From 1f1badf3246deca448c8effce622a7f59962ba95 Mon Sep 17 00:00:00 2001 From: Peter Sharpe Date: Tue, 15 Aug 2023 10:51:53 -0400 Subject: [PATCH] Remove section on a CasADi bug that has now been fixed. Thanks to @mcleantom for catching this! Also, added more clarity on softmax and improved plotting. --- .../06 - Tricky Functions - NaNs.ipynb | 513 ++++++------------ 1 file changed, 152 insertions(+), 361 deletions(-) diff --git a/tutorial/01 - Optimization and Math/06 - Tricky Functions - NaNs.ipynb b/tutorial/01 - Optimization and Math/06 - Tricky Functions - NaNs.ipynb index 21fd2ded0..ba2d35a1d 100644 --- a/tutorial/01 - Optimization and Math/06 - Tricky Functions - NaNs.ipynb +++ b/tutorial/01 - Optimization and Math/06 - Tricky Functions - NaNs.ipynb @@ -48,8 +48,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "This is Ipopt version 3.12.3, running with linear solver mumps.\n", - "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n", + "This is Ipopt version 3.14.11, running with linear solver MUMPS 5.4.1.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 0\n", "Number of nonzeros in inequality constraint Jacobian.: 0\n", @@ -77,15 +76,14 @@ "Number of equality constraint Jacobian evaluations = 0\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 0\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", + "Total seconds in IPOPT = 0.000\n", "\n", "EXIT: Invalid number in NLP function or derivative detected.\n", " solver : t_proc (avg) t_wall (avg) n_eval\n", - " nlp_grad_f | 0 ( 0) 0 ( 0) 2\n", - " total | 1.00ms ( 1.00ms) 997.00us (997.00us) 1\n", - "Error in Opti::solve [OptiNode] at .../casadi/core/optistack.cpp:159:\n", - ".../casadi/core/optistack_internal.cpp:999: Assertion \"return_success(accept_limit)\" failed:\n", + " nlp_grad_f | 0 ( 0) 99.00us ( 49.50us) 2\n", + " total | 0 ( 0) 653.00us (653.00us) 1\n", + "Error in Opti::solve [OptiNode] at .../casadi/core/optistack.cpp:157:\n", + ".../casadi/core/optistack_internal.cpp:998: Assertion \"return_success(accept_limit)\" failed:\n", "Solver failed. You may use opti.debug.value to investigate the latest values of variables. return_status is 'Invalid_Number_Detected'\n" ] }, @@ -93,8 +91,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "CasADi - WARNING(\"solver:nlp_grad_f failed: NaN detected for output grad_f_x, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_grad_f failed: NaN detected for output grad_f_x, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n" + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_grad_f failed: NaN detected for output grad_f_x, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_grad_f failed: NaN detected for output grad_f_x, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n" ] } ], @@ -117,6 +115,10 @@ "collapsed": false, "pycharm": { "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2023-08-15T14:48:44.161903500Z", + "start_time": "2023-08-15T14:48:43.110786900Z" } } }, @@ -146,8 +148,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "This is Ipopt version 3.12.3, running with linear solver mumps.\n", - "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n", + "This is Ipopt version 3.14.11, running with linear solver MUMPS 5.4.1.