Robust Optimization: Polynomial Optimization

Related lectures (31)

Explores dual cones, generalized inequalities, SDP duality, and KKT conditions in convex optimization.

Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.

Covers the KKT conditions and Slater's condition in convex optimization problems.

Covers the fundamentals of linear equations, matrices, and systems of linear equations, including matrix operations and solutions.

Explores problems with generalized inequalities in convex optimization and the equivalence between SOCP and SDP.

Explores the application of the representer theorem in kernel methods and its implications in regularized ERM.

Explores the properties of invertible matrices, including unique solutions and linear independence.

Explains row-echelon and reduced row-echelon matrices and their role in simplifying system resolution.

Explores the exponential of a matrix, properties, Frobenius norm, nilpotent matrices, commutativity, and system solutions.

Explores optimization problems in standard form, convex optimization, and optimality criteria.

Covers exercises on convex optimization, focusing on formulating and solving optimization problems using YALMIP and solvers like GUROBI and MOSEK.

Page 2 of 2