e+00\n", + "log(Z): -2.06731e+01, tol = 1.84101e+01, K = 1, iteration = 249, H = 2.64691e+00\n", + "log(Z): -2.06322e+01, tol = 1.83672e+01, K = 1, iteration = 250, H = 2.64170e+00\n", + "log(Z): -2.05928e+01, tol = 1.83258e+01, K = 1, iteration = 251, H = 2.63549e+00\n", + "log(Z): -2.05538e+01, tol = 1.82847e+01, K = 1, iteration = 252, H = 2.62934e+00\n", + "log(Z): -2.05150e+01, tol = 1.82440e+01, K = 1, iteration = 253, H = 2.62322e+00\n", + "log(Z): -2.04739e+01, tol = 1.82009e+01, K = 1, iteration = 254, H = 2.61947e+00\n", + "log(Z): -2.04285e+01, tol = 1.81535e+01, K = 1, iteration = 255, H = 2.62000e+00\n", + "log(Z): -2.03819e+01, tol = 1.81048e+01, K = 1, iteration = 256, H = 2.62184e+00\n", + "log(Z): -2.03307e+01, tol = 1.80516e+01, K = 1, iteration = 257, H = 2.62854e+00\n", + "log(Z): -2.02808e+01, tol = 1.79997e+01, K = 1, iteration = 258, H = 2.63352e+00\n", + "log(Z): -2.02293e+01, tol = 1.79462e+01, K = 1, iteration = 259, H = 2.64018e+00\n", + "log(Z): -2.01800e+01, tol = 1.78950e+01, K = 1, iteration = 260, H = 2.64414e+00\n", + "log(Z): 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K = 1, iteration = 272, H = 2.70188e+00\n", + "log(Z): -1.95182e+01, tol = 1.72071e+01, K = 1, iteration = 273, H = 2.70003e+00\n", + "log(Z): -1.94733e+01, tol = 1.71603e+01, K = 1, iteration = 274, H = 2.69814e+00\n", + "log(Z): -1.94296e+01, tol = 1.71146e+01, K = 1, iteration = 275, H = 2.69538e+00\n", + "log(Z): -1.93873e+01, tol = 1.70703e+01, K = 1, iteration = 276, H = 2.69155e+00\n", + "log(Z): -1.93451e+01, tol = 1.70260e+01, K = 1, iteration = 277, H = 2.68789e+00\n", + "log(Z): -1.93039e+01, tol = 1.69828e+01, K = 1, iteration = 278, H = 2.68354e+00\n", + "log(Z): -1.92616e+01, tol = 1.69385e+01, K = 1, iteration = 279, H = 2.68044e+00\n", + "log(Z): -1.92154e+01, tol = 1.68903e+01, K = 1, iteration = 280, H = 2.68124e+00\n", + "log(Z): -1.91710e+01, tol = 1.68439e+01, K = 1, iteration = 281, H = 2.68038e+00\n", + "log(Z): -1.91272e+01, tol = 1.67982e+01, K = 1, iteration = 282, H = 2.67899e+00\n", + "log(Z): -1.90843e+01, tol = 1.67533e+01, K = 1, iteration = 283, H = 2.67699e+00\n", + "log(Z): -1.90421e+01, tol = 1.67091e+01, K = 1, iteration = 284, H = 2.67446e+00\n", + "log(Z): -1.89996e+01, tol = 1.66645e+01, K = 1, iteration = 285, H = 2.67248e+00\n", + "log(Z): -1.89586e+01, tol = 1.66216e+01, K = 1, iteration = 286, H = 2.66916e+00\n", + "log(Z): -1.89156e+01, tol = 1.65766e+01, K = 1, iteration = 287, H = 2.66800e+00\n", + "log(Z): -1.88720e+01, tol = 1.65310e+01, K = 1, iteration = 288, H = 2.66750e+00\n", + "log(Z): -1.88296e+01, tol = 1.64865e+01, K = 1, iteration = 289, H = 2.66602e+00\n", + "log(Z): -1.87889e+01, tol = 1.64438e+01, K = 1, iteration = 290, H = 2.66306e+00\n", + "log(Z): -1.87471e+01, tol = 1.64001e+01, K = 1, iteration = 291, H = 2.66132e+00\n", + "log(Z): -1.87060e+01, tol = 1.63569e+01, K = 1, iteration = 292, H = 2.65910e+00\n", + "log(Z): -1.86647e+01, tol = 1.63136e+01, K = 1, iteration = 293, H = 2.65723e+00\n", + "log(Z): -1.86248e+01, tol = 1.62717e+01, K = 1, iteration = 294, H = 2.65420e+00\n", + "log(Z): 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K = 1, iteration = 306, H = 2.60649e+00\n", + "log(Z): -1.81425e+01, tol = 1.57635e+01, K = 1, iteration = 307, H = 2.60342e+00\n", + "log(Z): -1.81035e+01, tol = 1.57225e+01, K = 1, iteration = 308, H = 2.60274e+00\n", + "log(Z): -1.80655e+01, tol = 1.56824e+01, K = 1, iteration = 309, H = 2.60124e+00\n", + "log(Z): -1.80282e+01, tol = 1.56431e+01, K = 1, iteration = 310, H = 2.59916e+00\n", + "log(Z): -1.79919e+01, tol = 1.56049e+01, K = 1, iteration = 311, H = 2.59629e+00\n", + "log(Z): -1.79547e+01, tol = 1.55657e+01, K = 1, iteration = 312, H = 2.59446e+00\n", + "log(Z): -1.79182e+01, tol = 1.55271e+01, K = 1, iteration = 313, H = 2.59215e+00\n", + "log(Z): -1.78812e+01, tol = 1.54882e+01, K = 1, iteration = 314, H = 2.59039e+00\n", + "log(Z): -1.78456e+01, tol = 1.54505e+01, K = 1, iteration = 315, H = 2.58756e+00\n", + "log(Z): -1.78100e+01, tol = 1.54129e+01, K = 1, iteration = 316, H = 2.58483e+00\n", + "log(Z): -1.77746e+01, tol = 1.53756e+01, K = 1, iteration = 317, H = 2.58208e+00\n", + "log(Z): -1.77395e+01, tol = 1.53385e+01, K = 1, iteration = 318, H = 2.57924e+00\n", + "log(Z): -1.77054e+01, tol = 1.53024e+01, K = 1, iteration = 319, H = 2.57568e+00\n", + "log(Z): -1.76722e+01, tol = 1.52671e+01, K = 1, iteration = 320, H = 2.57155e+00\n", + "log(Z): -1.76396e+01, tol = 1.52326e+01, K = 1, iteration = 321, H = 2.56705e+00\n", + "log(Z): -1.76078e+01, tol = 1.51988e+01, K = 1, iteration = 322, H = 2.56213e+00\n", + "log(Z): -1.75766e+01, tol = 1.51655e+01, K = 1, iteration = 323, H = 2.55694e+00\n", + "log(Z): -1.75446e+01, tol = 1.51315e+01, K = 1, iteration = 324, H = 2.55263e+00\n", + "log(Z): -1.75116e+01, tol = 1.50966e+01, K = 1, iteration = 325, H = 2.54930e+00\n", + "log(Z): -1.74774e+01, tol = 1.50604e+01, K = 1, iteration = 326, H = 2.54730e+00\n", + "log(Z): -1.74407e+01, tol = 1.50216e+01, K = 1, iteration = 327, H = 2.54792e+00\n", + "log(Z): -1.74052e+01, tol = 1.49842e+01, K = 1, iteration = 328, H = 2.54728e+00\n", + "log(Z): 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K = 1, iteration = 340, H = 2.51766e+00\n", + "log(Z): -1.69832e+01, tol = 1.45362e+01, K = 1, iteration = 341, H = 2.51429e+00\n", + "log(Z): -1.69526e+01, tol = 1.45035e+01, K = 1, iteration = 342, H = 2.51122e+00\n", + "log(Z): -1.69197e+01, tol = 1.44687e+01, K = 1, iteration = 343, H = 2.51035e+00\n", + "log(Z): -1.68873e+01, tol = 1.44343e+01, K = 1, iteration = 344, H = 2.50911e+00\n", + "log(Z): -1.68549e+01, tol = 1.43998e+01, K = 1, iteration = 345, H = 2.50801e+00\n", + "log(Z): -1.68227e+01, tol = 1.43656e+01, K = 1, iteration = 346, H = 2.50681e+00\n", + "log(Z): -1.67912e+01, tol = 1.43322e+01, K = 1, iteration = 347, H = 2.50502e+00\n", + "log(Z): -1.67607e+01, tol = 1.42996e+01, K = 1, iteration = 348, H = 2.50247e+00\n", + "log(Z): -1.67304e+01, tol = 1.42674e+01, K = 1, iteration = 349, H = 2.49983e+00\n", + "log(Z): -1.66992e+01, tol = 1.42341e+01, K = 1, iteration = 350, H = 2.49822e+00\n", + "log(Z): -1.66686e+01, tol = 1.42016e+01, K = 1, iteration = 351, H = 2.49606e+00\n", + "log(Z): -1.66385e+01, tol = 1.41694e+01, K = 1, iteration = 352, H = 2.49371e+00\n", + "log(Z): -1.66084e+01, tol = 1.41373e+01, K = 1, iteration = 353, H = 2.49142e+00\n", + "log(Z): -1.65787e+01, tol = 1.41057e+01, K = 1, iteration = 354, H = 2.48885e+00\n", + "log(Z): -1.65492e+01, tol = 1.40741e+01, K = 1, iteration = 355, H = 2.48630e+00\n", + "log(Z): -1.65200e+01, tol = 1.40430e+01, K = 1, iteration = 356, H = 2.48357e+00\n", + "log(Z): -1.64911e+01, tol = 1.40121e+01, K = 1, iteration = 357, H = 2.48069e+00\n", + "log(Z): -1.64627e+01, tol = 1.39816e+01, K = 1, iteration = 358, H = 2.47758e+00\n", + "log(Z): -1.64314e+01, tol = 1.39483e+01, K = 1, iteration = 359, H = 2.47726e+00\n", + "log(Z): -1.64009e+01, tol = 1.39159e+01, K = 1, iteration = 360, H = 2.47621e+00\n", + "log(Z): -1.63711e+01, tol = 1.38840e+01, K = 1, iteration = 361, H = 2.47469e+00\n", + "log(Z): -1.63398e+01, tol = 1.38508e+01, K = 1, iteration = 362, H = 2.47458e+00\n", + "log(Z): 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K = 1, iteration = 374, H = 2.48973e+00\n", + "log(Z): -1.59198e+01, tol = 1.34048e+01, K = 1, iteration = 375, H = 2.49206e+00\n", + "log(Z): -1.58868e+01, tol = 1.33698e+01, K = 1, iteration = 376, H = 2.49408e+00\n", + "log(Z): -1.58539e+01, tol = 1.33348e+01, K = 1, iteration = 377, H = 2.49606e+00\n", + "log(Z): -1.58215e+01, tol = 1.33005e+01, K = 1, iteration = 378, H = 2.49741e+00\n", + "log(Z): -1.57899e+01, tol = 1.32668e+01, K = 1, iteration = 379, H = 2.49807e+00\n", + "log(Z): -1.57587e+01, tol = 1.32337e+01, K = 1, iteration = 380, H = 2.49827e+00\n", + "log(Z): -1.57276e+01, tol = 1.32005e+01, K = 1, iteration = 381, H = 2.49853e+00\n", + "log(Z): -1.56968e+01, tol = 1.31677e+01, K = 1, iteration = 382, H = 2.49850e+00\n", + "log(Z): -1.56662e+01, tol = 1.31351e+01, K = 1, iteration = 383, H = 2.49833e+00\n", + "log(Z): -1.56361e+01, tol = 1.31031e+01, K = 1, iteration = 384, H = 2.49767e+00\n", + "log(Z): -1.56070e+01, tol = 1.30720e+01, K = 1, iteration = 385, H = 2.49623e+00\n", + "log(Z): -1.55776e+01, tol = 1.30405e+01, K = 1, iteration = 386, H = 2.49516e+00\n", + "log(Z): -1.55490e+01, tol = 1.30099e+01, K = 1, iteration = 387, H = 2.49344e+00\n", + "log(Z): -1.55212e+01, tol = 1.29802e+01, K = 1, iteration = 388, H = 2.49104e+00\n", + "log(Z): -1.54925e+01, tol = 1.29495e+01, K = 1, iteration = 389, H = 2.48957e+00\n", + "log(Z): -1.54646e+01, tol = 1.29196e+01, K = 1, iteration = 390, H = 2.48753e+00\n", + "log(Z): -1.54369e+01, tol = 1.28899e+01, K = 1, iteration = 391, H = 2.48538e+00\n", + "log(Z): -1.54088e+01, tol = 1.28598e+01, K = 1, iteration = 392, H = 2.48372e+00\n", + "log(Z): -1.53813e+01, tol = 1.28303e+01, K = 1, iteration = 393, H = 2.48160e+00\n", + "log(Z): -1.53545e+01, tol = 1.28015e+01, K = 1, iteration = 394, H = 2.47901e+00\n", + "log(Z): -1.53281e+01, tol = 1.27730e+01, K = 1, iteration = 395, H = 2.47617e+00\n", + "log(Z): -1.53023e+01, tol = 1.27453e+01, K = 1, iteration = 396, H = 2.47289e+00\n", + "log(Z): -1.52768e+01, tol = 1.27178e+01, K = 1, iteration = 397, H = 2.46947e+00\n", + "log(Z): -1.52513e+01, tol = 1.26903e+01, K = 1, iteration = 398, H = 2.46623e+00\n", + "log(Z): -1.52262e+01, tol = 1.26632e+01, K = 1, iteration = 399, H = 2.46279e+00\n", + "log(Z): -1.52018e+01, tol = 1.26367e+01, K = 1, iteration = 400, H = 2.45892e+00\n", + "log(Z): -1.51775e+01, tol = 1.26104e+01, K = 1, iteration = 401, H = 2.45506e+00\n", + "log(Z): -1.51535e+01, tol = 1.25844e+01, K = 1, iteration = 402, H = 2.45110e+00\n", + "log(Z): -1.51280e+01, tol = 1.25570e+01, K = 1, iteration = 403, H = 2.44850e+00\n", + "log(Z): -1.51027e+01, tol = 1.25297e+01, K = 1, iteration = 404, H = 2.44593e+00\n", + "log(Z): -1.50770e+01, tol = 1.25020e+01, K = 1, iteration = 405, H = 2.44379e+00\n", + "log(Z): -1.50518e+01, tol = 1.24748e+01, K = 1, iteration = 406, H = 2.44132e+00\n", + "log(Z): -1.50270e+01, tol = 1.24480e+01, K = 1, iteration = 407, H = 2.43863e+00\n", + "log(Z): -1.50023e+01, tol = 1.24213e+01, K = 1, iteration = 408, H = 2.43594e+00\n", + "log(Z): -1.49771e+01, tol = 1.23941e+01, K = 1, iteration = 409, H = 2.43378e+00\n", + "log(Z): -1.49525e+01, tol = 1.23674e+01, K = 1, iteration = 410, H = 2.43128e+00\n", + "log(Z): -1.49282e+01, tol = 1.23411e+01, K = 1, iteration = 411, H = 2.42858e+00\n", + "log(Z): -1.49032e+01, tol = 1.23142e+01, K = 1, iteration = 412, H = 2.42657e+00\n", + "log(Z): -1.48785e+01, tol = 1.22875e+01, K = 1, iteration = 413, H = 2.42444e+00\n", + "log(Z): -1.48539e+01, tol = 1.22608e+01, K = 1, iteration = 414, H = 2.42235e+00\n", + "log(Z): -1.48286e+01, tol = 1.22335e+01, K = 1, iteration = 415, H = 2.42094e+00\n", + "log(Z): -1.48035e+01, tol = 1.22065e+01, K = 1, iteration = 416, H = 2.41940e+00\n", + "log(Z): -1.47788e+01, tol = 1.21797e+01, K = 1, iteration = 417, H = 2.41769e+00\n", + "log(Z): -1.47541e+01, tol = 1.21531e+01, K = 1, iteration = 418, H = 2.41593e+00\n", + "log(Z): -1.47295e+01, tol = 1.21265e+01, K = 1, iteration = 419, H = 2.41425e+00\n", + "log(Z): -1.47051e+01, tol = 1.21000e+01, K = 1, iteration = 420, H = 2.41252e+00\n", + "log(Z): -1.46807e+01, tol = 1.20737e+01, K = 1, iteration = 421, H = 2.41076e+00\n", + "log(Z): -1.46551e+01, tol = 1.20461e+01, K = 1, iteration = 422, H = 2.41031e+00\n", + "log(Z): -1.46302e+01, tol = 1.20191e+01, K = 1, iteration = 423, H = 2.40928e+00\n", + "log(Z): -1.46050e+01, tol = 1.19920e+01, K = 1, iteration = 424, H = 2.40854e+00\n", + "log(Z): -1.45802e+01, tol = 1.19652e+01, K = 1, iteration = 425, H = 2.40750e+00\n", + "log(Z): -1.45558e+01, tol = 1.19388e+01, K = 1, iteration = 426, H = 2.40618e+00\n", + "log(Z): -1.45317e+01, tol = 1.19127e+01, K = 1, iteration = 427, H = 2.40468e+00\n", + "log(Z): -1.45076e+01, tol = 1.18866e+01, K = 1, iteration = 428, H = 2.40323e+00\n", + "log(Z): -1.44835e+01, tol = 1.18605e+01, K = 1, iteration = 429, H = 2.40187e+00\n", + "log(Z): -1.44598e+01, tol = 1.18348e+01, K = 1, iteration = 430, H = 2.40020e+00\n", + "log(Z): -1.44362e+01, tol = 1.18092e+01, K = 1, iteration = 431, H = 2.39852e+00\n", + "log(Z): -1.44129e+01, tol = 1.17839e+01, K = 1, iteration = 432, H = 2.39669e+00\n", + "log(Z): -1.43895e+01, tol = 1.17585e+01, K = 1, iteration = 433, H = 2.39504e+00\n", + "log(Z): -1.43663e+01, tol = 1.17332e+01, K = 1, iteration = 434, H = 2.39331e+00\n", + "log(Z): -1.43431e+01, tol = 1.17080e+01, K = 1, iteration = 435, H = 2.39163e+00\n", + "log(Z): -1.43182e+01, tol = 1.16812e+01, K = 1, iteration = 436, H = 2.39162e+00\n", + "log(Z): -1.42937e+01, tol = 1.16547e+01, K = 1, iteration = 437, H = 2.39129e+00\n", + "log(Z): -1.42694e+01, tol = 1.16284e+01, K = 1, iteration = 438, H = 2.39079e+00\n", + "log(Z): -1.42457e+01, tol = 1.16027e+01, K = 1, iteration = 439, H = 2.38987e+00\n", + "log(Z): -1.42225e+01, tol = 1.15774e+01, K = 1, iteration = 440, H = 2.38854e+00\n", + "log(Z): -1.41988e+01, tol = 1.15518e+01, K = 1, iteration = 441, H = 2.38764e+00\n", + "log(Z): -1.41758e+01, tol = 1.15268e+01, K = 1, iteration = 442, H = 2.38626e+00\n", + "log(Z): -1.41528e+01, tol = 1.15017e+01, K = 1, iteration = 443, H = 2.38496e+00\n", + "log(Z): -1.41295e+01, tol = 1.14764e+01, K = 1, iteration = 444, H = 2.38396e+00\n", + "log(Z): -1.41063e+01, tol = 1.14512e+01, K = 1, iteration = 445, H = 2.38294e+00\n", + "log(Z): -1.40836e+01, tol = 1.14265e+01, K = 1, iteration = 446, H = 2.38154e+00\n", + "log(Z): -1.40613e+01, tol = 1.14023e+01, K = 1, iteration = 447, H = 2.37981e+00\n", + "log(Z): -1.40390e+01, tol = 1.13780e+01, K = 1, iteration = 448, H = 2.37815e+00\n", + "log(Z): -1.40169e+01, tol = 1.13539e+01, K = 1, iteration = 449, H = 2.37644e+00\n", + "log(Z): -1.39949e+01, tol = 1.13299e+01, K = 1, iteration = 450, H = 2.37474e+00\n", + "log(Z): -1.39724e+01, tol = 1.13054e+01, K = 1, iteration = 451, H = 2.37352e+00\n", + "log(Z): -1.39493e+01, tol = 1.12803e+01, K = 1, iteration = 452, H = 2.37298e+00\n", + "log(Z): -1.39263e+01, tol = 1.12553e+01, K = 1, iteration = 453, H = 2.37240e+00\n", + "log(Z): -1.39036e+01, tol = 1.12306e+01, K = 1, iteration = 454, H = 2.37160e+00\n", + "log(Z): -1.38813e+01, tol = 1.12062e+01, K = 1, iteration = 455, H = 2.37052e+00\n", + "log(Z): -1.38590e+01, tol = 1.11820e+01, K = 1, iteration = 456, H = 2.36941e+00\n", + "log(Z): -1.38372e+01, tol = 1.11582e+01, K = 1, iteration = 457, H = 2.36798e+00\n", + "log(Z): -1.38156e+01, tol = 1.11345e+01, K = 1, iteration = 458, H = 2.36643e+00\n", + "log(Z): -1.37937e+01, tol = 1.11107e+01, K = 1, iteration = 459, H = 2.36520e+00\n", + "log(Z): -1.37717e+01, tol = 1.10867e+01, K = 1, iteration = 460, H = 2.36414e+00\n", + "log(Z): -1.37499e+01, tol = 1.10629e+01, K = 1, iteration = 461, H = 2.36296e+00\n", + "log(Z): -1.37276e+01, tol = 1.10385e+01, K = 1, iteration = 462, H = 2.36236e+00\n", + "log(Z): -1.37050e+01, tol = 1.10140e+01, K = 1, iteration = 463, H = 2.36201e+00\n", + "log(Z): -1.36823e+01, tol = 1.09893e+01, K = 1, iteration = 464, H = 2.36190e+00\n", + "log(Z): -1.36598e+01, tol = 1.09648e+01, K = 1, iteration = 465, H = 2.36162e+00\n", + "log(Z): -1.36376e+01, tol = 1.09406e+01, K = 1, iteration = 466, H = 2.36110e+00\n", + "log(Z): -1.36156e+01, tol = 1.09165e+01, K = 1, iteration = 467, H = 2.36049e+00\n", + "log(Z): -1.35939e+01, tol = 1.08929e+01, K = 1, iteration = 468, H = 2.35954e+00\n", + "log(Z): -1.35724e+01, tol = 1.08694e+01, K = 1, iteration = 469, H = 2.35856e+00\n", + "log(Z): -1.35512e+01, tol = 1.08462e+01, K = 1, iteration = 470, H = 2.35731e+00\n", + "log(Z): -1.35303e+01, tol = 1.08233e+01, K = 1, iteration = 471, H = 2.35586e+00\n", + "log(Z): -1.35085e+01, tol = 1.07995e+01, K = 1, iteration = 472, H = 2.35535e+00\n", + "log(Z): -1.34868e+01, tol = 1.07758e+01, K = 1, iteration = 473, H = 2.35482e+00\n", + "log(Z): -1.34655e+01, tol = 1.07525e+01, K = 1, iteration = 474, H = 2.35391e+00\n", + "log(Z): -1.34445e+01, tol = 1.07295e+01, K = 1, iteration = 475, H = 2.35276e+00\n", + "log(Z): -1.34240e+01, tol = 1.07070e+01, K = 1, iteration = 476, H = 2.35131e+00\n", + "log(Z): -1.34038e+01, tol = 1.06848e+01, K = 1, iteration = 477, H = 2.34953e+00\n", + "log(Z): -1.33831e+01, tol = 1.06621e+01, K = 1, iteration = 478, H = 2.34838e+00\n", + "log(Z): -1.33625e+01, tol = 1.06395e+01, K = 1, iteration = 479, H = 2.34714e+00\n", + "log(Z): -1.33420e+01, tol = 1.06170e+01, K = 1, iteration = 480, H = 2.34592e+00\n", + "log(Z): -1.33216e+01, tol = 1.05946e+01, K = 1, iteration = 481, H = 2.34470e+00\n", + "log(Z): -1.33015e+01, tol = 1.05725e+01, K = 1, iteration = 482, H = 2.34320e+00\n", + "log(Z): -1.32810e+01, tol = 1.05499e+01, K = 1, iteration = 483, H = 2.34222e+00\n", + "log(Z): -1.32607e+01, tol = 1.05276e+01, K = 1, iteration = 484, H = 2.34106e+00\n", + "log(Z): -1.32407e+01, tol = 1.05056e+01, K = 1, iteration = 485, H = 2.33967e+00\n", + "log(Z): -1.32207e+01, tol = 1.04837e+01, K = 1, iteration = 486, H = 2.33836e+00\n", + "log(Z): -1.32007e+01, tol = 1.04616e+01, K = 1, iteration = 487, H = 2.33713e+00\n", + "log(Z): -1.31808e+01, tol = 1.04398e+01, K = 1, iteration = 488, H = 2.33583e+00\n", + "log(Z): -1.31611e+01, tol = 1.04181e+01, K = 1, iteration = 489, H = 2.33441e+00\n", + "log(Z): -1.31414e+01, tol = 1.03964e+01, K = 1, iteration = 490, H = 2.33312e+00\n", + "log(Z): -1.31219e+01, tol = 1.03749e+01, K = 1, iteration = 491, H = 2.33166e+00\n", + "log(Z): -1.31022e+01, tol = 1.03532e+01, K = 1, iteration = 492, H = 2.33042e+00\n", + "log(Z): -1.30829e+01, tol = 1.03319e+01, K = 1, iteration = 493, H = 2.32896e+00\n", + "log(Z): -1.30636e+01, tol = 1.03106e+01, K = 1, iteration = 494, H = 2.32752e+00\n", + "log(Z): -1.30445e+01, tol = 1.02895e+01, K = 1, iteration = 495, H = 2.32599e+00\n", + "log(Z): -1.30253e+01, tol = 1.02683e+01, K = 1, iteration = 496, H = 2.32456e+00\n", + "log(Z): -1.30055e+01, tol = 1.02465e+01, K = 1, iteration = 497, H = 2.32379e+00\n", + "log(Z): -1.29860e+01, tol = 1.02250e+01, K = 1, iteration = 498, H = 2.32283e+00\n", + "log(Z): -1.29669e+01, tol = 1.02039e+01, K = 1, iteration = 499, H = 2.32155e+00\n", + "log(Z): -1.29478e+01, tol = 1.01828e+01, K = 1, iteration = 500, H = 2.32025e+00\n", + "log(Z): -1.29286e+01, tol = 1.01616e+01, K = 1, iteration = 501, H = 2.31918e+00\n", + "log(Z): -1.29097e+01, tol = 1.01407e+01, K = 1, iteration = 502, H = 2.31783e+00\n", + "log(Z): -1.28903e+01, tol = 1.01193e+01, K = 1, iteration = 503, H = 2.31711e+00\n", + "log(Z): -1.28712e+01, tol = 1.00982e+01, K = 1, iteration = 504, H = 2.31609e+00\n", + "log(Z): -1.28524e+01, tol = 1.00774e+01, K = 1, iteration = 505, H = 2.31483e+00\n", + "log(Z): -1.28338e+01, tol = 1.00568e+01, K = 1, iteration = 506, H = 2.31349e+00\n", + "log(Z): -1.28142e+01, tol = 1.00352e+01, K = 1, iteration = 507, H = 2.31307e+00\n", + "log(Z): -1.27948e+01, tol = 1.00138e+01, K = 1, iteration = 508, H = 2.31261e+00\n", + "log(Z): -1.27757e+01, tol = 9.99270e+00, K = 1, iteration = 509, H = 2.31187e+00\n", + "log(Z): -1.27568e+01, tol = 9.97184e+00, K = 1, iteration = 510, H = 2.31096e+00\n", + "log(Z): -1.27382e+01, tol = 9.95116e+00, K = 1, iteration = 511, H = 2.30993e+00\n", + "log(Z): -1.27196e+01, tol = 9.93059e+00, K = 1, iteration = 512, H = 2.30885e+00\n", + "log(Z): -1.27011e+01, tol = 9.91010e+00, K = 1, iteration = 513, H = 2.30776e+00\n", + "log(Z): -1.26825e+01, tol = 9.88948e+00, K = 1, iteration = 514, H = 2.30684e+00\n", + "log(Z): -1.26642e+01, tol = 9.86918e+00, K = 1, iteration = 515, H = 2.30568e+00\n", + "log(Z): -1.26455e+01, tol = 9.84852e+00, K = 1, iteration = 516, H = 2.30491e+00\n", + "log(Z): -1.26268e+01, tol = 9.82782e+00, K = 1, iteration = 517, H = 2.30423e+00\n", + "log(Z): -1.26083e+01, tol = 9.80726e+00, K = 1, iteration = 518, H = 2.30347e+00\n", + "log(Z): -1.25896e+01, tol = 9.78663e+00, K = 1, iteration = 519, H = 2.30284e+00\n", + "log(Z): -1.25711e+01, tol = 9.76616e+00, K = 1, iteration = 520, H = 2.30209e+00\n", + "log(Z): -1.25525e+01, tol = 9.74556e+00, K = 1, iteration = 521, H = 2.30151e+00\n", + "log(Z): -1.25343e+01, tol = 9.72530e+00, K = 1, iteration = 522, H = 2.30067e+00\n", + "log(Z): -1.25161e+01, tol = 9.70516e+00, K = 1, iteration = 523, H = 2.29976e+00\n", + "log(Z): -1.24982e+01, tol = 9.68521e+00, K = 1, iteration = 524, H = 2.29872e+00\n", + "log(Z): -1.24796e+01, tol = 9.66467e+00, K = 1, iteration = 525, H = 2.29830e+00\n", + "log(Z): -1.24614e+01, tol = 9.64438e+00, K = 1, iteration = 526, H = 2.29769e+00\n", + "log(Z): -1.24434e+01, tol = 9.62439e+00, K = 1, iteration = 527, H = 2.29682e+00\n", + "log(Z): -1.24256e+01, tol = 9.60464e+00, K = 1, iteration = 528, H = 2.29580e+00\n", + "log(Z): -1.24078e+01, tol = 9.58482e+00, K = 1, iteration = 529, H = 2.29489e+00\n", + "log(Z): -1.23903e+01, tol = 9.56531e+00, K = 1, iteration = 530, H = 2.29374e+00\n", + "log(Z): -1.23728e+01, tol = 9.54579e+00, K = 1, iteration = 531, H = 2.29265e+00\n", + "log(Z): -1.23555e+01, tol = 9.52658e+00, K = 1, iteration = 532, H = 2.29134e+00\n", + "log(Z): -1.23386e+01, tol = 9.50764e+00, K = 1, iteration = 533, H = 2.28983e+00\n", + "log(Z): -1.23218e+01, tol = 9.48885e+00, K = 1, iteration = 534, H = 2.28824e+00\n", + "log(Z): -1.23051e+01, tol = 9.47009e+00, K = 1, iteration = 535, H = 2.28669e+00\n", + "log(Z): -1.22885e+01, tol = 9.45151e+00, K = 1, iteration = 536, H = 2.28504e+00\n", + "log(Z): -1.22713e+01, tol = 9.43236e+00, K = 1, iteration = 537, H = 2.28397e+00\n", + "log(Z): -1.22539e+01, tol = 9.41293e+00, K = 1, iteration = 538, H = 2.28321e+00\n", + "log(Z): -1.22365e+01, tol = 9.39358e+00, K = 1, iteration = 539, H = 2.28242e+00\n", + "log(Z): -1.22194e+01, tol = 9.37441e+00, K = 1, iteration = 540, H = 2.28152e+00\n", + "log(Z): -1.22023e+01, tol = 9.35538e+00, K = 1, iteration = 541, H = 2.28053e+00\n", + "log(Z): -1.21853e+01, tol = 9.33639e+00, K = 1, iteration = 542, H = 2.27955e+00\n", + "log(Z): -1.21676e+01, tol = 9.31664e+00, K = 1, iteration = 543, H = 2.27935e+00\n", + "log(Z): -1.21501e+01, tol = 9.29710e+00, K = 1, iteration = 544, H = 2.27899e+00\n", + "log(Z): -1.21328e+01, tol = 9.27781e+00, K = 1, iteration = 545, H = 2.27842e+00\n", + "log(Z): -1.21158e+01, tol = 9.25883e+00, K = 1, iteration = 546, H = 2.27761e+00\n", + "log(Z): -1.20990e+01, tol = 9.24002e+00, K = 1, iteration = 547, H = 2.27668e+00\n", + "log(Z): -1.20824e+01, tol = 9.22150e+00, K = 1, iteration = 548, H = 2.27553e+00\n", + "log(Z): -1.20655e+01, tol = 9.20251e+00, K = 1, iteration = 549, H = 2.27487e+00\n", + "log(Z): -1.20486e+01, tol = 9.18366e+00, K = 1, iteration = 550, H = 2.27412e+00\n", + "log(Z): -1.20320e+01, tol = 9.16511e+00, K = 1, iteration = 551, H = 2.27314e+00\n", + "log(Z): -1.20156e+01, tol = 9.14671e+00, K = 1, iteration = 552, H = 2.27207e+00\n", + "log(Z): -1.19995e+01, tol = 9.12861e+00, K = 1, iteration = 553, H = 2.27078e+00\n", + "log(Z): -1.19835e+01, tol = 9.11052e+00, K = 1, iteration = 554, H = 2.26952e+00\n", + "log(Z): -1.19674e+01, tol = 9.09247e+00, K = 1, iteration = 555, H = 2.26828e+00\n", + "log(Z): -1.19516e+01, tol = 9.07466e+00, K = 1, iteration = 556, H = 2.26687e+00\n", + "log(Z): -1.19357e+01, tol = 9.05675e+00, K = 1, iteration = 557, H = 2.26561e+00\n", + "log(Z): -1.19199e+01, tol = 9.03901e+00, K = 1, iteration = 558, H = 2.26425e+00\n", + "log(Z): -1.19043e+01, tol = 9.02141e+00, K = 1, iteration = 559, H = 2.26281e+00\n", + "log(Z): -1.18889e+01, tol = 9.00402e+00, K = 1, iteration = 560, H = 2.26123e+00\n", + "log(Z): -1.18736e+01, tol = 8.98673e+00, K = 1, iteration = 561, H = 2.25962e+00\n", + "log(Z): -1.18586e+01, tol = 8.96966e+00, K = 1, iteration = 562, H = 2.25787e+00\n", + "log(Z): -1.18436e+01, tol = 8.95273e+00, K = 1, iteration = 563, H = 2.25606e+00\n", + "log(Z): -1.18287e+01, tol = 8.93575e+00, K = 1, iteration = 564, H = 2.25435e+00\n", + "log(Z): -1.18135e+01, tol = 8.91856e+00, K = 1, iteration = 565, H = 2.25287e+00\n", + "log(Z): -1.17982e+01, tol = 8.90129e+00, K = 1, iteration = 566, H = 2.25153e+00\n", + "log(Z): 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K = 1, iteration = 578, H = 2.23407e+00\n", + "log(Z): -1.16058e+01, tol = 8.68290e+00, K = 1, iteration = 579, H = 2.23332e+00\n", + "log(Z): -1.15908e+01, tol = 8.66594e+00, K = 1, iteration = 580, H = 2.23240e+00\n", + "log(Z): -1.15759e+01, tol = 8.64904e+00, K = 1, iteration = 581, H = 2.23147e+00\n", + "log(Z): -1.15611e+01, tol = 8.63228e+00, K = 1, iteration = 582, H = 2.23045e+00\n", + "log(Z): -1.15465e+01, tol = 8.61566e+00, K = 1, iteration = 583, H = 2.22934e+00\n", + "log(Z): -1.15316e+01, tol = 8.59877e+00, K = 1, iteration = 584, H = 2.22854e+00\n", + "log(Z): -1.15168e+01, tol = 8.58196e+00, K = 1, iteration = 585, H = 2.22770e+00\n", + "log(Z): -1.15018e+01, tol = 8.56492e+00, K = 1, iteration = 586, H = 2.22712e+00\n", + "log(Z): -1.14869e+01, tol = 8.54803e+00, K = 1, iteration = 587, H = 2.22643e+00\n", + "log(Z): -1.14721e+01, tol = 8.53123e+00, K = 1, iteration = 588, H = 2.22570e+00\n", + "log(Z): -1.14574e+01, tol = 8.51457e+00, K = 1, iteration = 589, H = 2.22488e+00\n", + "log(Z): -1.14428e+01, tol = 8.49796e+00, K = 1, iteration = 590, H = 2.22405e+00\n", + "log(Z): -1.14281e+01, tol = 8.48124e+00, K = 1, iteration = 591, H = 2.22337e+00\n", + "log(Z): -1.14135e+01, tol = 8.46463e+00, K = 1, iteration = 592, H = 2.22262e+00\n", + "log(Z): -1.13991e+01, tol = 8.44822e+00, K = 1, iteration = 593, H = 2.22172e+00\n", + "log(Z): -1.13847e+01, tol = 8.43191e+00, K = 1, iteration = 594, H = 2.22078e+00\n", + "log(Z): -1.13706e+01, tol = 8.41579e+00, K = 1, iteration = 595, H = 2.21970e+00\n", + "log(Z): -1.13562e+01, tol = 8.39934e+00, K = 1, iteration = 596, H = 2.21896e+00\n", + "log(Z): -1.13419e+01, tol = 8.38305e+00, K = 1, iteration = 597, H = 2.21812e+00\n", + "log(Z): -1.13276e+01, tol = 8.36683e+00, K = 1, iteration = 598, H = 2.21725e+00\n", + "log(Z): -1.13136e+01, tol = 8.35082e+00, K = 1, iteration = 599, H = 2.21624e+00\n", + "log(Z): -1.12997e+01, tol = 8.33489e+00, K = 1, iteration = 600, H = 2.21518e+00\n", + "log(Z): 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K = 1, iteration = 612, H = 2.19894e+00\n", + "log(Z): -1.11272e+01, tol = 8.13645e+00, K = 1, iteration = 613, H = 2.19769e+00\n", + "log(Z): -1.11142e+01, tol = 8.12143e+00, K = 1, iteration = 614, H = 2.19644e+00\n", + "log(Z): -1.11013e+01, tol = 8.10658e+00, K = 1, iteration = 615, H = 2.19507e+00\n", + "log(Z): -1.10885e+01, tol = 8.09174e+00, K = 1, iteration = 616, H = 2.19374e+00\n", + "log(Z): -1.10758e+01, tol = 8.07707e+00, K = 1, iteration = 617, H = 2.19230e+00\n", + "log(Z): -1.10631e+01, tol = 8.06240e+00, K = 1, iteration = 618, H = 2.19090e+00\n", + "log(Z): -1.10502e+01, tol = 8.04751e+00, K = 1, iteration = 619, H = 2.18975e+00\n", + "log(Z): -1.10375e+01, tol = 8.03275e+00, K = 1, iteration = 620, H = 2.18852e+00\n", + "log(Z): -1.10249e+01, tol = 8.01817e+00, K = 1, iteration = 621, H = 2.18717e+00\n", + "log(Z): -1.10124e+01, tol = 8.00369e+00, K = 1, iteration = 622, H = 2.18576e+00\n", + "log(Z): -1.10000e+01, tol = 7.98932e+00, K = 1, iteration = 623, H = 2.18431e+00\n", + "log(Z): -1.09876e+01, tol = 7.97491e+00, K = 1, iteration = 624, H = 2.18294e+00\n", + "log(Z): -1.09753e+01, tol = 7.96063e+00, K = 1, iteration = 625, H = 2.18148e+00\n", + "log(Z): -1.09631e+01, tol = 7.94642e+00, K = 1, iteration = 626, H = 2.18002e+00\n", + "log(Z): -1.09509e+01, tol = 7.93223e+00, K = 1, iteration = 627, H = 2.17857e+00\n", + "log(Z): -1.09389e+01, tol = 7.91818e+00, K = 1, iteration = 628, H = 2.17704e+00\n", + "log(Z): -1.09269e+01, tol = 7.90420e+00, K = 1, iteration = 629, H = 2.17550e+00\n", + "log(Z): -1.09150e+01, tol = 7.89032e+00, K = 1, iteration = 630, H = 2.17391e+00\n", + "log(Z): -1.09031e+01, tol = 7.87644e+00, K = 1, iteration = 631, H = 2.17237e+00\n", + "log(Z): -1.08913e+01, tol = 7.86260e+00, K = 1, iteration = 632, H = 2.17084e+00\n", + "log(Z): -1.08795e+01, tol = 7.84889e+00, K = 1, iteration = 633, H = 2.16923e+00\n", + "log(Z): -1.08679e+01, tol = 7.83527e+00, K = 1, iteration = 634, H = 2.16758e+00\n", + "log(Z): 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K = 1, iteration = 646, H = 2.15078e+00\n", + "log(Z): -1.07172e+01, tol = 7.65863e+00, K = 1, iteration = 647, H = 2.14954e+00\n", + "log(Z): -1.07057e+01, tol = 7.64512e+00, K = 1, iteration = 648, H = 2.14835e+00\n", + "log(Z): -1.06943e+01, tol = 7.63172e+00, K = 1, iteration = 649, H = 2.14710e+00\n", + "log(Z): -1.06830e+01, tol = 7.61847e+00, K = 1, iteration = 650, H = 2.14576e+00\n", + "log(Z): -1.06719e+01, tol = 7.60531e+00, K = 1, iteration = 651, H = 2.14437e+00\n", + "log(Z): -1.06607e+01, tol = 7.59220e+00, K = 1, iteration = 652, H = 2.14298e+00\n", + "log(Z): -1.06497e+01, tol = 7.57921e+00, K = 1, iteration = 653, H = 2.14151e+00\n", + "log(Z): -1.06388e+01, tol = 7.56631e+00, K = 1, iteration = 654, H = 2.14001e+00\n", + "log(Z): -1.06280e+01, tol = 7.55352e+00, K = 1, iteration = 655, H = 2.13846e+00\n", + "log(Z): -1.06174e+01, tol = 7.54085e+00, K = 1, iteration = 656, H = 2.13683e+00\n", + "log(Z): -1.06068e+01, tol = 7.52831e+00, K = 1, iteration = 657, H = 2.13514e+00\n", + "log(Z): -1.05964e+01, tol = 7.51586e+00, K = 1, iteration = 658, H = 2.13342e+00\n", + "log(Z): -1.05857e+01, tol = 7.50322e+00, K = 1, iteration = 659, H = 2.13189e+00\n", + "log(Z): -1.05746e+01, tol = 7.49016e+00, K = 1, iteration = 660, H = 2.13076e+00\n", + "log(Z): -1.05637e+01, tol = 7.47721e+00, K = 1, iteration = 661, H = 2.12957e+00\n", + "log(Z): -1.05523e+01, tol = 7.46379e+00, K = 1, iteration = 662, H = 2.12886e+00\n", + "log(Z): -1.05409e+01, tol = 7.45046e+00, K = 1, iteration = 663, H = 2.12808e+00\n", + "log(Z): -1.05297e+01, tol = 7.43725e+00, K = 1, iteration = 664, H = 2.12722e+00\n", + "log(Z): -1.05184e+01, tol = 7.42399e+00, K = 1, iteration = 665, H = 2.12644e+00\n", + "log(Z): -1.05073e+01, tol = 7.41087e+00, K = 1, iteration = 666, H = 2.12556e+00\n", + "log(Z): -1.04963e+01, tol = 7.39790e+00, K = 1, iteration = 667, H = 2.12459e+00\n", + "log(Z): -1.04854e+01, tol = 7.38502e+00, K = 1, iteration = 668, H = 2.12356e+00\n", + "log(Z): 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K = 1, iteration = 680, H = 2.11450e+00\n", + "log(Z): -1.03431e+01, tol = 7.21676e+00, K = 1, iteration = 681, H = 2.11378e+00\n", + "log(Z): -1.03324e+01, tol = 7.20409e+00, K = 1, iteration = 682, H = 2.11297e+00\n", + "log(Z): -1.03218e+01, tol = 7.19148e+00, K = 1, iteration = 683, H = 2.11214e+00\n", + "log(Z): -1.03113e+01, tol = 7.17898e+00, K = 1, iteration = 684, H = 2.11125e+00\n", + "log(Z): -1.03008e+01, tol = 7.16653e+00, K = 1, iteration = 685, H = 2.11033e+00\n", + "log(Z): -1.02904e+01, tol = 7.15415e+00, K = 1, iteration = 686, H = 2.10939e+00\n", + "log(Z): -1.02801e+01, tol = 7.14185e+00, K = 1, iteration = 687, H = 2.10840e+00\n", + "log(Z): -1.02698e+01, tol = 7.12960e+00, K = 1, iteration = 688, H = 2.10739e+00\n", + "log(Z): -1.02596e+01, tol = 7.11733e+00, K = 1, iteration = 689, H = 2.10644e+00\n", + "log(Z): -1.02494e+01, tol = 7.10517e+00, K = 1, iteration = 690, H = 2.10542e+00\n", + "log(Z): -1.02391e+01, tol = 7.09285e+00, K = 1, iteration = 691, H = 2.10456e+00\n", + "log(Z): -1.02288e+01, tol = 7.08060e+00, K = 1, iteration = 692, H = 2.10368e+00\n", + "log(Z): -1.02185e+01, tol = 7.06827e+00, K = 1, iteration = 693, H = 2.10291e+00\n", + "log(Z): -1.02082e+01, tol = 7.05605e+00, K = 1, iteration = 694, H = 2.10205e+00\n", + "log(Z): -1.01981e+01, tol = 7.04389e+00, K = 1, iteration = 695, H = 2.10117e+00\n", + "log(Z): -1.01879e+01, tol = 7.03172e+00, K = 1, iteration = 696, H = 2.10034e+00\n", + "log(Z): -1.01778e+01, tol = 7.01964e+00, K = 1, iteration = 697, H = 2.09944e+00\n", + "log(Z): -1.01676e+01, tol = 7.00751e+00, K = 1, iteration = 698, H = 2.09863e+00\n", + "log(Z): -1.01576e+01, tol = 6.99544e+00, K = 1, iteration = 699, H = 2.09778e+00\n", + "log(Z): -1.01476e+01, tol = 6.98344e+00, K = 1, iteration = 700, H = 2.09690e+00\n", + "log(Z): -1.01376e+01, tol = 6.97147e+00, K = 1, iteration = 701, H = 2.09603e+00\n", + "log(Z): -1.01275e+01, tol = 6.95943e+00, K = 1, iteration = 702, H = 2.09525e+00\n", + "log(Z): 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K = 1, iteration = 714, H = 2.08548e+00\n", + "log(Z): -1.00000e+01, tol = 6.80609e+00, K = 1, iteration = 715, H = 2.08464e+00\n", + "log(Z): -9.99036e+00, tol = 6.79443e+00, K = 1, iteration = 716, H = 2.08390e+00\n", + "log(Z): -9.98060e+00, tol = 6.78269e+00, K = 1, iteration = 717, H = 2.08325e+00\n", + "log(Z): -9.97095e+00, tol = 6.77106e+00, K = 1, iteration = 718, H = 2.08254e+00\n", + "log(Z): -9.96142e+00, tol = 6.75953e+00, K = 1, iteration = 719, H = 2.08174e+00\n", + "log(Z): -9.95193e+00, tol = 6.74806e+00, K = 1, iteration = 720, H = 2.08093e+00\n", + "log(Z): -9.94243e+00, tol = 6.73658e+00, K = 1, iteration = 721, H = 2.08016e+00\n", + "log(Z): -9.93294e+00, tol = 6.72510e+00, K = 1, iteration = 722, H = 2.07940e+00\n", + "log(Z): -9.92354e+00, tol = 6.71372e+00, K = 1, iteration = 723, H = 2.07859e+00\n", + "log(Z): -9.91419e+00, tol = 6.70238e+00, K = 1, iteration = 724, H = 2.07775e+00\n", + "log(Z): -9.90493e+00, tol = 6.69113e+00, K = 1, iteration = 725, H = 2.07687e+00\n", + "log(Z): -9.89555e+00, tol = 6.67976e+00, K = 1, iteration = 726, H = 2.07612e+00\n", + "log(Z): -9.88601e+00, tol = 6.66824e+00, K = 1, iteration = 727, H = 2.07554e+00\n", + "log(Z): -9.87628e+00, tol = 6.65652e+00, K = 1, iteration = 728, H = 2.07517e+00\n", + "log(Z): -9.86653e+00, tol = 6.64479e+00, K = 1, iteration = 729, H = 2.07484e+00\n", + "log(Z): -9.85650e+00, tol = 6.63278e+00, K = 1, iteration = 730, H = 2.07480e+00\n", + "log(Z): -9.84656e+00, tol = 6.62085e+00, K = 1, iteration = 731, H = 2.07470e+00\n", + "log(Z): -9.83656e+00, tol = 6.60887e+00, K = 1, iteration = 732, H = 2.07468e+00\n", + "log(Z): -9.82668e+00, tol = 6.59701e+00, K = 1, iteration = 733, H = 2.07456e+00\n", + "log(Z): -9.81691e+00, tol = 6.58525e+00, K = 1, iteration = 734, H = 2.07435e+00\n", + "log(Z): -9.80723e+00, tol = 6.57359e+00, K = 1, iteration = 735, H = 2.07408e+00\n", + "log(Z): -9.79745e+00, tol = 6.56182e+00, K = 1, iteration = 736, H = 2.07392e+00\n", + "log(Z): 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K = 1, iteration = 748, H = 2.07394e+00\n", + "log(Z): -9.66989e+00, tol = 6.40849e+00, K = 1, iteration = 749, H = 2.07415e+00\n", + "log(Z): -9.66000e+00, tol = 6.39663e+00, K = 1, iteration = 750, H = 2.07438e+00\n", + "log(Z): -9.65003e+00, tol = 6.38468e+00, K = 1, iteration = 751, H = 2.07470e+00\n", + "log(Z): -9.64018e+00, tol = 6.37285e+00, K = 1, iteration = 752, H = 2.07492e+00\n", + "log(Z): -9.63027e+00, tol = 6.36096e+00, K = 1, iteration = 753, H = 2.07522e+00\n", + "log(Z): -9.62040e+00, tol = 6.34911e+00, K = 1, iteration = 754, H = 2.07550e+00\n", + "log(Z): -9.61048e+00, tol = 6.33720e+00, K = 1, iteration = 755, H = 2.07585e+00\n", + "log(Z): -9.60066e+00, tol = 6.32541e+00, K = 1, iteration = 756, H = 2.07610e+00\n", + "log(Z): -9.59097e+00, tol = 6.31374e+00, K = 1, iteration = 757, H = 2.07625e+00\n", + "log(Z): -9.58137e+00, tol = 6.31787e+00, K = 1, iteration = 758, H = 2.07632e+00\n", + "log(Z): -9.57184e+00, tol = 6.30636e+00, K = 1, iteration = 759, H = 2.07633e+00\n", + "log(Z): -9.56235e+00, tol = 6.29489e+00, K = 1, iteration = 760, H = 2.07633e+00\n", + "log(Z): -9.55290e+00, tol = 6.28346e+00, K = 1, iteration = 761, H = 2.07631e+00\n", + "log(Z): -9.54352e+00, tol = 6.27211e+00, K = 1, iteration = 762, H = 2.07623e+00\n", + "log(Z): -9.53422e+00, tol = 6.26082e+00, K = 1, iteration = 763, H = 2.07611e+00\n", + "log(Z): -9.52496e+00, tol = 6.24958e+00, K = 1, iteration = 764, H = 2.07596e+00\n", + "log(Z): -9.51576e+00, tol = 6.23841e+00, K = 1, iteration = 765, H = 2.07576e+00\n", + "log(Z): -9.50662e+00, tol = 6.22729e+00, K = 1, iteration = 766, H = 2.07553e+00\n", + "log(Z): -9.49746e+00, tol = 6.21615e+00, K = 1, iteration = 767, H = 2.07534e+00\n", + "log(Z): -9.48839e+00, tol = 6.20511e+00, K = 1, iteration = 768, H = 2.07508e+00\n", + "log(Z): -9.47937e+00, tol = 6.19411e+00, K = 1, iteration = 769, H = 2.07480e+00\n", + "log(Z): -9.47031e+00, tol = 6.18307e+00, K = 1, iteration = 770, H = 2.07457e+00\n", + "log(Z): 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K = 1, iteration = 782, H = 2.07158e+00\n", + "log(Z): -9.35462e+00, tol = 6.04170e+00, K = 1, iteration = 783, H = 2.07142e+00\n", + "log(Z): -9.34585e+00, tol = 6.03095e+00, K = 1, iteration = 784, H = 2.07119e+00\n", + "log(Z): -9.33697e+00, tol = 6.02010e+00, K = 1, iteration = 785, H = 2.07109e+00\n", + "log(Z): -9.32817e+00, tol = 6.00932e+00, K = 1, iteration = 786, H = 2.07093e+00\n", + "log(Z): -9.31928e+00, tol = 5.99846e+00, K = 1, iteration = 787, H = 2.07088e+00\n", + "log(Z): -9.31036e+00, tol = 5.98757e+00, K = 1, iteration = 788, H = 2.07087e+00\n", + "log(Z): -9.30146e+00, tol = 5.97669e+00, K = 1, iteration = 789, H = 2.07086e+00\n", + "log(Z): -9.29264e+00, tol = 5.96590e+00, K = 1, iteration = 790, H = 2.07079e+00\n", + "log(Z): -9.28390e+00, tol = 5.95519e+00, K = 1, iteration = 791, H = 2.07066e+00\n", + "log(Z): -9.27517e+00, tol = 5.94449e+00, K = 1, iteration = 792, H = 2.07054e+00\n", + "log(Z): -9.26650e+00, tol = 5.93385e+00, K = 1, iteration = 793, H = 2.07037e+00\n", + "log(Z): -9.25787e+00, tol = 5.92325e+00, K = 1, iteration = 794, H = 2.07019e+00\n", + "log(Z): -9.24934e+00, tol = 5.91274e+00, K = 1, iteration = 795, H = 2.06993e+00\n", + "log(Z): -9.24087e+00, tol = 5.90230e+00, K = 1, iteration = 796, H = 2.06963e+00\n", + "log(Z): -9.23243e+00, tol = 5.89188e+00, K = 1, iteration = 797, H = 2.06932e+00\n", + "log(Z): -9.22405e+00, tol = 5.88154e+00, K = 1, iteration = 798, H = 2.06897e+00\n", + "log(Z): -9.21576e+00, tol = 5.87127e+00, K = 1, iteration = 799, H = 2.06856e+00\n", + "log(Z): -9.20754e+00, tol = 5.86108e+00, K = 1, iteration = 800, H = 2.06810e+00\n", + "log(Z): -9.19938e+00, tol = 5.85096e+00, K = 1, iteration = 801, H = 2.06760e+00\n", + "log(Z): -9.19130e+00, tol = 5.84090e+00, K = 1, iteration = 802, H = 2.06706e+00\n", + "log(Z): -9.18321e+00, tol = 5.83085e+00, K = 1, iteration = 803, H = 2.06653e+00\n", + "log(Z): -9.17520e+00, tol = 5.82086e+00, K = 1, iteration = 804, H = 2.06596e+00\n", + "log(Z): 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K = 1, iteration = 816, H = 2.05944e+00\n", + "log(Z): -9.07257e+00, tol = 5.69264e+00, K = 1, iteration = 817, H = 2.05896e+00\n", + "log(Z): -9.06478e+00, tol = 5.68289e+00, K = 1, iteration = 818, H = 2.05846e+00\n", + "log(Z): -9.05703e+00, tol = 5.67317e+00, K = 1, iteration = 819, H = 2.05794e+00\n", + "log(Z): -9.04929e+00, tol = 5.66346e+00, K = 1, iteration = 820, H = 2.05744e+00\n", + "log(Z): -9.04158e+00, tol = 5.65378e+00, K = 1, iteration = 821, H = 2.05692e+00\n", + "log(Z): -9.03395e+00, tol = 5.64418e+00, K = 1, iteration = 822, H = 2.05636e+00\n", + "log(Z): -9.02629e+00, tol = 5.63456e+00, K = 1, iteration = 823, H = 2.05583e+00\n", + "log(Z): -9.01870e+00, tol = 5.62500e+00, K = 1, iteration = 824, H = 2.05527e+00\n", + "log(Z): -9.01108e+00, tol = 5.61542e+00, K = 1, iteration = 825, H = 2.05474e+00\n", + "log(Z): -9.00351e+00, tol = 5.60589e+00, K = 1, iteration = 826, H = 2.05419e+00\n", + "log(Z): -8.99595e+00, tol = 5.59636e+00, K = 1, iteration = 827, H = 2.05365e+00\n", + "log(Z): -8.98836e+00, tol = 5.58681e+00, K = 1, iteration = 828, H = 2.05316e+00\n", + "log(Z): -8.98082e+00, tol = 5.57731e+00, K = 1, iteration = 829, H = 2.05264e+00\n", + "log(Z): -8.97334e+00, tol = 5.56786e+00, K = 1, iteration = 830, H = 2.05209e+00\n", + "log(Z): -8.96591e+00, tol = 5.55847e+00, K = 1, iteration = 831, H = 2.05151e+00\n", + "log(Z): -8.95846e+00, tol = 5.54905e+00, K = 1, iteration = 832, H = 2.05096e+00\n", + "log(Z): -8.95107e+00, tol = 5.53970e+00, K = 1, iteration = 833, H = 2.05038e+00\n", + "log(Z): -8.94372e+00, tol = 5.53038e+00, K = 1, iteration = 834, H = 2.04978e+00\n", + "log(Z): -8.93643e+00, tol = 5.52113e+00, K = 1, iteration = 835, H = 2.04915e+00\n", + "log(Z): -8.92909e+00, tol = 5.51183e+00, K = 1, iteration = 836, H = 2.04857e+00\n", + "log(Z): -8.92173e+00, tol = 5.50251e+00, K = 1, iteration = 837, H = 2.04804e+00\n", + "log(Z): -8.91437e+00, tol = 5.49319e+00, K = 1, iteration = 838, H = 2.04753e+00\n", + "log(Z): -8.90697e+00, tol = 5.48382e+00, K = 1, iteration = 839, H = 2.04707e+00\n", + "log(Z): -8.89962e+00, tol = 5.47451e+00, K = 1, iteration = 840, H = 2.04658e+00\n", + "log(Z): -8.89228e+00, tol = 5.46521e+00, K = 1, iteration = 841, H = 2.04610e+00\n", + "log(Z): -8.88490e+00, tol = 5.45587e+00, K = 1, iteration = 842, H = 2.04568e+00\n", + "log(Z): -8.87751e+00, tol = 5.44653e+00, K = 1, iteration = 843, H = 2.04528e+00\n", + "log(Z): -8.87005e+00, tol = 5.43710e+00, K = 1, iteration = 844, H = 2.04498e+00\n", + "log(Z): -8.86265e+00, tol = 5.42774e+00, K = 1, iteration = 845, H = 2.04463e+00\n", + "log(Z): -8.85521e+00, tol = 5.41835e+00, K = 1, iteration = 846, H = 2.04433e+00\n", + "log(Z): -8.84776e+00, tol = 5.40893e+00, K = 1, iteration = 847, H = 2.04406e+00\n", + "log(Z): -8.84027e+00, tol = 5.39949e+00, K = 1, iteration = 848, H = 2.04384e+00\n", + "log(Z): -8.83284e+00, tol = 5.39010e+00, K = 1, iteration = 849, H = 2.04359e+00\n", + "log(Z): -8.82524e+00, tol = 5.38055e+00, K = 1, iteration = 850, H = 2.04352e+00\n", + "log(Z): -8.81767e+00, tol = 5.37102e+00, K = 1, iteration = 851, H = 2.04343e+00\n", + "log(Z): -8.81017e+00, tol = 5.36156e+00, K = 1, iteration = 852, H = 2.04330e+00\n", + "log(Z): -8.80265e+00, tol = 5.35209e+00, K = 1, iteration = 853, H = 2.04319e+00\n", + "log(Z): -8.79517e+00, tol = 5.34265e+00, K = 1, iteration = 854, H = 2.04306e+00\n", + "log(Z): -8.78772e+00, tol = 5.33325e+00, K = 1, iteration = 855, H = 2.04292e+00\n", + "log(Z): -8.78020e+00, tol = 5.32378e+00, K = 1, iteration = 856, H = 2.04286e+00\n", + "log(Z): -8.77270e+00, tol = 5.31432e+00, K = 1, iteration = 857, H = 2.04280e+00\n", + "log(Z): -8.76513e+00, tol = 5.30480e+00, K = 1, iteration = 858, H = 2.04283e+00\n", + "log(Z): -8.75759e+00, tol = 5.29532e+00, K = 1, iteration = 859, H = 2.04283e+00\n", + "log(Z): -8.75010e+00, tol = 5.28587e+00, K = 1, iteration = 860, H = 2.04281e+00\n", + "log(Z): -8.74258e+00, tol = 5.27640e+00, K = 1, iteration = 861, H = 2.04283e+00\n", + "log(Z): -8.73506e+00, tol = 5.26693e+00, K = 1, iteration = 862, H = 2.04286e+00\n", + "log(Z): -8.72761e+00, tol = 5.25753e+00, K = 1, iteration = 863, H = 2.04284e+00\n", + "log(Z): -8.72021e+00, tol = 5.24817e+00, K = 1, iteration = 864, H = 2.04279e+00\n", + "log(Z): -8.71287e+00, tol = 5.23888e+00, K = 1, iteration = 865, H = 2.04269e+00\n", + "log(Z): -8.70559e+00, tol = 5.22965e+00, K = 1, iteration = 866, H = 2.04254e+00\n", + "log(Z): -8.69831e+00, tol = 5.22042e+00, K = 1, iteration = 867, H = 2.04242e+00\n", + "log(Z): -8.69106e+00, tol = 5.21123e+00, K = 1, iteration = 868, H = 2.04227e+00\n", + "log(Z): -8.68388e+00, tol = 5.20209e+00, K = 1, iteration = 869, H = 2.04208e+00\n", + "log(Z): -8.67655e+00, tol = 5.19282e+00, K = 1, iteration = 870, H = 2.04205e+00\n", + "log(Z): -8.66925e+00, tol = 5.18357e+00, K = 1, iteration = 871, H = 2.04201e+00\n", + "log(Z): -8.66198e+00, tol = 5.17435e+00, K = 1, iteration = 872, H = 2.04194e+00\n", + "log(Z): -8.65475e+00, tol = 5.16517e+00, K = 1, iteration = 873, H = 2.04186e+00\n", + "log(Z): -8.64755e+00, tol = 5.15602e+00, K = 1, iteration = 874, H = 2.04176e+00\n", + "log(Z): -8.64037e+00, tol = 5.14690e+00, K = 1, iteration = 875, H = 2.04165e+00\n", + "log(Z): -8.63326e+00, tol = 5.13784e+00, K = 1, iteration = 876, H = 2.04150e+00\n", + "log(Z): -8.62611e+00, tol = 5.12875e+00, K = 1, iteration = 877, H = 2.04139e+00\n", + "log(Z): -8.61900e+00, tol = 5.11969e+00, K = 1, iteration = 878, H = 2.04126e+00\n", + "log(Z): -8.61192e+00, tol = 5.11066e+00, K = 1, iteration = 879, H = 2.04113e+00\n", + "log(Z): -8.60485e+00, tol = 5.10164e+00, K = 1, iteration = 880, H = 2.04099e+00\n", + "log(Z): -8.59782e+00, tol = 5.09267e+00, K = 1, iteration = 881, H = 2.04083e+00\n", + "log(Z): -8.59083e+00, tol = 5.08374e+00, K = 1, iteration = 882, H = 2.04064e+00\n", + "log(Z): -8.58389e+00, tol = 5.07485e+00, K = 1, iteration = 883, H = 2.04043e+00\n", + "log(Z): -8.57697e+00, tol = 5.06599e+00, K = 1, iteration = 884, H = 2.04020e+00\n", + "log(Z): -8.57008e+00, tol = 5.05716e+00, K = 1, iteration = 885, H = 2.03997e+00\n", + "log(Z): -8.56310e+00, tol = 5.04823e+00, K = 1, iteration = 886, H = 2.03984e+00\n", + "log(Z): -8.55613e+00, tol = 5.03932e+00, K = 1, iteration = 887, H = 2.03971e+00\n", + "log(Z): -8.54919e+00, tol = 5.03044e+00, K = 1, iteration = 888, H = 2.03956e+00\n", + "log(Z): -8.54230e+00, tol = 5.02161e+00, K = 1, iteration = 889, H = 2.03939e+00\n", + "log(Z): -8.53547e+00, tol = 5.01283e+00, K = 1, iteration = 890, H = 2.03917e+00\n", + "log(Z): -8.52869e+00, tol = 5.00411e+00, K = 1, iteration = 891, H = 2.03892e+00\n", + "log(Z): -8.52191e+00, tol = 4.99540e+00, K = 1, iteration = 892, H = 2.03868e+00\n", + "log(Z): -8.51518e+00, tol = 4.98673e+00, K = 1, iteration = 893, H = 2.03840e+00\n", + "log(Z): -8.50851e+00, tol = 4.97812e+00, K = 1, iteration = 894, H = 2.03809e+00\n", + "log(Z): -8.50190e+00, tol = 4.96956e+00, K = 1, iteration = 895, H = 2.03774e+00\n", + "log(Z): -8.49528e+00, tol = 4.96100e+00, K = 1, iteration = 896, H = 2.03742e+00\n", + "log(Z): -8.48856e+00, tol = 4.95235e+00, K = 1, iteration = 897, H = 2.03720e+00\n", + "log(Z): -8.48187e+00, tol = 4.94372e+00, K = 1, iteration = 898, H = 2.03697e+00\n", + "log(Z): -8.47520e+00, tol = 4.93511e+00, K = 1, iteration = 899, H = 2.03673e+00\n", + "log(Z): -8.46857e+00, tol = 4.92654e+00, K = 1, iteration = 900, H = 2.03647e+00\n", + "log(Z): -8.46199e+00, tol = 4.91803e+00, K = 1, iteration = 901, H = 2.03617e+00\n", + "log(Z): -8.45547e+00, tol = 4.90956e+00, K = 1, iteration = 902, H = 2.03585e+00\n", + "log(Z): -8.44899e+00, tol = 4.90115e+00, K = 1, iteration = 903, H = 2.03549e+00\n", + "log(Z): -8.44245e+00, tol = 4.89267e+00, K = 1, iteration = 904, H = 2.03520e+00\n", + "log(Z): -8.43591e+00, tol = 4.88420e+00, K = 1, iteration = 905, H = 2.03493e+00\n", + "log(Z): -8.42938e+00, tol = 4.87573e+00, K = 1, iteration = 906, H = 2.03467e+00\n", + "log(Z): 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K = 1, iteration = 918, H = 2.03233e+00\n", + "log(Z): -8.34497e+00, tol = 4.76621e+00, K = 1, iteration = 919, H = 2.03206e+00\n", + "log(Z): -8.33868e+00, tol = 4.75799e+00, K = 1, iteration = 920, H = 2.03176e+00\n", + "log(Z): -8.33239e+00, tol = 4.74978e+00, K = 1, iteration = 921, H = 2.03148e+00\n", + "log(Z): -8.32610e+00, tol = 4.74155e+00, K = 1, iteration = 922, H = 2.03121e+00\n", + "log(Z): -8.31984e+00, tol = 4.73337e+00, K = 1, iteration = 923, H = 2.03093e+00\n", + "log(Z): -8.31345e+00, tol = 4.72505e+00, K = 1, iteration = 924, H = 2.03079e+00\n", + "log(Z): -8.30710e+00, tol = 4.71677e+00, K = 1, iteration = 925, H = 2.03063e+00\n", + "log(Z): -8.30066e+00, tol = 4.70841e+00, K = 1, iteration = 926, H = 2.03056e+00\n", + "log(Z): -8.29425e+00, tol = 4.70007e+00, K = 1, iteration = 927, H = 2.03048e+00\n", + "log(Z): -8.28767e+00, tol = 4.69158e+00, K = 1, iteration = 928, H = 2.03057e+00\n", + "log(Z): -8.28103e+00, tol = 4.68302e+00, K = 1, iteration = 929, H = 2.03075e+00\n", + "log(Z): -8.27440e+00, tol = 4.67446e+00, K = 1, iteration = 930, H = 2.03093e+00\n", + "log(Z): -8.26780e+00, tol = 4.66595e+00, K = 1, iteration = 931, H = 2.03108e+00\n", + "log(Z): -8.26126e+00, tol = 4.65748e+00, K = 1, iteration = 932, H = 2.03120e+00\n", + "log(Z): -8.25474e+00, tol = 4.64905e+00, K = 1, iteration = 933, H = 2.03129e+00\n", + "log(Z): -8.24824e+00, tol = 4.64064e+00, K = 1, iteration = 934, H = 2.03138e+00\n", + "log(Z): -8.24175e+00, tol = 4.63222e+00, K = 1, iteration = 935, H = 2.03148e+00\n", + "log(Z): -8.23520e+00, tol = 4.62376e+00, K = 1, iteration = 936, H = 2.03165e+00\n", + "log(Z): -8.22866e+00, tol = 4.61531e+00, K = 1, iteration = 937, H = 2.03182e+00\n", + "log(Z): -8.22206e+00, tol = 4.60679e+00, K = 1, iteration = 938, H = 2.03206e+00\n", + "log(Z): -8.21547e+00, tol = 4.59829e+00, K = 1, iteration = 939, H = 2.03231e+00\n", + "log(Z): -8.20890e+00, tol = 4.58980e+00, K = 1, iteration = 940, H = 2.03255e+00\n", + "log(Z): 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K = 1, iteration = 952, H = 2.03743e+00\n", + "log(Z): -8.12238e+00, tol = 4.47849e+00, K = 1, iteration = 953, H = 2.03775e+00\n", + "log(Z): -8.11591e+00, tol = 4.47011e+00, K = 1, iteration = 954, H = 2.03803e+00\n", + "log(Z): -8.10949e+00, tol = 4.46179e+00, K = 1, iteration = 955, H = 2.03827e+00\n", + "log(Z): -8.10308e+00, tol = 4.45348e+00, K = 1, iteration = 956, H = 2.03850e+00\n", + "log(Z): -8.09668e+00, tol = 4.44518e+00, K = 1, iteration = 957, H = 2.03874e+00\n", + "log(Z): -8.09033e+00, tol = 4.43692e+00, K = 1, iteration = 958, H = 2.03894e+00\n", + "log(Z): -8.08397e+00, tol = 4.42867e+00, K = 1, iteration = 959, H = 2.03916e+00\n", + "log(Z): -8.07765e+00, tol = 4.42044e+00, K = 1, iteration = 960, H = 2.03936e+00\n", + "log(Z): -8.07137e+00, tol = 4.41226e+00, K = 1, iteration = 961, H = 2.03952e+00\n", + "log(Z): -8.06512e+00, tol = 4.40411e+00, K = 1, iteration = 962, H = 2.03966e+00\n", + "log(Z): -8.05884e+00, tol = 4.39594e+00, K = 1, iteration = 963, H = 2.03984e+00\n", + "log(Z): -8.05261e+00, tol = 4.38781e+00, K = 1, iteration = 964, H = 2.03999e+00\n", + "log(Z): -8.04642e+00, tol = 4.37972e+00, K = 1, iteration = 965, H = 2.04011e+00\n", + "log(Z): -8.04014e+00, tol = 4.37155e+00, K = 1, iteration = 966, H = 2.04032e+00\n", + "log(Z): -8.03385e+00, tol = 4.36336e+00, K = 1, iteration = 967, H = 2.04057e+00\n", + "log(Z): -8.02759e+00, tol = 4.35520e+00, K = 1, iteration = 968, H = 2.04079e+00\n", + "log(Z): -8.02136e+00, tol = 4.34708e+00, K = 1, iteration = 969, H = 2.04099e+00\n", + "log(Z): -8.01517e+00, tol = 4.33900e+00, K = 1, iteration = 970, H = 2.04116e+00\n", + "log(Z): -8.00902e+00, tol = 4.33096e+00, K = 1, iteration = 971, H = 2.04130e+00\n", + "log(Z): -8.00288e+00, tol = 4.32292e+00, K = 1, iteration = 972, H = 2.04145e+00\n", + "log(Z): -7.99678e+00, tol = 4.31493e+00, K = 1, iteration = 973, H = 2.04156e+00\n", + "log(Z): -7.99067e+00, tol = 4.30693e+00, K = 1, iteration = 974, H = 2.04169e+00\n", + "log(Z): 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K = 1, iteration = 986, H = 2.04228e+00\n", + "log(Z): -7.91348e+00, tol = 4.20520e+00, K = 1, iteration = 987, H = 2.04224e+00\n", + "log(Z): -7.90771e+00, tol = 4.19755e+00, K = 1, iteration = 988, H = 2.04219e+00\n", + "log(Z): -7.90199e+00, tol = 4.18994e+00, K = 1, iteration = 989, H = 2.04212e+00\n", + "log(Z): -7.89628e+00, tol = 4.18235e+00, K = 1, iteration = 990, H = 2.04203e+00\n", + "log(Z): -7.89061e+00, tol = 4.17480e+00, K = 1, iteration = 991, H = 2.04193e+00\n", + "log(Z): -7.88496e+00, tol = 4.16727e+00, K = 1, iteration = 992, H = 2.04181e+00\n", + "log(Z): -7.87936e+00, tol = 4.15978e+00, K = 1, iteration = 993, H = 2.04166e+00\n", + "log(Z): -7.87377e+00, tol = 4.15232e+00, K = 1, iteration = 994, H = 2.04150e+00\n", + "log(Z): -7.86817e+00, tol = 4.14483e+00, K = 1, iteration = 995, H = 2.04138e+00\n", + "log(Z): -7.86255e+00, tol = 4.13734e+00, K = 1, iteration = 996, H = 2.04128e+00\n", + "log(Z): -7.85698e+00, tol = 4.12988e+00, K = 1, iteration = 997, H = 2.04115e+00\n", + "log(Z): -7.85142e+00, tol = 4.12245e+00, K = 1, iteration = 998, H = 2.04102e+00\n", + "log(Z): -7.84591e+00, tol = 4.11506e+00, K = 1, iteration = 999, H = 2.04085e+00\n", + "log(Z): -7.84028e+00, tol = 4.10756e+00, K = 1, iteration = 1000, H = 2.04081e+00\n", + "log(Z): -7.83468e+00, tol = 4.10009e+00, K = 1, iteration = 1001, H = 2.04075e+00\n", + "log(Z): -7.82906e+00, tol = 4.09259e+00, K = 1, iteration = 1002, H = 2.04073e+00\n", + "log(Z): -7.82338e+00, tol = 4.08504e+00, K = 1, iteration = 1003, H = 2.04077e+00\n", + "log(Z): -7.81772e+00, tol = 4.07751e+00, K = 1, iteration = 1004, H = 2.04081e+00\n", + "log(Z): -7.81201e+00, tol = 4.06992e+00, K = 1, iteration = 1005, H = 2.04091e+00\n", + "log(Z): -7.80631e+00, tol = 4.06236e+00, K = 1, iteration = 1006, H = 2.04100e+00\n", + "log(Z): -7.80065e+00, tol = 4.05484e+00, K = 1, iteration = 1007, H = 2.04107e+00\n", + "log(Z): -7.79500e+00, tol = 4.04732e+00, K = 1, iteration = 1008, H = 2.04115e+00\n", + 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3.87287e+00, K = 1, iteration = 1031, H = 2.04759e+00\n", + "log(Z): -7.65737e+00, tol = 3.86525e+00, K = 1, iteration = 1032, H = 2.04804e+00\n", + "log(Z): -7.65161e+00, tol = 3.85765e+00, K = 1, iteration = 1033, H = 2.04847e+00\n", + "log(Z): -7.64588e+00, tol = 3.85008e+00, K = 1, iteration = 1034, H = 2.04889e+00\n", + "log(Z): -7.64016e+00, tol = 3.84253e+00, K = 1, iteration = 1035, H = 2.04928e+00\n", + "log(Z): -7.63449e+00, tol = 3.83502e+00, K = 1, iteration = 1036, H = 2.04965e+00\n", + "log(Z): -7.62883e+00, tol = 3.82753e+00, K = 1, iteration = 1037, H = 2.05000e+00\n", + "log(Z): -7.62318e+00, tol = 3.82004e+00, K = 1, iteration = 1038, H = 2.05037e+00\n", + "log(Z): -7.61756e+00, tol = 3.81259e+00, K = 1, iteration = 1039, H = 2.05070e+00\n", + "log(Z): -7.61195e+00, tol = 3.80515e+00, K = 1, iteration = 1040, H = 2.05104e+00\n", + "log(Z): -7.60636e+00, tol = 3.79773e+00, K = 1, iteration = 1041, H = 2.05135e+00\n", + "log(Z): -7.60075e+00, tol = 3.79029e+00, K = 1, iteration = 1042, H = 2.05171e+00\n", + "log(Z): -7.59514e+00, tol = 3.78285e+00, K = 1, iteration = 1043, H = 2.05207e+00\n", + "log(Z): -7.58955e+00, tol = 3.77544e+00, K = 1, iteration = 1044, H = 2.05242e+00\n", + "log(Z): -7.58400e+00, tol = 3.76806e+00, K = 1, iteration = 1045, H = 2.05274e+00\n", + "log(Z): -7.57849e+00, tol = 3.76072e+00, K = 1, iteration = 1046, H = 2.05303e+00\n", + "log(Z): -7.57297e+00, tol = 3.75338e+00, K = 1, iteration = 1047, H = 2.05333e+00\n", + "log(Z): -7.56744e+00, tol = 3.74603e+00, K = 1, iteration = 1048, H = 2.05365e+00\n", + "log(Z): -7.56194e+00, tol = 3.73870e+00, K = 1, iteration = 1049, H = 2.05395e+00\n", + "log(Z): -7.55643e+00, tol = 3.73137e+00, K = 1, iteration = 1050, H = 2.05427e+00\n", + "log(Z): -7.55093e+00, tol = 3.72405e+00, K = 1, iteration = 1051, H = 2.05460e+00\n", + "log(Z): -7.54543e+00, tol = 3.71674e+00, K = 1, iteration = 1052, H = 2.05492e+00\n", + "log(Z): -7.53989e+00, tol = 3.70938e+00, K = 1, iteration = 1053, H = 2.05530e+00\n", + "log(Z): -7.53437e+00, tol = 3.70205e+00, K = 1, iteration = 1054, H = 2.05567e+00\n", + "log(Z): -7.52889e+00, tol = 3.69475e+00, K = 1, iteration = 1055, H = 2.05601e+00\n", + "log(Z): -7.52343e+00, tol = 3.68748e+00, K = 1, iteration = 1056, H = 2.05632e+00\n", + "log(Z): -7.51798e+00, tol = 3.68021e+00, K = 1, iteration = 1057, H = 2.05665e+00\n", + "log(Z): -7.51253e+00, tol = 3.67295e+00, K = 1, iteration = 1058, H = 2.05698e+00\n", + "log(Z): -7.50688e+00, tol = 3.66550e+00, K = 1, iteration = 1059, H = 2.05753e+00\n", + "log(Z): -7.50127e+00, tol = 3.65809e+00, K = 1, iteration = 1060, H = 2.05805e+00\n", + "log(Z): -7.49562e+00, tol = 3.65063e+00, K = 1, iteration = 1061, H = 2.05862e+00\n", + "log(Z): -7.49001e+00, tol = 3.64322e+00, K = 1, iteration = 1062, H = 2.05916e+00\n", + "log(Z): -7.48441e+00, tol = 3.63581e+00, K = 1, iteration = 1063, H = 2.05970e+00\n", + "log(Z): -7.47879e+00, tol = 3.62840e+00, K = 1, iteration = 1064, H = 2.06026e+00\n", + 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3.46020e+00, K = 1, iteration = 1087, H = 2.07317e+00\n", + "log(Z): -7.34619e+00, tol = 3.45304e+00, K = 1, iteration = 1088, H = 2.07367e+00\n", + "log(Z): -7.34082e+00, tol = 3.44591e+00, K = 1, iteration = 1089, H = 2.07415e+00\n", + "log(Z): -7.33549e+00, tol = 3.43881e+00, K = 1, iteration = 1090, H = 2.07461e+00\n", + "log(Z): -7.33016e+00, tol = 3.43172e+00, K = 1, iteration = 1091, H = 2.07507e+00\n", + "log(Z): -7.32486e+00, tol = 3.42465e+00, K = 1, iteration = 1092, H = 2.07550e+00\n", + "log(Z): -7.31958e+00, tol = 3.41762e+00, K = 1, iteration = 1093, H = 2.07592e+00\n", + "log(Z): -7.31433e+00, tol = 3.41060e+00, K = 1, iteration = 1094, H = 2.07633e+00\n", + "log(Z): -7.30908e+00, tol = 3.40359e+00, K = 1, iteration = 1095, H = 2.07673e+00\n", + "log(Z): -7.30387e+00, tol = 3.39662e+00, K = 1, iteration = 1096, H = 2.07710e+00\n", + "log(Z): -7.29866e+00, tol = 3.38965e+00, K = 1, iteration = 1097, H = 2.07749e+00\n", + "log(Z): -7.29346e+00, tol = 3.38270e+00, K = 1, 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2.08120e+00\n", + "log(Z): -7.23262e+00, tol = 3.30086e+00, K = 1, iteration = 1110, H = 2.08142e+00\n", + "log(Z): -7.22765e+00, tol = 3.29415e+00, K = 1, iteration = 1111, H = 2.08167e+00\n", + "log(Z): -7.22268e+00, tol = 3.28744e+00, K = 1, iteration = 1112, H = 2.08192e+00\n", + "log(Z): -7.21770e+00, tol = 3.28072e+00, K = 1, iteration = 1113, H = 2.08220e+00\n", + "log(Z): -7.21269e+00, tol = 3.27397e+00, K = 1, iteration = 1114, H = 2.08251e+00\n", + "log(Z): -7.20771e+00, tol = 3.26726e+00, K = 1, iteration = 1115, H = 2.08280e+00\n", + "log(Z): -7.20276e+00, tol = 3.26058e+00, K = 1, iteration = 1116, H = 2.08307e+00\n", + "log(Z): -7.19783e+00, tol = 3.25391e+00, K = 1, iteration = 1117, H = 2.08333e+00\n", + "log(Z): -7.19290e+00, tol = 3.24725e+00, K = 1, iteration = 1118, H = 2.08360e+00\n", + "log(Z): -7.18799e+00, tol = 3.24061e+00, K = 1, iteration = 1119, H = 2.08385e+00\n", + "log(Z): -7.18309e+00, tol = 3.23399e+00, K = 1, iteration = 1120, H = 2.08409e+00\n", + 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2.09483e+00\n", + "log(Z): -6.96685e+00, tol = 2.93987e+00, K = 1, iteration = 1166, H = 2.09490e+00\n", + "log(Z): -6.96250e+00, tol = 2.93385e+00, K = 1, iteration = 1167, H = 2.09497e+00\n", + "log(Z): -6.95814e+00, tol = 2.92784e+00, K = 1, iteration = 1168, H = 2.09505e+00\n", + "log(Z): -6.95381e+00, tol = 2.92184e+00, K = 1, iteration = 1169, H = 2.09512e+00\n", + "log(Z): -6.94949e+00, tol = 2.91586e+00, K = 1, iteration = 1170, H = 2.09518e+00\n", + "log(Z): -6.94520e+00, tol = 2.90991e+00, K = 1, iteration = 1171, H = 2.09522e+00\n", + "log(Z): -6.94090e+00, tol = 2.90396e+00, K = 1, iteration = 1172, H = 2.09527e+00\n", + "log(Z): -6.93661e+00, tol = 2.89801e+00, K = 1, iteration = 1173, H = 2.09533e+00\n", + "log(Z): -6.93231e+00, tol = 2.89206e+00, K = 1, iteration = 1174, H = 2.09540e+00\n", + "log(Z): -6.92803e+00, tol = 2.88614e+00, K = 1, iteration = 1175, H = 2.09546e+00\n", + "log(Z): -6.92377e+00, tol = 2.88023e+00, K = 1, iteration = 1176, H = 2.09551e+00\n", + 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2.74768e+00, K = 1, iteration = 1199, H = 2.09593e+00\n", + "log(Z): -6.82476e+00, tol = 2.74206e+00, K = 1, iteration = 1200, H = 2.09592e+00\n", + "log(Z): -6.82076e+00, tol = 2.73645e+00, K = 1, iteration = 1201, H = 2.09591e+00\n", + "log(Z): -6.81677e+00, tol = 2.73085e+00, K = 1, iteration = 1202, H = 2.09591e+00\n", + "log(Z): -6.81280e+00, tol = 2.72527e+00, K = 1, iteration = 1203, H = 2.09588e+00\n", + "log(Z): -6.80885e+00, tol = 2.71971e+00, K = 1, iteration = 1204, H = 2.09585e+00\n", + "log(Z): -6.80492e+00, tol = 2.71417e+00, K = 1, iteration = 1205, H = 2.09581e+00\n", + "log(Z): -6.80099e+00, tol = 2.70863e+00, K = 1, iteration = 1206, H = 2.09577e+00\n", + "log(Z): -6.79709e+00, tol = 2.70312e+00, K = 1, iteration = 1207, H = 2.09572e+00\n", + "log(Z): -6.79317e+00, tol = 2.69760e+00, K = 1, iteration = 1208, H = 2.09568e+00\n", + "log(Z): -6.78924e+00, tol = 2.69208e+00, K = 1, iteration = 1209, H = 2.09567e+00\n", + "log(Z): -6.78529e+00, tol = 2.68653e+00, K = 1, iteration = 1210, H = 2.09568e+00\n", + "log(Z): -6.78136e+00, tol = 2.68101e+00, K = 1, iteration = 1211, H = 2.09568e+00\n", + "log(Z): -6.77745e+00, tol = 2.67551e+00, K = 1, iteration = 1212, H = 2.09567e+00\n", + "log(Z): -6.77357e+00, tol = 2.67002e+00, K = 1, iteration = 1213, H = 2.09565e+00\n", + "log(Z): -6.76970e+00, tol = 2.66456e+00, K = 1, iteration = 1214, H = 2.09562e+00\n", + "log(Z): -6.76584e+00, tol = 2.65912e+00, K = 1, iteration = 1215, H = 2.09558e+00\n", + "log(Z): -6.76201e+00, tol = 2.65369e+00, K = 1, iteration = 1216, H = 2.09553e+00\n", + "log(Z): -6.75813e+00, tol = 2.64822e+00, K = 1, iteration = 1217, H = 2.09553e+00\n", + "log(Z): -6.75424e+00, tol = 2.64276e+00, K = 1, iteration = 1218, H = 2.09554e+00\n", + "log(Z): -6.75038e+00, tol = 2.63732e+00, K = 1, iteration = 1219, H = 2.09553e+00\n", + "log(Z): -6.74654e+00, tol = 2.63190e+00, K = 1, iteration = 1220, H = 2.09552e+00\n", + "log(Z): -6.74272e+00, tol = 2.62649e+00, K = 1, iteration = 1221, H = 2.09550e+00\n", + "log(Z): -6.73891e+00, tol = 2.62110e+00, K = 1, iteration = 1222, H = 2.09547e+00\n", + "log(Z): -6.73510e+00, tol = 2.61572e+00, K = 1, iteration = 1223, H = 2.09544e+00\n", + "log(Z): -6.73131e+00, tol = 2.61036e+00, K = 1, iteration = 1224, H = 2.09540e+00\n", + "log(Z): -6.72752e+00, tol = 2.60499e+00, K = 1, iteration = 1225, H = 2.09537e+00\n", + "log(Z): -6.72375e+00, tol = 2.59965e+00, K = 1, iteration = 1226, H = 2.09534e+00\n", + "log(Z): -6.71998e+00, tol = 2.59431e+00, K = 1, iteration = 1227, H = 2.09530e+00\n", + "log(Z): -6.71621e+00, tol = 2.58897e+00, K = 1, iteration = 1228, H = 2.09528e+00\n", + "log(Z): -6.71244e+00, tol = 2.58363e+00, K = 1, iteration = 1229, H = 2.09527e+00\n", + "log(Z): -6.70866e+00, tol = 2.57829e+00, K = 1, iteration = 1230, H = 2.09527e+00\n", + "log(Z): -6.70489e+00, tol = 2.57296e+00, K = 1, iteration = 1231, H = 2.09526e+00\n", + "log(Z): -6.70114e+00, tol = 2.56765e+00, K = 1, iteration = 1232, H = 2.09525e+00\n", + 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2.44717e+00, K = 1, iteration = 1255, H = 2.09589e+00\n", + "log(Z): -6.61237e+00, tol = 2.44207e+00, K = 1, iteration = 1256, H = 2.09588e+00\n", + "log(Z): -6.60881e+00, tol = 2.43698e+00, K = 1, iteration = 1257, H = 2.09587e+00\n", + "log(Z): -6.60525e+00, tol = 2.43192e+00, K = 1, iteration = 1258, H = 2.09585e+00\n", + "log(Z): -6.60171e+00, tol = 2.42686e+00, K = 1, iteration = 1259, H = 2.09582e+00\n", + "log(Z): -6.59819e+00, tol = 2.42183e+00, K = 1, iteration = 1260, H = 2.09578e+00\n", + "log(Z): -6.59468e+00, tol = 2.41681e+00, K = 1, iteration = 1261, H = 2.09574e+00\n", + "log(Z): -6.59117e+00, tol = 2.41180e+00, K = 1, iteration = 1262, H = 2.09570e+00\n", + "log(Z): -6.58765e+00, tol = 2.40677e+00, K = 1, iteration = 1263, H = 2.09568e+00\n", + "log(Z): -6.58414e+00, tol = 2.40176e+00, K = 1, iteration = 1264, H = 2.09566e+00\n", + "log(Z): -6.58066e+00, tol = 2.39677e+00, K = 1, iteration = 1265, H = 2.09562e+00\n", + "log(Z): -6.57717e+00, tol = 2.39179e+00, K = 1, iteration = 1266, H = 2.09559e+00\n", + "log(Z): -6.57369e+00, tol = 2.38681e+00, K = 1, iteration = 1267, H = 2.09556e+00\n", + "log(Z): -6.57022e+00, tol = 2.38184e+00, K = 1, iteration = 1268, H = 2.09553e+00\n", + "log(Z): -6.56677e+00, tol = 2.37690e+00, K = 1, iteration = 1269, H = 2.09548e+00\n", + "log(Z): -6.56334e+00, tol = 2.37197e+00, K = 1, iteration = 1270, H = 2.09542e+00\n", + "log(Z): -6.55993e+00, tol = 2.36707e+00, K = 1, iteration = 1271, H = 2.09536e+00\n", + "log(Z): -6.55651e+00, tol = 2.36216e+00, K = 1, iteration = 1272, H = 2.09530e+00\n", + "log(Z): -6.55310e+00, tol = 2.35726e+00, K = 1, iteration = 1273, H = 2.09524e+00\n", + "log(Z): -6.54968e+00, tol = 2.35235e+00, K = 1, iteration = 1274, H = 2.09520e+00\n", + "log(Z): -6.54628e+00, tol = 2.34747e+00, K = 1, iteration = 1275, H = 2.09515e+00\n", + "log(Z): -6.54286e+00, tol = 2.34256e+00, K = 1, iteration = 1276, H = 2.09513e+00\n", + "log(Z): -6.53945e+00, tol = 2.33768e+00, K = 1, iteration = 1277, H = 2.09510e+00\n", + "log(Z): -6.53605e+00, tol = 2.33591e+00, K = 1, iteration = 1278, H = 2.09506e+00\n", + "log(Z): -6.53264e+00, tol = 2.33102e+00, K = 1, iteration = 1279, H = 2.09504e+00\n", + "log(Z): -6.52922e+00, tol = 2.32613e+00, K = 1, iteration = 1280, H = 2.09505e+00\n", + "log(Z): -6.52581e+00, tol = 2.32125e+00, K = 1, iteration = 1281, H = 2.09504e+00\n", + "log(Z): -6.52241e+00, tol = 2.31638e+00, K = 1, iteration = 1282, H = 2.09504e+00\n", + "log(Z): -6.51901e+00, tol = 2.31151e+00, K = 1, iteration = 1283, H = 2.09505e+00\n", + "log(Z): -6.51560e+00, tol = 2.30665e+00, K = 1, iteration = 1284, H = 2.09506e+00\n", + "log(Z): -6.51222e+00, tol = 2.30179e+00, K = 1, iteration = 1285, H = 2.09506e+00\n", + "log(Z): -6.50883e+00, tol = 2.29694e+00, K = 1, iteration = 1286, H = 2.09507e+00\n", + "log(Z): -6.50545e+00, tol = 2.29211e+00, K = 1, iteration = 1287, H = 2.09508e+00\n", + "log(Z): -6.50208e+00, tol = 2.28728e+00, K = 1, iteration = 1288, H = 2.09508e+00\n", + 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2.17880e+00, K = 1, iteration = 1311, H = 2.09506e+00\n", + "log(Z): -6.42337e+00, tol = 2.17420e+00, K = 1, iteration = 1312, H = 2.09503e+00\n", + "log(Z): -6.42020e+00, tol = 2.16962e+00, K = 1, iteration = 1313, H = 2.09500e+00\n", + "log(Z): -6.41705e+00, tol = 2.16506e+00, K = 1, iteration = 1314, H = 2.09496e+00\n", + "log(Z): -6.41390e+00, tol = 2.16050e+00, K = 1, iteration = 1315, H = 2.09493e+00\n", + "log(Z): -6.41074e+00, tol = 2.15594e+00, K = 1, iteration = 1316, H = 2.09490e+00\n", + "log(Z): -6.40760e+00, tol = 2.15139e+00, K = 1, iteration = 1317, H = 2.09487e+00\n", + "log(Z): -6.40447e+00, tol = 2.14686e+00, K = 1, iteration = 1318, H = 2.09483e+00\n", + "log(Z): -6.40135e+00, tol = 2.14234e+00, K = 1, iteration = 1319, H = 2.09479e+00\n", + "log(Z): -6.39825e+00, tol = 2.13784e+00, K = 1, iteration = 1320, H = 2.09474e+00\n", + "log(Z): -6.39516e+00, tol = 2.13335e+00, K = 1, iteration = 1321, H = 2.09468e+00\n", + "log(Z): -6.39208e+00, tol = 2.12888e+00, K = 1, iteration = 1322, H = 2.09462e+00\n", + "log(Z): -6.38902e+00, tol = 2.12441e+00, K = 1, iteration = 1323, H = 2.09455e+00\n", + "log(Z): -6.38595e+00, tol = 2.11996e+00, K = 1, iteration = 1324, H = 2.09448e+00\n", + "log(Z): -6.38290e+00, tol = 2.11551e+00, K = 1, iteration = 1325, H = 2.09442e+00\n", + "log(Z): -6.37985e+00, tol = 2.11108e+00, K = 1, iteration = 1326, H = 2.09435e+00\n", + "log(Z): -6.37683e+00, tol = 2.10666e+00, K = 1, iteration = 1327, H = 2.09427e+00\n", + "log(Z): -6.37380e+00, tol = 2.10225e+00, K = 1, iteration = 1328, H = 2.09419e+00\n", + "log(Z): -6.37080e+00, tol = 2.09785e+00, K = 1, iteration = 1329, H = 2.09410e+00\n", + "log(Z): -6.36779e+00, tol = 2.09346e+00, K = 1, iteration = 1330, H = 2.09402e+00\n", + "log(Z): -6.36478e+00, tol = 2.08907e+00, K = 1, iteration = 1331, H = 2.09395e+00\n", + "log(Z): -6.36176e+00, tol = 2.08468e+00, K = 1, iteration = 1332, H = 2.09389e+00\n", + "log(Z): -6.35875e+00, tol = 2.08029e+00, K = 1, iteration = 1333, H = 2.09382e+00\n", + "log(Z): -6.35575e+00, tol = 2.07592e+00, K = 1, iteration = 1334, H = 2.09376e+00\n", + "log(Z): -6.35276e+00, tol = 2.07156e+00, K = 1, iteration = 1335, H = 2.09369e+00\n", + "log(Z): -6.34978e+00, tol = 2.06720e+00, K = 1, iteration = 1336, H = 2.09362e+00\n", + "log(Z): -6.34681e+00, tol = 2.06286e+00, K = 1, iteration = 1337, H = 2.09355e+00\n", + "log(Z): -6.34383e+00, tol = 2.05852e+00, K = 1, iteration = 1338, H = 2.09349e+00\n", + "log(Z): -6.34087e+00, tol = 2.05419e+00, K = 1, iteration = 1339, H = 2.09342e+00\n", + "log(Z): -6.33792e+00, tol = 2.04988e+00, K = 1, iteration = 1340, H = 2.09334e+00\n", + "log(Z): -6.33498e+00, tol = 2.04558e+00, K = 1, iteration = 1341, H = 2.09326e+00\n", + "log(Z): -6.33204e+00, tol = 2.04128e+00, K = 1, iteration = 1342, H = 2.09318e+00\n", + "log(Z): -6.32911e+00, tol = 2.03698e+00, K = 1, iteration = 1343, H = 2.09311e+00\n", + "log(Z): -6.32617e+00, tol = 2.03269e+00, K = 1, iteration = 1344, H = 2.09305e+00\n", + 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1.93641e+00, K = 1, iteration = 1367, H = 2.09136e+00\n", + "log(Z): -6.25774e+00, tol = 1.93230e+00, K = 1, iteration = 1368, H = 2.09130e+00\n", + "log(Z): -6.25493e+00, tol = 1.92820e+00, K = 1, iteration = 1369, H = 2.09125e+00\n", + "log(Z): -6.25214e+00, tol = 1.92410e+00, K = 1, iteration = 1370, H = 