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Lecture
Robust Optimization: Polynomial Optimization
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Related lectures (31)
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Linear Algebra Review: Convex Optimization
Covers essential linear algebra concepts for convex optimization, including vector norms, eigenvalue decomposition, and matrix properties.
Lagrangian Duality: Convex Optimization
Explores Lagrangian duality in convex optimization, transforming problems into min-max formulations and discussing the significance of dual solutions.
Matrix of Cofactors and Inverse Matrix Formula
Covers the concept of the matrix of cofactors and a formula to calculate the inverse of a matrix.
Polynomial Optimization: SOS and SDP
Explores Sum of Squares polynomials and Semidefinite Programming in Polynomial Optimization, enabling the approximation of non-convex polynomials with convex SDP.
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Convex Optimization: Elementary Results
Explores elementary results in convex optimization, including affine, convex, and conic hulls, proper cones, and convex functions.
Semi-Definite Programming
Covers semi-definite programming and optimization over positive semidefinite cones.
Linear Applications: Matrices and Transformations
Covers linear applications, matrices, transformations, and the principle of superposition.
Matrix Column Space Analysis
Covers the analysis of the column space of a matrix and its implications.
