Constrained optimization

Related lectures (31)

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Explores stationary points and the Lagrange multiplier theorem for function optimization.

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

Explores constrained convex optimization with min-max formulation and Fenchel conjugation.

Explores Lagrange multiplier method and DuBois-Reymond equation in optimization.

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

Explores optimal control in unconstrained and constrained problems, emphasizing the importance of sparsity.

Covers methods for solving nonlinear optimization problems, including direct search, Newton-Raphson, and branch and bound.

Covers the basics of Support Vector Machines, focusing on hard-margin and soft-margin formulations.

Introduces optimisation problems, methods, and constraints for solving multi-dimensional problems with and without constraints.

Explains linear constraints and variable elimination in optimization problems, illustrating the process step by step.