Concept

Mathematical optimization

Related lectures (29)

Optimization with Constraints: KKT Conditions

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Introduction to Optimization and Operations Research

Covers fundamental concepts of optimization and operations research, exploring real-world examples and key topics over a semester.

Optimization Principles

Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.

Energy optimization strategies

Covers brainstorming options for smart operation changes, heat recovery, and PV panel performance.

MILP Model and Typical Days by FM

Discusses MILP model, typical days, clustering, and extreme periods analysis in energy systems optimization.

Optimization with Constraints: KKT Conditions

Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.

Introduction to Optimization

Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.

Linear Programming: Weighted Bipartite Matching

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Deep Generative Models in Drug Discovery

Explores the application of deep generative models in drug discovery, focusing on designing small molecules and optimizing molecular structures.

Optimization in Engineering

Explores optimization methods in engineering, covering decision variables, constraints, and various solving techniques.

Energy Systems Optimization

Explores energy systems modeling, optimization, and cost analysis for efficient operations.

Integer Optimization

Covers optimization problems, minimal packing, and bounds in Integer Optimization.

Convex Relaxation in Optimization

Explores convexifying nonconvex problems through relaxation techniques, illustrated with total variation reconstruction examples.

Semi-Definite Programming

Covers semi-definite programming and optimization over positive semidefinite cones.

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Constrained optimization: the basics

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

Distributionally Robust Portfolio Optimization

Explores distributionally robust portfolio optimization and compares different estimation approaches and methods for portfolio evaluation.

Chemical Reaction Optimization: Multi-Task Learning

Explores multi-task learning for accelerated chemical reaction optimization, showcasing challenges, automated workflows, and optimization algorithms.

Optimality Conditions in Linear Optimization

Covers optimality conditions, strong duality, and complementarity slackness in linear optimization.