Lectures:

• Lecture #1 - Introduction
• Lecture #2 - Linear algebra review
• Lecture #3 - Singular value decomposition (SVD), Moore-Penrose pseudoinverse
• Lecture #4 - Special vector/matrix operators, the Schur complement
• Lecture #5 - Matrix calculus concepts (real/complex matrix differentiation)
• Lecture #6 - Convex sets, generalized inequalities
• Lecture #7 - Convex functions, quasiconvex functions, log-concave/convex functions
• Lecture #8 - Introduction to convex optimization problems: LPs, QPs, QCQPs, & SOCPs
• Lecture #9 - Introduction to convex optimization problems: GPs & SDPs
• Lecture #10 - Vector optimization problems, Pareto optimal points, scalarization
• Lecture #11 - Duality: Lagrange dual function/problem, weak & strong duality, geometric interpretations
• Lecture #12 - Duality: KKT conditions, perturbation and sensitivity analysis, problems with generalized inequalities
• Lecture #13 - Approximation and fitting problems with regularization
• Lecture #14 - Robust approximation, function fitting and interpolation problems
• Lecture #15 - Statistical estimation: parametric/nonparametric estimation, optimal detector design
• Lecture #16 - Statistical estimation: probability bounds, experiment design
• Lecture #17 - Geometric problems: Euclidean distance problems, extremal volume ellipsoids, centering problems
• Lecture #18 - Geometric problems: classification, placement and location, floor planning