From aafce323812a11a5701fdddb52d2654358e5f77c Mon Sep 17 00:00:00 2001 From: Yimin Zhong Date: Thu, 15 Feb 2024 13:54:29 -0600 Subject: [PATCH] update --- Intro.md | 6 +----- index.md | 2 +- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/Intro.md b/Intro.md index 4196941..a3ed436 100644 --- a/Intro.md +++ b/Intro.md @@ -197,13 +197,9 @@ Consider the function $f(x) = x^4$. The first derivative is zero at $x=0$, but t ```` -## Overview of optimization algorithms - -The optimization algorithms are *iterative*, which means they start from an initial point $x_0\in\mathbb{R}^n$ and then generate a sequence of points $x_k$, $k=1,2,\cdots$ that converge to the (possibly) optimal solution. To decide how to move from $x_k$ to $x_{k+1}$, the algorithms usually require the information of $f$ at earlier points. - ### Two strategies: line search and trust region -Here we introduce two classical strategies for optimization algorithms: **line search** and **trust region**. +To decide how to move from $x_k$ to $x_{k+1}$, the algorithms usually require the information of $f$ at earlier points. Here we introduce two classical strategies for optimization algorithms: **line search** and **trust region**. - **Line search**: the line search strategy selects a direction $p_k$ and then searches along this direction from the current point to minimize the objective function. The distance to move is determined by the following one-dimensional optimization problem diff --git a/index.md b/index.md index be268e1..ebb16aa 100644 --- a/index.md +++ b/index.md @@ -1,6 +1,6 @@ # Before you start -This repository hosts the course material for Math 5650/6650: **Nonlinear Optimization**. The class will use the textbook written by Jorge Nocedal and Stephen Wright, *Numerical Optimization*, 2nd Edition. +This repository hosts the course material for Math 5650/6650: **Nonlinear Optimization** at [Auburn University](https://www.auburn.edu/cosam/departments/math/). The class will use the textbook written by Jorge Nocedal and Stephen Wright, *Numerical Optimization*, 2nd Edition. This course involves both basic optimization theory and programming. The prerequisites for the theory part are