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 0\n", "Number of nonzeros in inequality constraint Jacobian.: 1\n", @@ -164,137 +165,128 @@ " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 1.0000000e+000 0.00e+000 2.50e-001 0.0 0.00e+000 - 0.00e+000 0.00e+000 0\n", - " 1 1.8447703e+000 0.00e+000 3.72e-001 0.4 2.40e+000 - 8.99e-001 1.00e+000f 1\n", - " 2 1.8080897e+000 0.00e+000 1.34e-001 -1.6 1.34e-001 0.0 1.00e+000 1.00e+000f 1\n", - " 3 1.6823475e+000 0.00e+000 1.48e-001 -2.3 4.39e-001 -0.5 1.00e+000 1.00e+000f 1\n", - " 4 1.0476193e+000 0.00e+000 2.82e-001 -2.3 1.73e+000 -1.0 1.00e+000 1.00e+000f 1\n", - " 5 1.0476188e-001 0.00e+000 4.42e+000 -2.3 1.25e+000 -0.5 1.00e+000 8.68e-001f 1\n", - " 6 5.4016180e-002 0.00e+000 5.99e+000 -10.1 8.06e-003 2.6 1.00e+000 1.00e+000f 1\n", - " 7 5.4007015e-003 0.00e+000 8.68e+001 -10.8 1.61e-002 3.0 1.00e+000 1.79e-001f 1\n", - " 8 5.3082556e-004 0.00e+000 8.96e+002 -11.0 6.81e-005 6.2 1.00e+000 4.24e-001f 1\n", + " 0 1.0000000e+00 0.00e+00 2.50e-01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 1.8447703e+00 0.00e+00 3.72e-01 0.4 2.40e+00 - 8.99e-01 1.00e+00f 1\n", + " 2 1.8080897e+00 0.00e+00 1.34e-01 -1.6 1.34e-01 0.0 1.00e+00 1.00e+00f 1\n", + " 3 1.6823475e+00 0.00e+00 1.48e-01 -2.3 4.39e-01 -0.5 1.00e+00 1.00e+00f 1\n", + " 4 1.0476193e+00 0.00e+00 2.82e-01 -2.3 1.73e+00 -1.0 1.00e+00 1.00e+00f 1\n", + " 5 1.0476188e-01 0.00e+00 4.42e+00 -2.3 1.25e+00 -0.5 1.00e+00 8.68e-01f 1\n", + " 6 5.4016180e-02 0.00e+00 5.99e+00 -10.1 8.06e-03 2.6 1.00e+00 1.00e+00f 1\n", + " 7 5.4007015e-03 0.00e+00 8.68e+01 -10.8 1.61e-02 3.0 1.00e+00 1.79e-01f 1\n", + " 8 5.3082556e-04 0.00e+00 8.96e+02 -11.0 6.81e-05 6.2 1.00e+00 4.24e-01f 1\n", "Warning: Cutting back alpha due to evaluation error\n", - " 9 3.7060324e-004 0.00e+000 1.03e+003 -11.0 4.01e-007 9.3 1.00e+000 3.60e-001f 2\n", + " 9 3.7060324e-04 0.00e+00 1.03e+03 -11.0 4.01e-07 9.3 1.00e+00 3.60e-01f 2\n", "Warning: Cutting back alpha due to evaluation error\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 2.5379148e-004 0.00e+000 1.36e+003 -11.0 2.32e-007 9.7 1.00e+000 3.14e-001f 2\n", + " 10 2.5379148e-04 0.00e+00 1.36e+03 -11.0 2.32e-07 9.7 1.00e+00 3.14e-01f 2\n", "Warning: Cutting back alpha due to evaluation error\n", - " 11 1.6606357e-004 0.00e+000 2.03e+003 -11.0 1.48e-007 10.2 1.00e+000 2.49e-001f 2\n", + " 11 1.6606357e-04 0.00e+00 2.03e+03 -11.0 1.48e-07 10.2 1.00e+00 2.49e-01f 2\n", "Warning: Cutting back alpha due to evaluation error\n", - " 12 9.4744076e-005 0.00e+000 3.71e+003 -11.0 1.39e-007 10.6 1.00e+000 1.34e-001f 2\n", - " 13 7.0837365e-005 0.00e+000 3.84e+003 -11.0 3.96e-009 11.9 1.00e+000 1.00e+000f 1\n", - " 14 5.5465578e-005 0.00e+000 4.80e+003 -11.0 1.94e-009 12.3 1.00e+000 1.00e+000f 1\n", - " 15 4.6737050e-005 0.00e+000 5.54e+003 -11.0 8.92e-010 12.8 1.00e+000 1.00e+000f 1\n", + " 12 9.4744076e-05 0.00e+00 3.71e+03 -11.0 1.39e-07 10.6 1.00e+00 1.34e-01f 2\n", + " 13 7.0837365e-05 0.00e+00 3.84e+03 -11.0 3.96e-09 11.9 1.00e+00 1.00e+00f 1\n", + " 14 5.5465578e-05 0.00e+00 4.80e+03 -11.0 1.94e-09 12.3 1.00e+00 1.00e+00f 1\n", + " 15 4.6737050e-05 0.00e+00 5.54e+03 -11.0 8.92e-10 12.8 1.00e+00 1.00e+00f 1\n", "Warning: Cutting back alpha due to evaluation error\n", "Warning: Cutting back alpha due to evaluation error\n", - " 16 1.6874305e-005 0.00e+000 2.21e+004 -10.5 7.60e-009 12.3 1.00e+000 2.50e-001f 3\n", + " 16 1.6874305e-05 0.00e+00 2.21e+04 -10.5 7.60e-09 12.3 1.00e+00 2.50e-01f 3\n", "Warning: Cutting back alpha due to evaluation error\n", "Warning: Cutting back alpha due to evaluation error\n", - " 17 6.3111246e-006 0.00e+000 6.35e+004 -11.0 9.80e-010 13.6 1.00e+000 2.50e-001f 3\n", + " 17 6.3111240e-06 0.00e+00 6.35e+04 -11.0 9.80e-10 13.6 1.00e+00 2.50e-01f 3\n", "Warning: Cutting back alpha due to evaluation error\n", "Warning: Cutting back alpha due to evaluation error\n", - " 18 3.7140253e-006 0.00e+000 1.00e+005 -11.0 1.04e-010 14.9 1.00e+000 2.50e-001f 3\n", - " 19 3.1080572e-006 0.00e+000 8.35e+004 -11.0 4.13e-012 16.3 1.00e+000 1.00e+000f 1\n", + " 18 3.7140225e-06 0.00e+00 1.00e+05 -11.0 1.04e-10 14.9 1.00e+00 2.50e-01f 3\n", + " 19 3.1080531e-06 0.00e+00 8.35e+04 -11.0 4.13e-12 16.3 1.00e+00 1.00e+00f 1\n", "Warning: Cutting back alpha due to evaluation error\n", "Warning: Cutting back alpha due