2.09120e+00\n", + "log(Z): -6.24936e+00, tol = 1.92002e+00, K = 1, iteration = 1371, H = 2.09115e+00\n", + "log(Z): -6.24659e+00, tol = 1.91595e+00, K = 1, iteration = 1372, H = 2.09109e+00\n", + "log(Z): -6.24382e+00, tol = 1.91189e+00, K = 1, iteration = 1373, H = 2.09103e+00\n", + "log(Z): -6.24107e+00, tol = 1.90784e+00, K = 1, iteration = 1374, H = 2.09096e+00\n", + "log(Z): -6.23832e+00, tol = 1.90380e+00, K = 1, iteration = 1375, H = 2.09089e+00\n", + "log(Z): -6.23557e+00, tol = 1.89976e+00, K = 1, iteration = 1376, H = 2.09083e+00\n", + "log(Z): -6.23284e+00, tol = 1.89573e+00, K = 1, iteration = 1377, H = 2.09076e+00\n", + "log(Z): -6.23011e+00, tol = 1.89172e+00, K = 1, iteration = 1378, H = 2.09069e+00\n", + "log(Z): -6.22739e+00, tol = 1.88771e+00, K = 1, iteration = 1379, H = 2.09062e+00\n", + "log(Z): -6.22467e+00, tol = 1.88371e+00, K = 1, iteration = 1380, H = 2.09055e+00\n", + "log(Z): -6.22197e+00, tol = 1.87972e+00, K = 1, iteration = 1381, H = 2.09047e+00\n", + "log(Z): -6.21928e+00, tol = 1.87575e+00, K = 1, iteration = 1382, H = 2.09039e+00\n", + "log(Z): -6.21660e+00, tol = 1.87178e+00, K = 1, iteration = 1383, H = 2.09031e+00\n", + "log(Z): -6.21393e+00, tol = 1.86783e+00, K = 1, iteration = 1384, H = 2.09022e+00\n", + "log(Z): -6.21126e+00, tol = 1.86389e+00, K = 1, iteration = 1385, H = 2.09013e+00\n", + "log(Z): -6.20861e+00, tol = 1.85996e+00, K = 1, iteration = 1386, H = 2.09003e+00\n", + "log(Z): -6.20596e+00, tol = 1.85604e+00, K = 1, iteration = 1387, H = 2.08993e+00\n", + "log(Z): -6.20333e+00, tol = 1.85213e+00, K = 1, iteration = 1388, H = 2.08983e+00\n", + "log(Z): -6.20070e+00, tol = 1.84823e+00, K = 1, iteration = 1389, H = 2.08972e+00\n", + "log(Z): -6.19809e+00, tol = 1.84434e+00, K = 1, iteration = 1390, H = 2.08961e+00\n", + "log(Z): -6.19548e+00, tol = 1.84047e+00, K = 1, iteration = 1391, H = 2.08950e+00\n", + "log(Z): -6.19287e+00, tol = 1.83659e+00, K = 1, iteration = 1392, H = 2.08940e+00\n", + "log(Z): -6.19026e+00, tol = 1.83271e+00, K = 1, iteration = 1393, H = 2.08929e+00\n", + "log(Z): -6.18765e+00, tol = 1.82885e+00, K = 1, iteration = 1394, H = 2.08919e+00\n", + "log(Z): -6.18505e+00, tol = 1.82499e+00, K = 1, iteration = 1395, H = 2.08910e+00\n", + "log(Z): -6.18246e+00, tol = 1.82114e+00, K = 1, iteration = 1396, H = 2.08900e+00\n", + "log(Z): -6.17986e+00, tol = 1.81729e+00, K = 1, iteration = 1397, H = 2.08891e+00\n", + "log(Z): -6.17727e+00, tol = 1.81344e+00, K = 1, iteration = 1398, H = 2.08881e+00\n", + "log(Z): -6.17469e+00, tol = 1.80961e+00, K = 1, iteration = 1399, H = 2.08873e+00\n", + "log(Z): -6.17211e+00, tol = 1.80578e+00, K = 1, iteration = 1400, H = 2.08863e+00\n", + 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1.71971e+00, K = 1, iteration = 1423, H = 2.08663e+00\n", + "log(Z): -6.11175e+00, tol = 1.71605e+00, K = 1, iteration = 1424, H = 2.08655e+00\n", + "log(Z): -6.10929e+00, tol = 1.71240e+00, K = 1, iteration = 1425, H = 2.08647e+00\n", + "log(Z): -6.10682e+00, tol = 1.70874e+00, K = 1, iteration = 1426, H = 2.08641e+00\n", + "log(Z): -6.10436e+00, tol = 1.70508e+00, K = 1, iteration = 1427, H = 2.08635e+00\n", + "log(Z): -6.10189e+00, tol = 1.70143e+00, K = 1, iteration = 1428, H = 2.08630e+00\n", + "log(Z): -6.09943e+00, tol = 1.69778e+00, K = 1, iteration = 1429, H = 2.08625e+00\n", + "log(Z): -6.09697e+00, tol = 1.69414e+00, K = 1, iteration = 1430, H = 2.08619e+00\n", + "log(Z): -6.09451e+00, tol = 1.69051e+00, K = 1, iteration = 1431, H = 2.08614e+00\n", + "log(Z): -6.09207e+00, tol = 1.68689e+00, K = 1, iteration = 1432, H = 2.08609e+00\n", + "log(Z): -6.08963e+00, tol = 1.68326e+00, K = 1, iteration = 1433, H = 2.08604e+00\n", + "log(Z): -6.08719e+00, tol = 1.67965e+00, K = 1, iteration = 1434, H = 2.08598e+00\n", + "log(Z): -6.08476e+00, tol = 1.67605e+00, K = 1, iteration = 1435, H = 2.08593e+00\n", + "log(Z): -6.08233e+00, tol = 1.67245e+00, K = 1, iteration = 1436, H = 2.08588e+00\n", + "log(Z): -6.07990e+00, tol = 1.66885e+00, K = 1, iteration = 1437, H = 2.08584e+00\n", + "log(Z): -6.07747e+00, tol = 1.66527e+00, K = 1, iteration = 1438, H = 2.08579e+00\n", + "log(Z): -6.07506e+00, tol = 1.66169e+00, K = 1, iteration = 1439, H = 2.08574e+00\n", + "log(Z): -6.07265e+00, tol = 1.65812e+00, K = 1, iteration = 1440, H = 2.08569e+00\n", + "log(Z): -6.07025e+00, tol = 1.65456e+00, K = 1, iteration = 1441, H = 2.08564e+00\n", + "log(Z): -6.06786e+00, tol = 1.65101e+00, K = 1, iteration = 1442, H = 2.08558e+00\n", + "log(Z): -6.06548e+00, tol = 1.64746e+00, K = 1, iteration = 1443, H = 2.08552e+00\n", + "log(Z): -6.06309e+00, tol = 1.64393e+00, K = 1, iteration = 1444, H = 2.08547e+00\n", + "log(Z): -6.06071e+00, tol = 1.64040e+00, K = 1, iteration = 1445, H = 2.08541e+00\n", + "log(Z): -6.05835e+00, tol = 1.63688e+00, K = 1, iteration = 1446, H = 2.08535e+00\n", + "log(Z): -6.05598e+00, tol = 1.63336e+00, K = 1, iteration = 1447, H = 2.08529e+00\n", + "log(Z): -6.05363e+00, tol = 1.62986e+00, K = 1, iteration = 1448, H = 2.08523e+00\n", + "log(Z): -6.05128e+00, tol = 1.62636e+00, K = 1, iteration = 1449, H = 2.08517e+00\n", + "log(Z): -6.04892e+00, tol = 1.62287e+00, K = 1, iteration = 1450, H = 2.08511e+00\n", + "log(Z): -6.04658e+00, tol = 1.61938e+00, K = 1, iteration = 1451, H = 2.08505e+00\n", + "log(Z): -6.04424e+00, tol = 1.61590e+00, K = 1, iteration = 1452, H = 2.08499e+00\n", + "log(Z): -6.04191e+00, tol = 1.61244e+00, K = 1, iteration = 1453, H = 2.08493e+00\n", + "log(Z): -6.03959e+00, tol = 1.60898e+00, K = 1, iteration = 1454, H = 2.08486e+00\n", + "log(Z): -6.03728e+00, tol = 1.60553e+00, K = 1, iteration = 1455, H = 2.08479e+00\n", + "log(Z): -6.03497e+00, tol = 1.60209e+00, K = 1, iteration = 1456, H = 2.08472e+00\n", + 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1.52452e+00, K = 1, iteration = 1479, H = 2.08347e+00\n", + "log(Z): -5.98063e+00, tol = 1.52123e+00, K = 1, iteration = 1480, H = 2.08341e+00\n", + "log(Z): -5.97842e+00, tol = 1.51794e+00, K = 1, iteration = 1481, H = 2.08336e+00\n", + "log(Z): -5.97622e+00, tol = 1.51466e+00, K = 1, iteration = 1482, H = 2.08331e+00\n", + "log(Z): -5.97401e+00, tol = 1.51138e+00, K = 1, iteration = 1483, H = 2.08326e+00\n", + "log(Z): -5.97182e+00, tol = 1.50811e+00, K = 1, iteration = 1484, H = 2.08321e+00\n", + "log(Z): -5.96963e+00, tol = 1.50485e+00, K = 1, iteration = 1485, H = 2.08316e+00\n", + "log(Z): -5.96744e+00, tol = 1.50160e+00, K = 1, iteration = 1486, H = 2.08311e+00\n", + "log(Z): -5.96526e+00, tol = 1.49835e+00, K = 1, iteration = 1487, H = 2.08306e+00\n", + "log(Z): -5.96309e+00, tol = 1.49511e+00, K = 1, iteration = 1488, H = 2.08300e+00\n", + "log(Z): -5.96093e+00, tol = 1.49188e+00, K = 1, iteration = 1489, H = 2.08294e+00\n", + "log(Z): -5.95877e+00, tol = 1.48866e+00, K = 1, iteration = 1490, H = 2.08288e+00\n", + "log(Z): -5.95662e+00, tol = 1.48545e+00, K = 1, iteration = 1491, H = 2.08282e+00\n", + "log(Z): -5.95446e+00, tol = 1.48224e+00, K = 1, iteration = 1492, H = 2.08277e+00\n", + "log(Z): -5.95231e+00, tol = 1.47903e+00, K = 1, iteration = 1493, H = 2.08271e+00\n", + "log(Z): -5.95017e+00, tol = 1.47583e+00, K = 1, iteration = 1494, H = 2.08265e+00\n", + "log(Z): -5.94804e+00, tol = 1.47265e+00, K = 1, iteration = 1495, H = 2.08259e+00\n", + "log(Z): -5.94591e+00, tol = 1.46946e+00, K = 1, iteration = 1496, H = 2.08253e+00\n", + "log(Z): -5.94378e+00, tol = 1.46629e+00, K = 1, iteration = 1497, H = 2.08247e+00\n", + "log(Z): -5.94166e+00, tol = 1.46312e+00, K = 1, iteration = 1498, H = 2.08241e+00\n", + "log(Z): -5.93954e+00, tol = 1.45996e+00, K = 1, iteration = 1499, H = 2.08235e+00\n", + "log(Z): -5.93743e+00, tol = 1.45680e+00, K = 1, iteration = 1500, H = 2.08229e+00\n", + "log(Z): -5.93532e+00, tol = 1.45365e+00, K = 1, iteration = 1501, H = 2.08223e+00\n", + "log(Z): -5.93323e+00, tol = 1.45052e+00, K = 1, iteration = 1502, H = 2.08216e+00\n", + "log(Z): -5.93113e+00, tol = 1.44738e+00, K = 1, iteration = 1503, H = 2.08210e+00\n", + "log(Z): -5.92903e+00, tol = 1.44425e+00, K = 1, iteration = 1504, H = 2.08204e+00\n", + "log(Z): -5.92694e+00, tol = 1.44112e+00, K = 1, iteration = 1505, H = 2.08199e+00\n", + "log(Z): -5.92484e+00, tol = 1.43800e+00, K = 1, iteration = 1506, H = 2.08193e+00\n", + "log(Z): -5.92275e+00, tol = 1.43488e+00, K = 1, iteration = 1507, H = 2.08189e+00\n", + "log(Z): -5.92065e+00, tol = 1.43175e+00, K = 1, iteration = 1508, H = 2.08185e+00\n", + "log(Z): -5.91855e+00, tol = 1.42864e+00, K = 1, iteration = 1509, H = 2.08181e+00\n", + "log(Z): -5.91645e+00, tol = 1.42552e+00, K = 1, iteration = 1510, H = 2.08178e+00\n", + "log(Z): -5.91436e+00, tol = 1.42242e+00, K = 1, iteration = 1511, H = 2.08175e+00\n", + "log(Z): -5.91228e+00, tol = 1.41932e+00, K = 1, iteration = 1512, H = 2.08171e+00\n", + "log(Z): -5.91019e+00, tol = 1.41622e+00, K = 1, iteration = 1513, H = 2.08168e+00\n", + "log(Z): -5.90811e+00, tol = 1.41313e+00, K = 1, iteration = 1514, H = 2.08165e+00\n", + "log(Z): -5.90603e+00, tol = 1.41005e+00, K = 1, iteration = 1515, H = 2.08162e+00\n", + "log(Z): -5.90395e+00, tol = 1.40697e+00, K = 1, iteration = 1516, H = 2.08159e+00\n", + "log(Z): -5.90188e+00, tol = 1.40389e+00, K = 1, iteration = 1517, H = 2.08157e+00\n", + "log(Z): -5.89981e+00, tol = 1.40083e+00, K = 1, iteration = 1518, H = 2.08154e+00\n", + "log(Z): -5.89775e+00, tol = 1.39777e+00, K = 1, iteration = 1519, H = 2.08151e+00\n", + "log(Z): -5.89569e+00, tol = 1.39471e+00, K = 1, iteration = 1520, H = 2.08149e+00\n", + "log(Z): -5.89363e+00, tol = 1.39166e+00, K = 1, iteration = 1521, H = 2.08146e+00\n", + "log(Z): -5.89159e+00, tol = 1.38862e+00, K = 1, iteration = 1522, H = 2.08143e+00\n", + "log(Z): -5.88954e+00, tol = 1.38559e+00, K = 1, iteration = 1523, H = 2.08140e+00\n", + "log(Z): 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1.34974e+00, K = 1, iteration = 1535, H = 2.08094e+00\n", + "log(Z): -5.86346e+00, tol = 1.34679e+00, K = 1, iteration = 1536, H = 2.08090e+00\n", + "log(Z): -5.86148e+00, tol = 1.34385e+00, K = 1, iteration = 1537, H = 2.08087e+00\n", + "log(Z): -5.85950e+00, tol = 1.34091e+00, K = 1, iteration = 1538, H = 2.08083e+00\n", + "log(Z): -5.85753e+00, tol = 1.33798e+00, K = 1, iteration = 1539, H = 2.08080e+00\n", + "log(Z): -5.85556e+00, tol = 1.33505e+00, K = 1, iteration = 1540, H = 2.08077e+00\n", + "log(Z): -5.85359e+00, tol = 1.33213e+00, K = 1, iteration = 1541, H = 2.08073e+00\n", + "log(Z): -5.85163e+00, tol = 1.32921e+00, K = 1, iteration = 1542, H = 2.08070e+00\n", + "log(Z): -5.84965e+00, tol = 1.32629e+00, K = 1, iteration = 1543, H = 2.08069e+00\n", + "log(Z): -5.84768e+00, tol = 1.32338e+00, K = 1, iteration = 1544, H = 2.08067e+00\n", + "log(Z): -5.84572e+00, tol = 1.32047e+00, K = 1, iteration = 1545, H = 2.08065e+00\n", + "log(Z): -5.84376e+00, tol = 1.31757e+00, K = 1, iteration = 1546, H = 2.08063e+00\n", + "log(Z): -5.84181e+00, tol = 1.31468e+00, K = 1, iteration = 1547, H = 2.08061e+00\n", + "log(Z): -5.83986e+00, tol = 1.31179e+00, K = 1, iteration = 1548, H = 2.08058e+00\n", + "log(Z): -5.83792e+00, tol = 1.30891e+00, K = 1, iteration = 1549, H = 2.08056e+00\n", + "log(Z): -5.83598e+00, tol = 1.30604e+00, K = 1, iteration = 1550, H = 2.08053e+00\n", + "log(Z): -5.83406e+00, tol = 1.30318e+00, K = 1, iteration = 1551, H = 2.08050e+00\n", + "log(Z): -5.83213e+00, tol = 1.30033e+00, K = 1, iteration = 1552, H = 2.08046e+00\n", + "log(Z): -5.83021e+00, tol = 1.29747e+00, K = 1, iteration = 1553, H = 2.08044e+00\n", + "log(Z): -5.82829e+00, tol = 1.29462e+00, K = 1, iteration = 1554, H = 2.08041e+00\n", + "log(Z): -5.82637e+00, tol = 1.29178e+00, K = 1, iteration = 1555, H = 2.08038e+00\n", + "log(Z): -5.82446e+00, tol = 1.28895e+00, K = 1, iteration = 1556, H = 2.08035e+00\n", + "log(Z): -5.82255e+00, tol = 1.28612e+00, K = 1, iteration = 1557, H = 2.08033e+00\n", + "log(Z): -5.82065e+00, tol = 1.28330e+00, K = 1, iteration = 1558, H = 2.08030e+00\n", + "log(Z): -5.81875e+00, tol = 1.28048e+00, K = 1, iteration = 1559, H = 2.08027e+00\n", + "log(Z): -5.81686e+00, tol = 1.27767e+00, K = 1, iteration = 1560, H = 2.08024e+00\n", + "log(Z): -5.81496e+00, tol = 1.27486e+00, K = 1, iteration = 1561, H = 2.08022e+00\n", + "log(Z): -5.81305e+00, tol = 1.27204e+00, K = 1, iteration = 1562, H = 2.08021e+00\n", + "log(Z): -5.81114e+00, tol = 1.26923e+00, K = 1, iteration = 1563, H = 2.08021e+00\n", + "log(Z): -5.80924e+00, tol = 1.26643e+00, K = 1, iteration = 1564, H = 2.08020e+00\n", + "log(Z): -5.80734e+00, tol = 1.26363e+00, K = 1, iteration = 1565, H = 2.08019e+00\n", + "log(Z): -5.80545e+00, tol = 1.26084e+00, K = 1, iteration = 1566, H = 2.08019e+00\n", + "log(Z): -5.80356e+00, tol = 1.25806e+00, K = 1, iteration = 1567, H = 2.08017e+00\n", + "log(Z): -5.80168e+00, tol = 1.25528e+00, K = 1, iteration = 1568, H = 2.08016e+00\n", + "log(Z): -5.79980e+00, tol = 1.25250e+00, K = 1, iteration = 1569, H = 2.08016e+00\n", + "log(Z): -5.79792e+00, tol = 1.24974e+00, K = 1, iteration = 1570, H = 2.08015e+00\n", + "log(Z): -5.79605e+00, tol = 1.24698e+00, K = 1, iteration = 1571, H = 2.08014e+00\n", + "log(Z): -5.79418e+00, tol = 1.24422e+00, K = 1, iteration = 1572, H = 2.08013e+00\n", + "log(Z): -5.79232e+00, tol = 1.24147e+00, K = 1, iteration = 1573, H = 2.08012e+00\n", + "log(Z): -5.79046e+00, tol = 1.23873e+00, K = 1, iteration = 1574, H = 2.08011e+00\n", + "log(Z): -5.78860e+00, tol = 1.23599e+00, K = 1, iteration = 1575, H = 2.08010e+00\n", + "log(Z): -5.78675e+00, tol = 1.23326e+00, K = 1, iteration = 1576, H = 2.08008e+00\n", + "log(Z): -5.78490e+00, tol = 1.23053e+00, K = 1, iteration = 1577, H = 2.08008e+00\n", + "log(Z): -5.78305e+00, tol = 1.22781e+00, K = 1, iteration = 1578, H = 