Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
Explores the convexity of Lovász extension and submodular function maximization, focusing on extending functions to convex sets and proving their convexity.
Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.