to evaluation error\n", "Warning: Cutting back alpha due to evaluation error\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 2.1955435e-006 0.00e+000 1.30e+005 -11.0 3.87e-011 15.8 1.00e+000 1.25e-001f 4\n", - "Warning: Cutting back alpha due to evaluation error\n", - "Warning: Cutting back alpha due to evaluation error\n", - "Warning: Cutting back alpha due to evaluation error\n", - " 21 1.3691013e-006 0.00e+000 2.30e+005 -11.0 2.36e-011 16.2 1.00e+000 1.25e-001f 4\n", - " 22 1.1306383e-006 0.00e+000 2.31e+005 -11.0 5.96e-013 17.6 1.00e+000 1.00e+000f 1\n", - "Warning: Cutting back alpha due to evaluation error\n", + " 20 2.1955276e-06 0.00e+00 1.30e+05 -11.0 3.87e-11 15.8 1.00e+00 1.25e-01f 4\n", "Warning: Cutting back alpha due to evaluation error\n", "Warning: Cutting back alpha due to evaluation error\n", - " 23 6.4098033e-007 0.00e+000 4.92e+005 -11.0 6.94e-012 17.1 1.00e+000 1.25e-001f 4\n", - " 24 4.6968502e-007 0.00e+000 5.84e+005 -11.0 1.90e-013 18.4 1.00e+000 1.00e+000f 1\n", - " 25 3.5254175e-007 0.00e+000 7.70e+005 -11.0 9.63e-014 18.8 1.00e+000 1.00e+000f 1\n", - " 26 2.7807395e-007 0.00e+000 9.55e+005 -11.0 4.70e-014 19.3 1.00e+000 1.00e+000f 1\n", "Warning: Cutting back alpha due to evaluation error\n", + " 21 1.3690108e-06 0.00e+00 2.31e+05 -11.0 2.36e-11 16.2 1.00e+00 1.25e-01f 4\n", + " 22 1.1305030e-06 0.00e+00 2.31e+05 -11.0 5.96e-13 17.6 1.00e+00 1.00e+00f 1\n", "Warning: Cutting back alpha due to evaluation error\n", "Warning: Cutting back alpha due to evaluation error\n", "Warning: Cutting back alpha due to evaluation error\n", + " 23 6.3999928e-07 0.00e+00 4.93e+05 -11.0 6.95e-12 17.1 1.00e+00 1.25e-01f 4\n", + " 24 4.6781648e-07 0.00e+00 5.87e+05 -11.0 1.91e-13 18.4 1.00e+00 1.00e+00f 1\n", + " 25 3.4913404e-07 0.00e+00 7.79e+05 -11.0 9.70e-14 18.8 1.00e+00 1.00e+00f 1\n", + " 26 2.7241381e-07 0.00e+00 9.79e+05 -11.0 4.77e-14 19.3 1.00e+00 1.00e+00f 1\n", + " 27 2.2846821e-07 0.00e+00 1.13e+06 -11.0 2.20e-14 19.7 1.00e+00 1.00e+00f 1\n", "Warning: Cutting back alpha due to evaluation error\n", "Warning: Cutting back alpha due to evaluation error\n", - "Warning: Cutting back alpha due to evaluation error\n", - " 27 1.9047087e-007 0.00e+000 1.54e+006 -11.0 5.25e-012 18.8 1.00e+000 7.81e-003f 8\n", - " 28 1.1001631e-007 0.00e+000 3.46e+006 -11.0 2.42e-014 20.0 1.00e+000 1.00e+000f 1\n", + " 28 4.7434993e-08 0.00e+00 8.95e+06 -11.0 2.00e-13 19.2 1.00e+00 2.50e-01f 3\n", "WARNING: Problem in step computation; switching to emergency mode.\n", - "Restoration phase is called at point that is almost feasible,\n", - " with constraint violation 1.335396e-014. Abort.\n", + "Cannot call restoration phase at point that is almost feasible (violation 0.000000e+00).\n", + "Abort in line search due to no other fall back.\n", "\n", "Number of Iterations....: 28\n", "\n", " (scaled) (unscaled)\n", - "Objective...............: 1.1001630983884095e-007 1.1001630983884095e-007\n", - "Dual infeasibility......: 3.4626205448061009e+006 3.4626205448061009e+006\n", - "Constraint violation....: 0.0000000000000000e+000 0.0000000000000000e+000\n", - "Complementarity.........: 1.0000000005629319e-011 1.0000000005629319e-011\n", - "Overall NLP error.......: 6.3994605099958937e+002 3.4626205448061009e+006\n", + "Objective...............: 4.7434993341754794e-08 4.7434993341754794e-08\n", + "Dual infeasibility......: 8.9532733337609135e+06 8.9532733337609135e+06\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Variable bound violation: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 1.0000149841154756e-11 1.0000149841154756e-11\n", + "Overall NLP error.......: 1.1279941052968204e+03 8.9532733337609135e+06\n", "\n", "\n", - "Number of objective function evaluations = 55\n", + "Number of objective function