Optimization Portal
Optimization lecture note
Slides
Lecture 1 First glances
Lecture 2 Some mathematical overheads
Lecture 3 Geometry of optimization and convexity
Lecture 4 The theory of unconstrained optimization
Lecture 5 The theory of constrained optimization
Lecture 6 Gradient descent algorithms for unconstrained problems
Lecture 7 Neural networks in the language of optimization
Lecture 8 Deconstraining — Penalty and barrier methods
Lecture 9 Parameter estimation
Lecture 10 Linear programming — Simplex methods
Lecture 11 Linear programming — Duality
Lecture 12 Integer linear programming — Branch-and-bound algorithms
Lecture 13 Stochastic optimization
Lecture 14 Dynamic programming and Bellmans’ principle
Lecture 15 Real-world case studies
General notes
Lecture note
Optimization methods in regression