Lectures related to The Conjugate Gradients Method (CG)

Covers Newton's method on Riemannian manifolds, focusing on second-order optimality conditions and quadratic convergence.

Introduces Manopt, a toolbox for optimization on manifolds, covering gradient and Hessian checks, solver calls, and manual caching.

Covers the formulation of linear systems, direct and iterative methods for solving them, and the cost of LU factorization.

Explores smooth manifolds through diffeomorphisms and embedded submanifolds in a linear space.

Explores matrix rank, subspaces, and their role in solving linear systems of equations.

Explores vector subspaces in R4, symmetric matrices, basis vectors, and canonical forms.

Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.

Covers iterative methods for solving linear equations and analyzing convergence, including error control and positive definite matrices.