evaluations = 50\n", "Number of objective gradient evaluations = 29\n", "Number of equality constraint evaluations = 0\n", - "Number of inequality constraint evaluations = 55\n", + "Number of inequality constraint evaluations = 50\n", "Number of equality constraint Jacobian evaluations = 0\n", "Number of inequality constraint Jacobian evaluations = 29\n", "Number of Lagrangian Hessian evaluations = 29\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.017\n", - "Total CPU secs in NLP function evaluations = 0.012\n", + "Total seconds in IPOPT = 0.016\n", "\n", - "EXIT: Restoration Failed!\n", + "EXIT: Error in step computation!\n", " solver : t_proc (avg) t_wall (avg) n_eval\n", - " nlp_f | 12.00ms (218.18us) 11.91ms (216.56us) 55\n", - " nlp_g | 0 ( 0) 0 ( 0) 55\n", - " nlp_grad_f | 0 ( 0) 0 ( 0) 30\n", - " nlp_hess_l | 0 ( 0) 0 ( 0) 29\n", - " nlp_jac_g | 0 ( 0) 0 ( 0) 30\n", - " total | 30.00ms ( 30.00ms) 29.63ms ( 29.63ms) 1\n", - "Error in Opti::solve [OptiNode] at .../casadi/core/optistack.cpp:159:\n", - ".../casadi/core/optistack_internal.cpp:999: Assertion \"return_success(accept_limit)\" failed:\n", - "Solver failed. You may use opti.debug.value to investigate the latest values of variables. return_status is 'Restoration_Failed'\n" + " nlp_f | 0 ( 0) 760.00us ( 15.20us) 50\n", + " nlp_g | 0 ( 0) 38.00us (760.00ns) 50\n", + " nlp_grad_f | 0 ( 0) 18.00us (600.00ns) 30\n", + " nlp_hess_l | 0 ( 0) 20.00us (689.66ns) 29\n", + " nlp_jac_g | 0 ( 0) 11.00us (366.67ns) 30\n", + " total | 15.00ms ( 15.00ms) 16.11ms ( 16.11ms) 1\n", + "Error in Opti::solve [OptiNode] at .../casadi/core/optistack.cpp:157:\n", + ".../casadi/core/optistack_internal.cpp:998: Assertion \"return_success(accept_limit)\" failed:\n", + "Solver failed. You may use opti.debug.value to investigate the latest values of variables. return_status is 'Error_In_Step_Computation'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n" + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n", + "CasADi - 2023-08-15 10:48:44 WARNING(\"solver:nlp_f failed: NaN detected for output f, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:326]\n" ] } ], "source": [ "opti = asb.Opti()\n", "\n", - "x = opti.variable(init_guess=1, lower_bound=0) # `lower_bound` is a shorthand for writing a bounds constraint on a variable at declaration.\n", + "x = opti.variable(init_guess=1,\n", + " lower_bound=0) # `lower_bound` is a shorthand for writing a bounds constraint on a variable at declaration.\n", "\n", "opti.minimize(np.sqrt(x))\n", "\n", @@ -307,6 +299,10 @@ "collapsed": false, "pycharm": { "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2023-08-15T14:48:44.241721800Z", + "start_time": "2023-08-15T14:48:44.161903500Z" } } }, @@ -336,7 +332,7 @@ { "data": { "text/plain": "
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" }, "metadata": {}, "output_type": "display_data" @@ -344,31 +340,34 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "sns.set(palette=sns.color_palette(\"husl\"))\n", - "fig, ax = plt.subplots(1, 1, figsize=(6.4, 4.8), dpi=200)\n", - "x = np.hstack([np.linspace(-3, 0, 200), np.linspace(0, 5, 200)])\n", - "plt.plot(x[x>=0], np.sqrt(x[x>=0]), label=r\"$\\sqrt{x}$\", zorder=4)\n", + "import aerosandbox.tools.pretty_plots as p\n", + "\n", + "fig, ax = plt.subplots(figsize=(6.4, 4.8), dpi=200)\n", + "x = np.linspace(-5, 5, 1001)\n", + "plt.plot(x[x >= 0], np.sqrt(x[x >= 0]), \"k\", label=r\"$\\sqrt{x}$\", zorder=4, alpha=0.7)\n", "for k in [1, 2, 4, 8, 16]:\n", - " plt.plot(x, np.softmax(0, x, hardness=k)**0.5, label=fr\"Softmax with $k = {k}$\")\n", - "plt.xlabel(r\"$x$\")\n", - "plt.ylabel(r\"$f(x)$\")\n", - "plt.title(r\"Approximations for $\\sqrt{x}$\")\n", - "plt.tight_layout()\n", - "plt.legend()\n", - "plt.show()\n" + " plt.plot(x, np.softmax(0, x, hardness=k) ** 