2.08007e+00\n", + "log(Z): -5.78121e+00, tol = 1.22510e+00, K = 1, iteration = 1579, H = 2.08006e+00\n", + "log(Z): -5.77937e+00, tol = 1.22239e+00, K = 1, iteration = 1580, H = 2.08005e+00\n", + "log(Z): -5.77754e+00, tol = 1.21968e+00, K = 1, iteration = 1581, H = 2.08004e+00\n", + "log(Z): -5.77571e+00, tol = 1.21699e+00, K = 1, iteration = 1582, H = 2.08003e+00\n", + "log(Z): -5.77388e+00, tol = 1.21429e+00, K = 1, iteration = 1583, H = 2.08002e+00\n", + "log(Z): -5.77205e+00, tol = 1.21160e+00, K = 1, iteration = 1584, H = 2.08001e+00\n", + "log(Z): -5.77023e+00, tol = 1.20892e+00, K = 1, iteration = 1585, H = 2.08000e+00\n", + "log(Z): -5.76840e+00, tol = 1.20624e+00, K = 1, iteration = 1586, H = 2.08000e+00\n", + "log(Z): -5.76658e+00, tol = 1.20356e+00, K = 1, iteration = 1587, H = 2.08001e+00\n", + "log(Z): -5.76476e+00, tol = 1.20089e+00, K = 1, iteration = 1588, H = 2.08001e+00\n", + "log(Z): -5.76294e+00, tol = 1.19822e+00, K = 1, iteration = 1589, H = 2.08001e+00\n", + "log(Z): -5.76112e+00, tol = 1.19556e+00, K = 1, iteration = 1590, H = 2.08002e+00\n", + "log(Z): -5.75932e+00, tol = 1.19290e+00, K = 1, iteration = 1591, H = 2.08002e+00\n", + "log(Z): -5.75751e+00, tol = 1.19025e+00, K = 1, iteration = 1592, H = 2.08002e+00\n", + "log(Z): -5.75571e+00, tol = 1.18761e+00, K = 1, iteration = 1593, H = 2.08002e+00\n", + "log(Z): -5.75391e+00, tol = 1.18497e+00, K = 1, iteration = 1594, H = 2.08002e+00\n", + "log(Z): -5.75211e+00, tol = 1.18233e+00, K = 1, iteration = 1595, H = 2.08003e+00\n", + "log(Z): -5.75032e+00, tol = 1.17971e+00, K = 1, iteration = 1596, H = 2.08003e+00\n", + "log(Z): -5.74854e+00, tol = 1.17709e+00, K = 1, iteration = 1597, H = 2.08003e+00\n", + "log(Z): -5.74676e+00, tol = 1.17448e+00, K = 1, iteration = 1598, H = 2.08002e+00\n", + "log(Z): -5.74498e+00, tol = 1.17187e+00, K = 1, iteration = 1599, H = 2.08001e+00\n", + "log(Z): -5.74322e+00, tol = 1.16927e+00, K = 1, iteration = 1600, H = 2.08001e+00\n", + "log(Z): -5.74145e+00, tol = 1.16668e+00, K = 1, iteration = 1601, H = 2.08000e+00\n", + "log(Z): -5.73969e+00, tol = 1.16409e+00, K = 1, iteration = 1602, H = 2.07999e+00\n", + "log(Z): -5.73794e+00, tol = 1.16151e+00, K = 1, iteration = 1603, H = 2.07998e+00\n", + "log(Z): -5.73618e+00, tol = 1.15893e+00, K = 1, iteration = 1604, H = 2.07998e+00\n", + "log(Z): -5.73443e+00, tol = 1.15635e+00, K = 1, iteration = 1605, H = 2.07997e+00\n", + "log(Z): -5.73268e+00, tol = 1.15379e+00, K = 1, iteration = 1606, H = 2.07996e+00\n", + "log(Z): -5.73094e+00, tol = 1.15123e+00, K = 1, iteration = 1607, H = 2.07995e+00\n", + "log(Z): -5.72921e+00, tol = 1.14868e+00, K = 1, iteration = 1608, H = 2.07994e+00\n", + "log(Z): -5.72748e+00, tol = 1.14613e+00, K = 1, iteration = 1609, H = 2.07992e+00\n", + "log(Z): -5.72575e+00, tol = 1.14359e+00, K = 1, iteration = 1610, H = 2.07991e+00\n", + "log(Z): -5.72404e+00, tol = 1.14106e+00, K = 1, iteration = 1611, H = 2.07989e+00\n", + "log(Z): -5.72232e+00, tol = 1.13854e+00, K = 1, iteration = 1612, H = 2.07987e+00\n", + "log(Z): -5.72061e+00, tol = 1.13602e+00, K = 1, iteration = 1613, H = 2.07985e+00\n", + "log(Z): -5.71891e+00, tol = 1.13351e+00, K = 1, iteration = 1614, H = 2.07983e+00\n", + "log(Z): -5.71721e+00, tol = 1.13100e+00, K = 1, iteration = 1615, H = 2.07980e+00\n", + "log(Z): -5.71552e+00, tol = 1.12850e+00, K = 1, iteration = 1616, H = 2.07977e+00\n", + "log(Z): -5.71383e+00, tol = 1.12600e+00, K = 1, iteration = 1617, H = 2.07975e+00\n", + "log(Z): -5.71214e+00, tol = 1.12352e+00, K = 1, iteration = 1618, H = 2.07972e+00\n", + "log(Z): -5.71046e+00, tol = 1.12103e+00, K = 1, iteration = 1619, H = 2.07970e+00\n", + "log(Z): -5.70877e+00, tol = 1.11855e+00, K = 1, iteration = 1620, H = 2.07968e+00\n", + "log(Z): -5.70709e+00, tol = 1.11607e+00, K = 1, iteration = 1621, H = 2.07966e+00\n", + "log(Z): -5.70541e+00, tol = 1.11360e+00, K = 1, iteration = 1622, H = 2.07964e+00\n", + "log(Z): -5.70373e+00, tol = 1.11113e+00, K = 1, iteration = 1623, H = 2.07962e+00\n", + "log(Z): -5.70206e+00, tol = 1.10867e+00, K = 1, iteration = 1624, H = 2.07961e+00\n", + "log(Z): -5.70039e+00, tol = 1.10621e+00, K = 1, iteration = 1625, H = 2.07959e+00\n", + "log(Z): -5.69871e+00, tol = 1.10375e+00, K = 1, iteration = 1626, H = 2.07958e+00\n", + "log(Z): -5.69704e+00, tol = 1.10130e+00, K = 1, iteration = 1627, H = 2.07957e+00\n", + "log(Z): -5.69537e+00, tol = 1.09885e+00, K = 1, iteration = 1628, H = 2.07956e+00\n", + "log(Z): -5.69371e+00, tol = 1.09641e+00, K = 1, iteration = 1629, H = 2.07955e+00\n", + "log(Z): -5.69205e+00, tol = 1.09397e+00, K = 1, iteration = 1630, H = 2.07953e+00\n", + "log(Z): -5.69040e+00, tol = 1.09155e+00, K = 1, iteration = 1631, H = 2.07952e+00\n", + "log(Z): -5.68875e+00, tol = 1.08912e+00, K = 1, iteration = 1632, H = 2.07951e+00\n", + "log(Z): -5.68710e+00, tol = 1.08670e+00, K = 1, iteration = 1633, H = 2.07949e+00\n", + "log(Z): -5.68545e+00, tol = 1.08429e+00, K = 1, iteration = 1634, H = 2.07948e+00\n", + "log(Z): -5.68381e+00, tol = 1.08188e+00, K = 1, iteration = 1635, H = 2.07947e+00\n", + "log(Z): -5.68217e+00, tol = 1.07947e+00, K = 1, iteration = 1636, H = 2.07946e+00\n", + "log(Z): -5.68054e+00, tol = 1.07708e+00, K = 1, iteration = 1637, H = 2.07944e+00\n", + "log(Z): -5.67890e+00, tol = 1.07468e+00, K = 1, iteration = 1638, H = 2.07943e+00\n", + "log(Z): -5.67728e+00, tol = 1.07230e+00, K = 1, iteration = 1639, H = 2.07942e+00\n", + "log(Z): -5.67566e+00, tol = 1.06991e+00, K = 1, iteration = 1640, H = 2.07940e+00\n", + "log(Z): -5.67402e+00, tol = 1.06753e+00, K = 1, iteration = 1641, H = 2.07940e+00\n", + "log(Z): -5.67239e+00, tol = 1.06515e+00, K = 1, iteration = 1642, H = 2.07940e+00\n", + "log(Z): -5.67077e+00, tol = 1.06277e+00, K = 1, iteration = 1643, H = 2.07939e+00\n", + "log(Z): -5.66914e+00, tol = 1.06040e+00, K = 1, iteration = 1644, H = 2.07939e+00\n", + "log(Z): -5.66752e+00, tol = 1.05804e+00, K = 1, iteration = 1645, H = 2.07939e+00\n", + "log(Z): -5.66591e+00, tol = 1.05568e+00, K = 1, iteration = 1646, H = 2.07939e+00\n", + "log(Z): -5.66430e+00, tol = 1.05333e+00, K = 1, iteration = 1647, H = 2.07938e+00\n", + "log(Z): -5.66269e+00, tol = 1.05098e+00, K = 1, iteration = 1648, H = 2.07938e+00\n", + "log(Z): -5.66109e+00, tol = 1.04864e+00, K = 1, iteration = 1649, H = 2.07937e+00\n", + "log(Z): -5.65949e+00, tol = 1.04631e+00, K = 1, iteration = 1650, H = 2.07936e+00\n", + "log(Z): -5.65790e+00, tol = 1.04398e+00, K = 1, iteration = 1651, H = 2.07935e+00\n", + "log(Z): -5.65631e+00, tol = 1.04165e+00, K = 1, iteration = 1652, H = 2.07934e+00\n", + "log(Z): -5.65472e+00, tol = 1.03933e+00, K = 1, iteration = 1653, H = 2.07933e+00\n", + "log(Z): -5.65314e+00, tol = 1.03702e+00, K = 1, iteration = 1654, H = 2.07933e+00\n", + "log(Z): -5.65156e+00, tol = 1.03471e+00, K = 1, iteration = 1655, H = 2.07932e+00\n", + "log(Z): -5.64998e+00, tol = 1.03240e+00, K = 1, iteration = 1656, H = 2.07931e+00\n", + "log(Z): -5.64841e+00, tol = 1.03010e+00, K = 1, iteration = 1657, H = 2.07930e+00\n", + "log(Z): -5.64684e+00, tol = 1.02781e+00, K = 1, iteration = 1658, H = 2.07928e+00\n", + "log(Z): -5.64528e+00, tol = 1.02552e+00, K = 1, iteration = 1659, H = 2.07927e+00\n", + "log(Z): -5.64372e+00, tol = 1.02324e+00, K = 1, iteration = 1660, H = 2.07926e+00\n", + "log(Z): -5.64216e+00, tol = 1.02096e+00, K = 1, iteration = 1661, H = 2.07925e+00\n", + "log(Z): -5.64060e+00, tol = 1.01869e+00, K = 1, iteration = 1662, H = 2.07924e+00\n", + "log(Z): -5.63904e+00, tol = 1.01642e+00, K = 1, iteration = 1663, H = 2.07923e+00\n", + "log(Z): -5.63749e+00, tol = 1.01415e+00, K = 1, iteration = 1664, H = 2.07922e+00\n", + "log(Z): -5.63595e+00, tol = 1.01190e+00, K = 1, iteration = 1665, H = 2.07921e+00\n", + "log(Z): -5.63441e+00, tol = 1.00964e+00, K = 1, iteration = 1666, H = 2.07919e+00\n", + "log(Z): -5.63287e+00, tol = 1.00740e+00, K = 1, iteration = 1667, H = 2.07918e+00\n", + "log(Z): -5.63134e+00, tol = 1.00516e+00, K = 1, iteration = 1668, H = 2.07916e+00\n", + "log(Z): -5.62981e+00, tol = 1.00292e+00, K = 1, iteration = 1669, H = 2.07914e+00\n", + "log(Z): -5.62828e+00, tol = 1.00068e+00, K = 1, iteration = 1670, H = 2.07914e+00\n", + "log(Z): -5.62674e+00, tol = 9.98451e-01, K = 1, iteration = 1671, H = 2.07913e+00\n", + "Elapsed time is 0.445 seconds\n", + "\n", + "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "\n", + "Running DREAM\n", + "\n", + "------------------ Summary of the main settings used ------------------\n", + " nParams: 2\n", + " nChains: 10\n", + " nGenerations: 2000\n", + " parallel: false\n", + " CPU: 1\n", + " jumpProbability: 0.5\n", + " pUnitGamma: 0.2\n", + " nCR: 3\n", + " delta: 3\n", + " steps: 50\n", + " zeta: 1e-12\n", + " outlier: 'iqr'\n", + " adaptPCR: true\n", + " thinning: 1\n", + " ABC: false\n", + " epsilon: 0.025\n", + " IO: false\n", + " storeOutput: false\n", + " R: [10x10 double]\n", + " -----------------------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DREAM: 100%|██████████████████████████████████████████████████████████████████████████████" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Elapsed time is 1.201 seconds\n", + "\n", + "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "ns_controls = RAT.Controls(procedure=\"ns\", nsTolerance=1, nLive=500, display=\"final\")\n", + "_, ns_results = RAT.run(project, ns_controls)\n", + "\n", + "dream_controls = RAT.Controls(procedure=\"dream\", display=\"final\")\n", + "_, dream_results = RAT.run(project, dream_controls)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will perform our direct calculation. The standard `'calculate'` procedure in RAT runs an Abelès calculation for the reflectivity of our model, and calculates the $\\chi^2$ statistic for how well this reflectivity fits the given data. We will take a sample of 30 values between the minimum and maximum value of our roughness and background parameters, and calculate $\\exp(-\\chi^2 / 2)$ on this roughness-background grid. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "rough_param = project.parameters[0]\n", + "roughness = np.linspace(rough_param.min, rough_param.max, 30)\n", + "\n", + "back_param = project.background_parameters[0]\n", + "background = np.linspace(back_param.min, back_param.max, 30)\n", + "\n", + "controls = RAT.Controls(procedure=\"calculate\", calcSldDuringFit=True, display=\"off\")\n", + "\n", + "# function to calculate exp(-chi_squared / 2) for a given pair of roughness/background values\n", + "def calculate_posterior(roughness_index: int, background_index: int) -> float:\n", + " \"\"\"Calculate the posterior for an item in the roughness and background vectors.\n", + "\n", + " Parameters\n", + " ----------\n", + " roughness_index : int\n", + " The index of the roughness vector to use as the roughness parameter value.\n", + " background_index : int\n", + " The index of the background vector to use as the background parameter value.\n", + "\n", + " Returns\n", + " -------\n", + " float\n", + " The value of exp(-chi^2 / 2) for the given roughness and background values.\n", + " \"\"\"\n", + " project.parameters[0].value = roughness[roughness_index]\n", + " project.background_parameters[0].value = background[background_index]\n", + "\n", + " _, results = RAT.run(project, controls)\n", + " chi_squared = results.calculationResults.sumChi\n", + "\n", + " return np.exp(-chi_squared / 2)\n", + "\n", + "# we vectorise the calculation to make it faster by running it over a matrix of indices (x, y)\n", + "vectorized_calc_posterior = np.vectorize(calculate_posterior)\n", + "probability_array = vectorized_calc_posterior(*np.indices((30, 30), dtype=int))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can get the parameter values that best fit our model for each method:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best values according to direct calculation:\n", + " Roughness: 4.896551724137931 \n", + " Background: 3.16551724137931e-06\n", + "Best values according to Nested Sampler:\n", + " Roughness: 4.819791726804396 \n", + "\n", + "Best values according to DREAM:\n", + " Roughness: 4.8072205077358285 \n", + "\n" + ] + } + ], + "source": [ + "# get the vector indices that produced the lowest chi-squared\n", + "best_indices = np.unravel_index(np.argmax(probability_array, axis=None), probability_array.shape)\n", + "print(\"Best values according to direct calculation:\\n\",\n", + " \"Roughness: \", roughness[best_indices[0]], \"\\n\",\n", + " \"Background: \", background[best_indices[1]])\n", + "\n", + "print(\"Best values according to Nested Sampler:\\n\",\n", + " \"Roughness: \", ns_results.fitParams[0], \"\\n\",\n", + " ## FIXME: once fitParams outputs properly!\n", + ")# \"Background: \", ns_results.fitParams[1])\n", + "\n", + "print(\"Best values according to DREAM:\\n\",\n", + " \"Roughness: \", dream_results.fitParams[0], \"\\n\",\n", + " ## FIXME: once fitParams outputs properly!\n", + " )# \"Background: \", dream_results.fitParams[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And finally, we will plot the posteriors created via each method to compare." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2651/3011725667.py:46: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import RATapi.utils.plotting as RATplot\n", + "\n", + "fig, axes = plt.subplots(3, 2, figsize=(6, 9))\n", + "\n", + "# plot NS and DREAM for each parameter\n", + "for i in [0, 1]:\n", + " RATplot.plot_one_hist(ns_results, i, axes=axes[0][i])\n", + " RATplot.plot_one_hist(dream_results, i, axes=axes[1][i])\n", + " # we want all 3 plots to have the same x-range\n", + " # so we will use the nested sampler x-range as our base\n", + " axes[1][i].set_xlim(*axes[0][i].get_xlim())\n", + " axes[1][i].set_title(\"\")\n", + "\n", + "# marginalise the probability array to get distributions for each parameter\n", + "roughness_distribution = np.sum(probability_array, axis=1)\n", + "background_distribution = np.sum(probability_array, axis=0)\n", + "\n", + "axes[2][0].hist(\n", + " roughness,\n", + " bins=25,\n", + " range=axes[0][0].get_xlim(),\n", + " weights=roughness_distribution,\n", + " density=True,\n", + " edgecolor=\"black\",\n", + " linewidth=1.2,\n", + " color=\"white\",\n", + " )\n", + "\n", + "axes[2][1].hist(\n", + " background,\n", + " bins=25,\n", + " range=axes[0][1].get_xlim(),\n", + " weights=background_distribution,\n", + " density=True,\n", + " edgecolor=\"black\",\n", + " linewidth=1.2,\n", + " color=\"white\",\n", + " )\n", + "\n", + "axes[0][0].set_ylabel(\"nested sampler\")\n", + "axes[1][0].set_ylabel(\"DREAM\")\n", + "axes[2][0].set_ylabel(\"direct calculation\")\n", + "fig.tight_layout()\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/RATapi/examples/bayes_benchmark/bayes_benchmark.py b/RATapi/examples/bayes_benchmark/bayes_benchmark.py new file mode 100644 index 0000000..8ec6e6a --- /dev/null +++ b/RATapi/examples/bayes_benchmark/bayes_benchmark.py @@ -0,0 +1,361 @@ +""" +This example compares three Bayesian posteriors for a low-dimensional +example: a posterior generated by DREAM, one generated by NS, and +one calculated directly. + +The likelihood of the parameters being equal to a certain value is proportional +to exp(-chi^2 / 2) [1], so for a low-dimensional example we can calculate this directly +for a sample of parameter values. + +Citation: +[1] D. S. Sivia, J. R. P. Webster, + "The Bayesian approach to reflectivity data", + Physica B: Condensed Matter, + Volume 248, June 1998, pages 327-337 + DOI: 10.1016/S0921-4526(98)00259-2 + URL: https://bayes.wustl.edu/sivia/98_20feb03.pdf + +""" + +from dataclasses import dataclass +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np + +import RATapi as RAT +import RATapi.utils.plotting as RATplot +from RATapi.models import Parameter, Background, Resolution, Data, Contrast + +PWD = Path(__file__).parents[0] + + +# function to get our starting project +# this is the RasCAL-1 default project +# it is a bare D2O substrate +def get_project() -> RAT.Project: + return RAT.Project( + name="Bare D2O Substrate", + calculation="normal", + model="standard layers", + geometry="air/substrate", + absorption="False", + parameters=[Parameter(name="Substrate Roughness", min=3.0, value=4.844363132849221, max=8.0, fit=True)], + background_parameters=[ + Parameter(name="Background parameter 1", min=5e-08, value=3.069003361230152e-06, max=7e-06, fit=True) + ], + scalefactors=[Parameter(name="Scalefactor 1", min=0.07, value=0.10141560336360426, max=0.13, fit=False)], + bulk_in=[Parameter(name="Air", min=0.0, value=0.0, max=0.0, fit=False)], + bulk_out=[Parameter(name="D2O", min=6.3e-06, value=6.35e-06, max=6.4e-06, fit=False)], + resolution_parameters=[Parameter(name="Resolution parameter 1", min=0.01, value=0.03, max=0.05, fit=False)], + backgrounds=[Background(name="Background 1", type="constant", value_1="Background parameter 1")], + resolutions=[Resolution(name="Resolution 1", type="constant", value_1="Resolution parameter 1")], + data=[ + Data(name="Simulation", data=np.empty([0, 3]), simulation_range=[0.005, 0.7]), + Data( + name="f82395c", + data=np.array( + [ + [4.8866e-02, 1.2343e-04, 1.3213e-06], + [5.1309e-02, 1.0063e-04, 1.0803e-06], + [5.3874e-02, 8.2165e-05, 8.8779e-07], + [5.6568e-02, 6.4993e-05, 7.2018e-07], + [5.9396e-02, 5.3958e-05, 6.0015e-07], + [6.2366e-02, 4.3590e-05, 5.0129e-07], + [6.5485e-02, 3.5780e-05, 4.1957e-07], + [6.8759e-02, 2.9130e-05, 3.5171e-07], + [7.2197e-02, 2.3481e-05, 3.0586e-07], + [7.5807e-02, 1.8906e-05, 2.6344e-07], + [7.9597e-02, 1.4642e-05, 2.2314e-07], + [8.3577e-02, 1.1589e-05, 1.8938e-07], + [8.7756e-02, 9.5418e-06, 1.6220e-07], + [9.2143e-02, 7.5694e-06, 1.3809e-07], + [9.6751e-02, 6.3831e-06, 1.2097e-07], + [1.0159e-01, 5.0708e-06, 1.0333e-07], + [1.0667e-01, 4.1041e-06, 8.9548e-08], + [1.1200e-01, 3.4253e-06, 7.9830e-08], + [1.1760e-01, 2.8116e-06, 7.1554e-08], + [1.2348e-01, 2.3767e-06, 6.3738e-08], + [1.2966e-01, 1.9241e-06, 5.6586e-08], + [1.3614e-01, 1.5642e-06, 5.2778e-08], + [1.4294e-01, 1.2922e-06, 4.9730e-08], + [1.5009e-01, 1.1694e-06, 5.1175e-08], + [1.5760e-01, 9.7837e-07, 5.0755e-08], + [1.6548e-01, 8.9138e-07, 5.3542e-08], + [1.7375e-01, 7.9420e-07, 5.4857e-08], + [1.8244e-01, 7.9131e-07, 5.8067e-08], + [1.9156e-01, 6.5358e-07, 5.7717e-08], + [2.0114e-01, 6.2970e-07, 5.7951e-08], + [2.1119e-01, 5.0130e-07, 5.5262e-08], + [2.2175e-01, 5.0218e-07, 5.6461e-08], + [2.3284e-01, 3.9299e-07, 5.0685e-08], + [2.4448e-01, 3.5324e-07, 5.0194e-08], + [2.5671e-01, 4.4475e-07, 5.6485e-08], + [2.6954e-01, 5.1338e-07, 6.2247e-08], + [2.8302e-01, 3.4918e-07, 4.9745e-08], + [2.9717e-01, 4.3037e-07, 5.5488e-08], + [3.1203e-01, 4.0099e-07, 5.3591e-08], + [3.2763e-01, 3.8397e-07, 5.1303e-08], + [3.4401e-01, 3.0995e-07, 4.5965e-08], + [3.6121e-01, 3.9357e-07, 5.0135e-08], + [3.7927e-01, 3.0997e-07, 4.3680e-08], + [3.9824e-01, 2.9656e-07, 4.2432e-08], + [4.1815e-01, 2.1909e-07, 3.6117e-08], + [4.3906e-01, 2.3153e-07, 3.6307e-08], + [4.6101e-01, 3.3428e-07, 4.3874e-08], + [4.8406e-01, 2.3441e-07, 3.7488e-08], + [5.0826e-01, 1.5496e-07, 3.0585e-08], + [5.3368e-01, 2.4708e-07, 3.9376e-08], + [5.6036e-01, 2.2157e-07, 3.8258e-08], + [5.8838e-01, 2.2798e-07, 4.6976e-08], + [6.1169e-01, 6.0272e-07, 2.3239e-07], + ] + ), + data_range=[0.048866, 0.61169], + simulation_range=[0.048866, 0.61169], + ), + ], + contrasts=[ + Contrast( + name="Chain-d, acmw", + data="f82395c", + background="Background 1", + background_action="add", + bulk_in="Air", + bulk_out="D2O", + scalefactor="Scalefactor 1", + resolution="Resolution 1", + resample=False, + model=[], + ) + ], + ) + + +@dataclass +class CalculationResults: + """Data class for results from a direct calculation.""" + + x_data: list[np.array] + distribution: np.array + + +def bayes_benchmark_2d(grid_size: int) -> (RAT.outputs.BayesResults, CalculationResults): + """Bayes benchmark for a 2-dimensional example. + + Parameters + ---------- + grid_size : int + The number of points to sample for each fit parameter. + + Here we estimate the substrate roughness and background using two different methods: + nested sampling (the 'ns' procedure in RAT) and through a direct calculation of chi-squared + over a range of parameter values. + + Returns + ------- + RAT.BayesResults + The BayesResults object from a nested sampler calculation. + CalculationResults + Results from the direct calculation. + + """ + problem = get_project() + + ns_controls = RAT.Controls(procedure="ns", nsTolerance=1, nLive=500, display="final") + _, ns_results = RAT.run(problem, ns_controls) + + dream_controls = RAT.Controls(procedure="dream", display="final") + _, dream_results = RAT.run(problem, dream_controls) + + # now we get the parameters and use them to do a direct calculation + rough_param = problem.parameters[0] + roughness = np.linspace(rough_param.min, rough_param.max, grid_size) + + back_param = problem.background_parameters[0] + background = np.linspace(back_param.min, back_param.max, grid_size) + + controls = RAT.Controls(procedure="calculate", display="off") + + def calculate_posterior(roughness_index: int, background_index: int) -> float: + """Calculate the posterior for an item in the roughness and background vectors. + + Parameters + ---------- + roughness_index : int + The index of the roughness vector to use as the roughness parameter value. + background_index : int + The index of the background vector to use as the background parameter value. + + Returns + ------- + float + The value of exp(-chi^2 / 2) for the given roughness and background values. + """ + problem.parameters[0].value = roughness[roughness_index] + problem.background_parameters[0].value = background[background_index] + + _, results = RAT.run(problem, controls) + chi_squared = results.calculationResults.sumChi + + return np.exp(-chi_squared / 2) + + vectorized_calc_posterior = np.vectorize(calculate_posterior) + + print("Calculating posterior directly...") + probability_array = vectorized_calc_posterior(*np.indices((grid_size, grid_size), dtype=int)) + + return ns_results, dream_results, CalculationResults(x_data=[roughness, background], distribution=probability_array) + + +def bayes_benchmark_3d(grid_size: int) -> (RAT.outputs.BayesResults, CalculationResults): + """Bayes benchmark for a 3-dimensional example. + + Here we estimate the substrate roughness and background using two different methods: + nested sampling (the 'ns' procedure in RAT) and through a direct calculation of chi-squared + over a range of parameter values. + + Parameters + ---------- + grid_size : int + The number of points to sample for each fit parameter. + + Returns + ------- + RAT.BayesResults + The BayesResults object from a nested sampler calculation. + CalculationResults + Results from the direct calculation. + + """ + problem = get_project() + problem.scalefactors[0].fit = True + + ns_controls = RAT.Controls(procedure="ns", nsTolerance=1, nLive=500, display="final") + _, ns_results = RAT.run(problem, ns_controls) + + dream_controls = RAT.Controls(procedure="dream", display="final") + _, dream_results = RAT.run(problem, dream_controls) + + # now we get the parameters and use them to do a direct calculation + rough_param = problem.parameters[0] + roughness = np.linspace(rough_param.min, rough_param.max, grid_size) + + back_param = problem.background_parameters[0] + background = np.linspace(back_param.min, back_param.max, grid_size) + + scale_param = problem.scalefactors[0] + scalefactor = np.linspace(scale_param.min, scale_param.max, grid_size) + + controls = RAT.Controls(procedure="calculate", calcSldDuringFit=True, display="off") + + def calculate_posterior(roughness_index: int, background_index: int, scalefactor_index: int) -> float: + """Calculate the posterior for an item in the roughness, background, and scalefactor vectors. + + Parameters + ---------- + roughness_index : int + The index of the roughness vector to use as the roughness parameter value. + background_index : int + The index of the background vector to use as the background parameter value. + scalefactor_index : int + The index of the scalefactor vector to use as the scalefactor parameter. + + Returns + ------- + float + The value of exp(-chi^2 / 2) for the given roughness and background values. + """ + problem.parameters[0].value = roughness[roughness_index] + problem.background_parameters[0].value = background[background_index] + problem.scalefactors[0].value = scalefactor[scalefactor_index] + + _, results = RAT.run(problem, controls) + chi_squared = results.calculationResults.sumChi + + return np.exp(-chi_squared / 2) + + vectorized_calc_posterior = np.vectorize(calculate_posterior) + + print("Calculating posterior directly...") + probability_array = vectorized_calc_posterior(*np.indices((grid_size, grid_size, grid_size), dtype=int)) + + return ( + ns_results, + dream_results, + CalculationResults(x_data=[roughness, background, scalefactor], distribution=probability_array), + ) + + +def plot_posterior_comparison( + ns_results: RAT.outputs.BayesResults, dream_results: RAT.outputs.BayesResults, calc_results: CalculationResults +): + """Create a grid of marginalised posteriors comparing different calculation methods. + + Parameters + ---------- + ns_results : RAT.BayesResults + The BayesResults object from a nested sampler calculation. + dream_results : RAT.BayesResults + The BayesResults object from a DREAM calculation. + calc_results : CalculationResults + The results from a direct calculation. + """ + num_params = calc_results.distribution.ndim + fig, axes = plt.subplots(3, num_params, figsize=(3 * num_params, 9)) + + def plot_marginalised_result(dimension: int, axes: plt.Axes, limits: tuple[float]): + """Plot a histogram of a marginalised posterior from the calculation results. + + Parameters + ---------- + dimension : int + The dimension of the array to marginalise over. + axes : plt.Axes + The Axes object to plot the histogram onto. + limits : tuple[float] + The x-axis limits for the histogram. + + """ + # marginalise to the dimension + # note we don't need to normalise here as np.histogram normalises for us + sum_axes = tuple(i for i in range(0, num_params) if i != dimension) + distribution = np.sum(calc_results.distribution, axis=sum_axes) + distribution /= np.sum(calc_results.distribution) + + # create histogram + axes.hist( + calc_results.x_data[i], + bins=25, + range=limits, + weights=distribution, + density=True, + edgecolor="black", + linewidth=1.2, + color="white", + ) + + # row 0 contains NS histograms for each parameter + # row 1 contains direct calculation histograms for each parameter + for i in range(0, num_params): + RATplot.plot_one_hist(ns_results, i, axes=axes[0][i]) + RATplot.plot_one_hist(dream_results, i, axes=axes[1][i]) + # we want all 3 plots to have the same x-axis + axes[1][i].set_xlim(*axes[0][i].get_xlim()) + axes[1][i].set_title("") + plot_marginalised_result(i, axes[2][i], limits=axes[0][i].get_xlim()) + + axes[0][0].set_ylabel("nested sampler") + axes[1][0].set_ylabel("DREAM") + axes[2][0].set_ylabel("direct calculation") + + fig.tight_layout() + fig.show() + + +if __name__ == "__main__": + ns_2d, dream_2d, calc_2d = bayes_benchmark_2d(30) + ns_3d, dream_3d, calc_3d = bayes_benchmark_3d(40) + + plot_posterior_comparison(ns_2d, dream_2d, calc_2d) + plot_posterior_comparison(ns_3d, dream_3d, calc_3d)