Optimization Basics: Norms, Convexity, Differentiability

Related lectures (29)

Page 3 of 3

Covers orthogonal projections, rectors, norms, and geometric observations in vector spaces.

Explores orthogonality, norms, and distances in vector spaces for solving linear systems.

Covers the properties of the matrix exponential and its justification through the norm definition.

Covers essential concepts of vectors, norms, and their properties in linear algebra.

Explores Sobolev spaces in higher dimensions, discussing derivatives, properties, and challenges with continuity.

Explains orthogonal projection and vector decomposition with examples in particle trajectory analysis.

Covers a recap of Analysis I and delves into the concept of open sets in R^n, emphasizing their importance in mathematical analysis.

Explores norms and bounded operators in functional analysis, demonstrating their properties and applications.

Covers normed spaces, Banach spaces, and Hilbert spaces, as well as dual spaces and weak convergence.