Conjugate gradient method

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Optimization without Constraints: Gradient Method

Covers optimization without constraints using the gradient method to find the function's minimum.

Gradient Descent

Covers the concept of gradient descent in scalar cases, focusing on finding the minimum of a function by iteratively moving in the direction of the negative gradient.

Jacobi and Gauss-Seidel methods

Explains the Jacobi and Gauss-Seidel methods for solving linear systems iteratively.

Conjugate Gradient Methods: Overview

Provides an overview of conjugate gradient methods, including preconditioning, nonlinear conjugate gradient, and singular value decomposition.

Momentum methods and nonlinear CG

Explores gradient descent with memory, momentum methods, conjugate gradients, and nonlinear CG on manifolds.

Conjugate Gradient Methods

Explores gradient and conjugate gradient methods for solving linear systems efficiently.

Optimization Methods in Machine Learning

Explores optimization methods in machine learning, emphasizing gradients, costs, and computational efforts for efficient model training.

Optimality of Convergence Rates: Accelerated Gradient Descent

Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.

Conjugate Gradient Optimization

Explores Conjugate Gradient optimization, covering quadratic and nonlinear cases, Wolfe conditions, BFGS, CG algorithms, and matrix symmetry.

Conjugate Gradient Method

Explores the Conjugate Gradient method for solving linear systems and introduces Quasi-Newton methods and rank 2 updates.