GD

"Gradient descent".

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Introduction

the goal is to solve the equation $A \cdot U = b$. Using a gradient descent method in the program, the gradient descent method is the conjugate gradient method. In this subject, the matrix $A$ is a positive semidefinite matrix. The obective function is $f: f(x) = 0.5x^\top\cdot,A\cdot,x- x^\top \cdot b $.

Notation

• $N$ size matrix and size vector
• $A$ matrix
• $b$ vector
• $X$ coordinate vector
• $U$ a solve vector

subject to :

• $N$ is a interger such that $N > 1$
• $A$ symmetric matrix defined semi positive
• $b$ $\in \mathbb{R}^N$
• $0 < X < 1$
• $U$ such as $f(U) = 0$

to get started

change string and $N$ in program.py. string ="2D" or string = "3D".

if __name__ == '__main__'

pre-requisites

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the use of program progmanopt requires :

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if the packages are not installed. you can install them the command :

pip install -r requirements.txt

if the command did not work, you can install the packages one by one with the command

pip install [package]

Contributor

• Loik johan ACAKPO-ADDRA alias @loik77360

Links

packages

• numpy
• matplotlib
• mpl_toolkits

Documentation

• numerical analysis