0.5, label=fr\"Softmax with $k = {k}$\")\n", + "p.show_plot(\n", + " r\"Approximations for $\\sqrt{x}$\",\n", + " r\"$x$\",\n", + " r\"$f(x)$\",\n", + ")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2023-08-15T14:48:45.411202600Z", + "start_time": "2023-08-15T14:48:44.190939300Z" } } }, { "cell_type": "markdown", "source": [ - "So for any positive value of `k`, we can get an arbitrarily accurate approximation of $\\sqrt x$ that has finite gradients everywhere. We could swap this into our optimization problem and things would solve just fine (provided we keep our $x > 0$ constraint to keep the problem bounded).\n", + "So for any positive value of hardness $k$, we can get an arbitrarily accurate approximation of $\\sqrt x$ that has finite gradients everywhere. We could swap this into our optimization problem and things would solve just fine (provided we keep our $x > 0$ constraint to keep the problem bounded).\n", "\n", "Let's look at another example.\n", "\n", @@ -382,14 +381,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "This is Ipopt version 3.12.3, running with linear solver mumps.\n", - "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n", + "This is Ipopt version 3.14.11, running with linear solver MUMPS 5.4.1.\n", "\n", "Number of nonzeros in equality constraint Jacobian...: 0\n", "Number of nonzeros in inequality constraint Jacobian.: 1\n", @@ -406,40 +404,41 @@ " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 1.0000000e+000 0.00e+000 2.50e-001 0.0 0.00e+000 - 0.00e+000 0.00e+000 0\n", - " 1 5.3995246e-002 0.00e+000 2.90e-001 -6.0 8.57e-001 - 1.00e+000 1.00e+000f 1\n", - " 2 1.9259237e-002 0.00e+000 2.40e-002 -6.9 7.10e-002 - 1.00e+000 1.00e+000f 1\n", - " 3 4.1395792e-003 0.00e+000 3.23e-002 -7.5 4.61e-002 - 1.00e+000 1.00e+000f 1\n", - " 4 1.0007938e-003 0.00e+000 1.71e-002 -8.2 1.58e-002 - 1.00e+000 1.00e+000f 1\n", - " 5 2.3485830e-004 0.00e+000 1.10e-002 -8.8 6.20e-003 - 1.00e+000 1.00e+000f 1\n", - " 6 5.5524788e-005 0.00e+000 6.74e-003 -9.4 2.35e-003 - 1.00e+000 1.00e+000f 1\n", - " 7 1.3102653e-005 0.00e+000 4.18e-003 -10.0 9.00e-004 - 1.00e+000 1.00e+000f 1\n", - " 8 3.0933251e-006 0.00e+000 2.58e-003 -10.7 3.43e-004 - 1.00e+000 1.00e+000f 1\n", - " 9 7.3016884e-007 0.00e+000 1.59e-003 -11.0 1.31e-004 - 1.00e+000 1.00e+000f 1\n", + " 0 1.0000000e+00 0.00e+00 2.50e-01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 5.3995246e-02 0.00e+00 2.90e-01 -6.0 8.57e-01 - 1.00e+00 1.00e+00f 1\n", + " 2 1.9259237e-02 0.00e+00 2.40e-02 -6.9 7.10e-02 - 1.00e+00 1.00e+00f 1\n", + " 3 4.1395792e-03 0.00e+00 3.23e-02 -7.5 4.61e-02 - 1.00e+00 1.00e+00f 1\n", + " 4 1.0007938e-03 0.00e+00 1.71e-02 -8.2 1.58e-02 - 1.00e+00 1.00e+00f 1\n", + " 5 2.3485830e-04 0.00e+00 1.10e-02 -8.8 6.20e-03 - 1.00e+00 1.00e+00f 1\n", + " 6 5.5524788e-05 0.00e+00 6.74e-03 -9.4 2.35e-03 - 1.00e+00 1.00e+00f 1\n", + " 7 1.3102653e-05 0.00e+00 4.18e-03 -10.0 9.00e-04 - 1.00e+00 1.00e+00f 1\n", + " 8 3.0933251e-06 0.00e+00 2.58e-03 -10.7 3.43e-04 - 1.00e+00 1.00e+00f 1\n", + " 9 7.3016884e-07 0.00e+00 1.59e-03 -11.0 1.31e-04 - 1.00e+00 1.00e+00f 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 1.7234156e-007 0.00e+000 9.86e-004 -11.0 5.01e-005 - 1.00e+000 1.00e+000f 1\n", - " 11 4.0667762e-008 0.00e+000 6.09e-004 -11.0 1.91e-005 - 1.00e+000 1.00e+000f 1\n", - " 12 9.5915429e-009 0.00e+000 3.77e-004 -11.0 7.31e-006 - 1.00e+000 1.00e+000f 1\n", - " 13 2.2601887e-009 0.00e+000 2.33e-004 -11.0 2.79e-006 - 1.00e+000 1.00e+000f 1\n", - " 14 5.3242492e-010 0.00e+000 1.44e-004 -11.0 1.07e-006 - 1.00e+000 1.00e+000f 1\n", - " 15 1.2636353e-010 0.00e+000 8.82e-005 -11.0 4.05e-007 - 1.00e+000 1.00e+000f 1\n", - " 16 1.1378662e-010 0.00e+000 4.44e-007 -9.8 1.70e-008 - 