Lecture

Conjugate Gradient Optimization

Related lectures (37)
Calcul de valeurs propres
Covers the calculation of eigenvalues and eigenvectors, emphasizing their significance and applications.
Optimization methods
Covers optimization methods, focusing on gradient methods and line search techniques.
Newton Method: Convergence and Quadratic Care
Covers the Newton method and its convergence properties near the optimal point.
Quasi-newton optimization
Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.
Optimal Control: KKT Conditions
Explores optimal control and KKT conditions for non-linear optimization with constraints.
Conjugate Gradient Method
Explores the Conjugate Gradient method for solving linear systems and introduces Quasi-Newton methods and rank 2 updates.
Optimization without Constraints: Gradient Method
Covers optimization without constraints using the gradient method to find the function's minimum.
Equivalent formulation
Covers the concept of equivalent formulation in constrained optimization and explores the tangent cone.
Numerical Analysis: Linear Systems
Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.
Optimisation with Constraints: Interior Point Algorithm
Explores optimization with constraints using KKT conditions and interior point algorithm on two examples of quadratic programming.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.