Lectures related to Ellipsoid method

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

Discusses proximal and subgradient descent methods for optimization in machine learning.

Explores the KKT conditions in convex optimization, including dual cones, SDP duality, and convex hulls.

Covers the implementation of linked lists in Java, focusing on operations and testing.

Covers the Local Hamiltonian Problem, focusing on QMA-Complete problems and the complexity of verifying quantum computations.