1.00e+000 1.00e+000f 1\n", - " 17 2.3909507e-011 0.00e+000 5.98e-005 -11.0 1.52e-007 - 1.00e+000 1.00e+000f 1\n", - " 18 2.1826491e-011 0.00e+000 1.94e-007 -10.4 4.89e-009 - 1.00e+000 1.00e+000f 1\n", - " 19 7.1273778e-012 0.00e+000 2.03e-005 -11.0 4.11e-008 - 1.00e+000 1.00e+000f 1\n", + " 10 1.7234156e-07 0.00e+00 9.86e-04 -11.0 5.01e-05 - 1.00e+00 1.00e+00f 1\n", + " 11 4.0667762e-08 0.00e+00 6.09e-04 -11.0 1.91e-05 - 1.00e+00 1.00e+00f 1\n", + " 12 9.5915429e-09 0.00e+00 3.77e-04 -11.0 7.31e-06 - 1.00e+00 1.00e+00f 1\n", + " 13 2.2601887e-09 0.00e+00 2.33e-04 -11.0 2.79e-06 - 1.00e+00 1.00e+00f 1\n", + " 14 5.3242492e-10 0.00e+00 1.44e-04 -11.0 1.07e-06 - 1.00e+00 1.00e+00f 1\n", + " 15 1.2636353e-10 0.00e+00 8.82e-05 -11.0 4.05e-07 - 1.00e+00 1.00e+00f 1\n", + " 16 1.1378662e-10 0.00e+00 4.44e-07 -9.8 1.70e-08 - 1.00e+00 1.00e+00f 1\n", + " 17 2.3909507e-11 0.00e+00 5.98e-05 -11.0 1.52e-07 - 1.00e+00 1.00e+00f 1\n", + " 18 2.1826491e-11 0.00e+00 1.94e-07 -10.4 4.89e-09 - 1.00e+00 1.00e+00f 1\n", + " 19 7.1273778e-12 0.00e+00 2.03e-05 -11.0 4.11e-08 - 1.00e+00 1.00e+00f 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 6.0146079e-012 0.00e+000 4.37e-007 -10.9 3.96e-009 - 1.00e+000 1.00e+000f 1\n", - " 21 4.9876017e-012 0.00e+000 4.99e-007 -11.0 3.88e-009 - 1.00e+000 1.00e+000f 1\n", - " 22 4.9611824e-012 0.00e+000 4.01e-010 -11.0 1.03e-010 - 1.00e+000 1.00e+000f 1\n", + " 20 6.0146079e-12 0.00e+00 4.37e-07 -10.9 3.96e-09 - 1.00e+00 1.00e+00f 1\n", + " 21 4.9876017e-12 0.00e+00 4.99e-07 -11.0 3.88e-09 - 1.00e+00 1.00e+00f 1\n", + " 22 4.9611824e-12 0.00e+00 4.01e-10 -11.0 1.03e-10 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 22\n", "\n", " (scaled) (unscaled)\n", - "Objective...............: 4.9611824145824379e-012 4.9611824145824379e-012\n", - "Dual infeasibility......: 4.0090739251595273e-010 4.0090739251595273e-010\n", - "Constraint violation....: 0.0000000000000000e+000 0.0000000000000000e+000\n", - "Complementarity.........: 1.0000098247274144e-011 1.0000098247274144e-011\n", - "Overall NLP error.......: 4.0090739251595273e-010 4.0090739251595273e-010\n", + "Objective...............: 4.9611824145824379e-12 4.9611824145824379e-12\n", + "Dual infeasibility......: 4.0090739257016283e-10 4.0090739257016283e-10\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Variable bound violation: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 1.0000098247274144e-11 1.0000098247274144e-11\n", + "Overall NLP error.......: 4.0090739257016283e-10 4.0090739257016283e-10\n", "\n", "\n", "Number of objective function evaluations = 23\n", @@ -449,17 +448,16 @@ "Number of equality constraint Jacobian evaluations = 0\n", "Number of inequality constraint Jacobian evaluations = 23\n", "Number of Lagrangian Hessian evaluations = 22\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.008\n", - "Total CPU secs in NLP function evaluations = 0.000\n", + "Total seconds in IPOPT = 0.008\n", "\n", "EXIT: Optimal Solution Found.\n", " solver : t_proc (avg) t_wall (avg) n_eval\n", - " nlp_f | 0 ( 0) 0 ( 0) 23\n", - " nlp_g | 0 ( 0) 0 ( 0) 23\n", - " nlp_grad_f | 0 ( 0) 0 ( 0) 24\n", - " nlp_hess_l | 0 ( 0) 0 ( 0) 22\n", - " nlp_jac_g | 0 ( 0) 0 ( 0) 24\n", - " total | 8.00ms ( 8.00ms) 7.98ms ( 7.98ms) 1\n" + " nlp_f | 0 ( 0) 21.00us (913.04ns) 23\n", + " nlp_g | 0 ( 0) 20.00us (869.57ns) 23\n", + " nlp_grad_f | 0 ( 0) 12.00us (500.00ns) 24\n", + " nlp_hess_l | 0 ( 0) 13.00us (590.91ns) 22\n", + " nlp_jac_g | 0 ( 0) 9.00us (375.00ns) 24\n", + " total | 16.00ms ( 16.00ms) 8.31ms ( 8.31ms) 1\n" ] } ], @@ -476,233 +474,26 @@ "collapsed": false, "pycharm": { "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "This solves - nice!