Conjugate gradient methods

Author: Alexandra Roberts, Anye Shi, Yue Sun (SYSEN6800 Fall 2021)

Introduction

The conjugate gradient method (CG) was originally invented to minimize a quadratic function:
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F(\textbf{x})=\frac{1}{2}\textbf{x}^{T}\textbf{A}\textbf{x}-\textbf{b}\textbf{x}}
where A is an n × n symmetric positive definite matrix, x and b are n × 1 vectors. The solution to the minimization problem is equivalent to solving the linear system, i.e. determining x when ∇F(x) = 0
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \textbf{A}\textbf{x}-\textbf{b} = \textbf{0}}