Explores KKT conditions in convex optimization, covering dual problems, logarithmic constraints, least squares, matrix functions, and suboptimality of covering ellipsoids.
Explores gradient descent methods for smooth convex and non-convex problems, covering iterative strategies, convergence rates, and challenges in optimization.
Explores primal-dual optimization methods, focusing on Lagrangian approaches and various methods like penalty, augmented Lagrangian, and splitting techniques.
Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.
Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.