\n", - "\n", - "One thing to know on this note: IPOPT does something it calls \"bounds pushing\" - this is detailed in the excellent original IPOPT paper by Andreas Waechter, but the basic idea is that IPOPT solver performance seems to improve if the feasible space is extended by a small amount ($\\epsilon \\approx 10^{-8}$). In cases like this, that has the potential to allow a function like $x^{1.5}$ bounded by $x>0$ to go very slightly negative and NaN. IPOPT has lots of tools to help get the iterate back into the well-posed design space, but just be aware of this possibility.\n", - "\n", - "## NaNs in Conditionals (`np.where`)\n", - "\n", - "Let's talk about one last possibility, and an issue that can come up with conditionals.\n", - "\n", - "Imagine we have the problem:\n", - "\n", - "* Minimize $f(x)$, where\n", - "* $f(x) = \\begin{cases}\n", - "x^{1.5} & \\text{for } x\\geq 0\\\\\n", - "(-x)^{1.5} & \\text{for } x < 0 \\\\\n", - "\\end{cases}\n", - "$\n", - "\n", - "Note that $f(x)$ is a $C_1$-continuous function - both its value and derivative are continuous everywhere. Also, note that $f(x)$ should never return NaN for any input value $x$.\n", - "\n", - "Let's see what happens when we try to solve this:" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is Ipopt version 3.12.3, running with linear solver mumps.\n", - "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 0\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1\n", - "\n", - "Error evaluating objective gradient at user provided starting point.\n", - " No scaling factor for objective function computed!\n", - "Total number of variables............................: 1\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 0\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "\n", - "Number of Iterations....: 0\n", - "\n", - "Number of objective function evaluations = 0\n", - "Number of objective gradient evaluations = 1\n", - "Number of equality constraint evaluations = 0\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 0\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 0\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Invalid number in NLP function or derivative detected.\n", - " solver : t_proc (avg) t_wall (avg) n_eval\n", - " nlp_grad_f | 0 ( 0) 0 ( 0) 2\n", - " total | 0 ( 0) 0 ( 0) 1\n", - "Error in Opti::solve [OptiNode] at .../casadi/core/optistack.cpp:159:\n", - ".../casadi/core/optistack_internal.cpp:999: Assertion \"return_success(accept_limit)\" failed:\n", - "Solver failed. You may use opti.debug.value to investigate the latest values of variables. return_status is 'Invalid_Number_Detected'\n" - ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "CasADi - WARNING(\"solver:nlp_grad_f failed: NaN detected for output grad_f_x, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n", - "CasADi - WARNING(\"solver:nlp_grad_f failed: NaN detected for output grad_f_x, at (row 0, col 0).\") [.../casadi/core/oracle_function.cpp:265]\n" - ] - } - ], - "source": [ - "opti = asb.Opti()\n", - "\n", - "x = opti.variable(init_guess=1)\n", - "\n", - "f = np.where(\n", - " x >= 0,\n", - " x ** 1.5,\n", - " (-x) ** 1.5\n", - ")\n", - "\n", - "opti.minimize(f)\n", - "\n", - "try:\n", - " sol = opti.solve()\n", - "except RuntimeError as e:\n", - " print(e)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" + "ExecuteTime": { + "end_time": "2023-08-15T14:49:35.072485300Z", + "start_time": "2023-08-15T14:49:35.040984900Z" } } }, { "cell_type": "markdown", "source": [ - "This errored too! Why?\n", - "\n", - "Because the *intermediate values* of $x^{1.5}$ in `np.where()` are NaN. In the current functionality of `np.where()`, if either value is NaN, the result is NaN (regardless of the value of `condition`). Let's look at the inputs to `np.where()` at the initial point $x=1$:\n", - "* The `condition`, `x >= 0`, evaluates to `True`.