Extreme Values and Constraints

Related lectures (27)

Page 1 of 3

Explores improper integrals, convergence criteria, comparison theorems, and solid revolution.

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

Covers the theory and applications of optimization with constraints, including key concepts and numerical methods.

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

Covers the derivation and application of Euler-Lagrange equations for optimization problems in mathematical analysis.

Introduces the Dirac delta function and discusses its properties and applications in signal processing and physics.

Covers the Lipschitz gradient theorem and its applications in function optimization.

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

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

Explores numerical integration methods and their application in solving differential equations and simulating physical systems.