1. Introduction to the importance of optimization to machine learning
2. Introduction to convex optimization engineering for ML, DL, and AI
3. Rules and guidelines to know before you start the course
4.1 Applied Optimization-Reference2.pdf
4.2 bv_cvxbook.pdf
4.3 bv_cvxslides-all lectures.pdf
4.4 intro.pdf
4.5 Lectures on Moder Optimization-Reference3.pdf
4.6 Matlab Framework for solving convex optimization.html
4.7 Python Framework for solving convex optimization.html
4. Convex Optimization Introduction
5. Convex Sets Convex Optimization
6. Convex Functions Convex Optimization
7. Convex Optimization Principles Convex Optimization
8. Linear Programming & SIMPLEX algorithm with MATLAB Convex Optimization
9. Quadratic Programs Convex Optimization
10. Quadratically Constrained Quadratic Programs Convex Optimiz
11. Second Order Cone Programming (SOCP) Convex Optimization
12. Geometric Programs (GP) Convex Optimization
13. Generalized Geometric Programs (GGP) Convex Optimization
14. Semidefinite Programming (SDP) Convex Optimization
15. Vector and Multicriterion Optimization Pareto Optimal points
16. Optimal Trade-off Analysis Convex Optimization
17. Lagrange Dual Function Convex Optimization
18. Lagrange Dual Problem Convex Optimization
19. Certificate of Suboptimality (e-suboptimality)
20. Complementary Slackness
21. KKT Conditions Convex Optimization
22. Perturbation and Sensitivity Analysis
23. Equivalent Reformulations
24. Weak Alternatives Convex Optimization
25. Strong Alternatives Convex Optimization
26. Descent Backtracking & Unconstrained Minimization