\n", - "* The `value_if_true`, `x ** 1.5` evaluates to $1^{1.5}=1$.\n", - "* The `value_if_false`, `(-x) ** 1.5` evaluates to $(-1)^{1.5}=\\text{NaN}$ (remember, complex math isn't supported on optimization variables).\n", - "\n", - "So, *even though* the condition is `True`, the function `np.where()` as a whole will return `NaN` because of the `NaN` in the `False` branch. This will be fixed in a future update.\n", - "\n", - "For now, you can bypass this problem by using `np.fabs(x)` or `np.fmax(x, 0)` to ensure that each piece of the conditional is non-NaN (even for values that will never be used):" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is Ipopt version 3.12.3, running with linear solver mumps.\n", - "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 0\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1\n", - "\n", - "Total number of variables............................: 1\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 0\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 1.0000000e+000 0.00e+000 1.50e+000 0.0 0.00e+000 - 0.00e+000 0.00e+000 0\n", - " 1 0.0000000e+000 0.00e+000 0.00e+000 -11.0 2.00e+000 - 1.00e+000 5.00e-001f 2\n", - "\n", - "Number of Iterations....: 1\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+000 0.0000000000000000e+000\n", - "Dual infeasibility......: 0.0000000000000000e+000 0.0000000000000000e+000\n", - "Constraint violation....: 0.0000000000000000e+000 0.0000000000000000e+000\n", - "Complementarity.........: 0.0000000000000000e+000 0.0000000000000000e+000\n", - "Overall NLP error.......: 0.0000000000000000e+000 0.0000000000000000e+000\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 2\n", - "Number of equality constraint evaluations = 0\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 0\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 1\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - " solver : t_proc (avg) t_wall (avg) n_eval\n", - " nlp_f | 0 ( 0) 0 ( 0) 7\n", - " nlp_grad_f | 0 ( 0) 0 ( 0) 3\n", - " nlp_hess_l | 0 ( 0) 0 ( 0) 1\n", - " total | 1.00ms ( 1.00ms) 1.00ms ( 1.00ms) 1\n", - "x = 0.0\n" - ] - } - ], - "source": [ - "opti = asb.Opti()\n", - "\n", - "x = opti.variable(init_guess=1)\n", + "This solves - nice! However, note that posing the problem in this way is not guaranteed to work. This is because the underlying optimizer, IPOPT, does something it calls \"bounds pushing\". This is detailed in the excellent original IPOPT paper by Andreas Waechter, but the basic idea is that IPOPT solver performance seems to improve if the feasible space is extended by a small amount ($\\epsilon \\approx 10^{-8}$). In cases like this, that has the potential to allow a function like $x^{1.5}$ bounded by $x>0$ to go very slightly negative and NaN. IPOPT has lots of tools to help get the iterate back into the well-posed design space, but just be aware of this possibility.\n", "\n", - "f = np.where(\n", - " x > 0,\n", - " np.fmax(x, 0) ** 1.5, # One way to fix a piece of a np.where() NaN\n", - " np.fabs(x) ** 1.5 # Another way to fix a piece of a np.where() NaN\n", - ")\n", - "\n", - "opti.minimize(f)\n", - "\n", - "\n", - "sol = opti.solve()\n", - "\n", - "print(f\"x = {sol.value(x)}\")" + "Better practice would be to set the lower bound to something small but positive and nonzero, like $x>10^{-6}$. This ensures the underlying model will not NaN, even when the optimizer pushes the bounds a bit." ], "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } } - }, - { - "cell_type": "markdown", - "source": [ - "This works, and it indicates that the optimum is `x = 0` as expected." - ], - "metadata": { - "collapsed": false - } } ], "metadata": { @@ -726,4 +517,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +}