Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
Explores KKT conditions in convex optimization, covering dual problems, logarithmic constraints, least squares, matrix functions, and suboptimality of covering ellipsoids.
Covers subquadratic attention mechanisms and state space models, focusing on their theoretical foundations and practical implementations in machine learning.
