Introduction to Optimization

Related lectures (30)

Page 3 of 3

Covers the definition of scalar product, properties, examples, and applications in Euclidean spaces, including the Cauchy-Schwartz inequality.

Covers the basics of constrained optimization, including tangent directions, trust-region subproblems, and necessary optimality conditions.

Explores Weyl characters, Macdonald's identities, and Borcherds products in Euclidean space.

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.

Covers the KKT conditions for optimization with constraints, detailing their application and significance in solving constrained problems.

Covers linear and membrane theories of pressure vessels, differential geometry of surfaces, and the reduction of dimensionality from 3D to 2D.

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Covers unconstrained and constrained optimization, optimal control, neural networks, and global optimization methods.

Introduces optimization on manifolds, covering classical and modern techniques in the field.

Covers the definition and properties of vector spaces, along with examples like Euclidean spaces and matrix spaces.