Linear Algebra Review: Convex Optimization

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (25)

Page 2 of 3

Singular Values: Definitions and Properties

Covers the concept of singular values in linear algebra and their properties, including diagonalization and practical examples.

Matrix Equations: Finding Free Variables

Explains how to find free variables in matrix equations and analyze characteristic polynomials.

Cholesky Factorization: Theory and Algorithm

Explores the Cholesky factorization method for symmetric positive definite matrices.

Convex Optimization: Convex Functions

Covers the concept of convex functions and their applications in optimization problems.

Singular Value Decomposition: Orthogonal Vectors and Matrix Decomposition

Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.

Linear Algebra: Matrix Representation

Explores linear applications in R² and matrix representation, including basis, operations, and geometric interpretation of transformations.

Eigenvalues and Eigenvectors Decomposition

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Linear Algebra: Bases and Transformations

Covers bases, transformations, and matrix decompositions in linear algebra.

Singular Value Decomposition

Explores Singular Value Decomposition and its role in unsupervised learning and dimensionality reduction, emphasizing its properties and applications.

Symmetric Matrices: Diagonalization

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.