Truncated CG: Conjugate Gradients

Related lectures (26)

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Explores vector spaces, subspaces, bases, and linear combinations in R² and R³, including free and linked families.

Explores strong convexity's role in faster convergence rates for optimization algorithms.

Explores the inverse power method for ODEs and the significance of Lipschitz continuity.

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

Covers linear applications, matrices, transformations, and the principle of superposition.

Explores matrix representations of linear maps and their invariance, using examples and special cases.

Covers line search, Newton's method, BFGS, and conjugate gradient in nonlinear optimization.

Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.

Explores linear similarities and similitudes, demonstrating their properties and applications in geometry.

Covers the theory and applications of bilinear forms in various mathematical contexts.