Conjugate Gradient Method

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Explores the Newton method for non-linear equations, discussing convergence criteria and stopping conditions.

Explores symmetric matrices, quadratic forms, and critical points in functions of two variables.

Introduces Manopt, a toolbox for optimization on manifolds, focusing on solving optimization problems on smooth manifolds using the Matlab version.

Discusses gradient descent for optimization with equality constraints and iterative convergence criteria.

Explores the diagonalization of symmetric matrices and the orthogonality of eigenvectors.

Covers the diagonalization of symmetric matrices and the spectral theorem.

Focuses on transforming linear systems into triangular matrices to simplify the process of finding solutions.

Covers higher order methods for solving equations iteratively, including fixed point methods and Newton's method.

Covers the concept of inverse matrices and systems resolution, including the conditions for matrix invertibility and the Gauss-Jordan algorithm.

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