Advanced Statistical Computing (STA5029), Fall 2025.
Lecture Material
[Lecture 1-1] Introduction - Why Convex Optimization?
[Lecture 1-2] Introduction - Convex Sets and Functions
[Lecture 2-1] Optimization Problems- Optimization Basics
[Lecture 2-2] Optimization Problems- Canonical Problem Forms
[Lecture 3-1] First-order Methods I - Gradient Descent
[Lecture 3-2] First-order Methods I - Subgradients
[Lecture 4-1] First-order Methods II - Subgradient Method
[Lecture 4-2] First-order Methods II - Proximal Gradient Descent (and Acceleration)
[Lecture 5] First-order Methods III - Stochastic Gradient Descent
[Lecture 6-1] Optimality and Duality I - Duality in Linear Programs
[Lecture 6-2] Optimality and Duality I - Duality in General Programs
[Lecture 7-1] Optimality and Duality II - Karush-Kuhn-Tucker (KKT) Conditions
[Lecture 7-2] Optimality and Duality II - Duality Uses and Correspondences
[Lecture 8-1] Second-order Methods I - Newton’s Method
[Lecture 8-2] Second-order Methods I - Barrier Method
[Lecture 9-1] Second-order Methods II - Primal-Dual Interior-Point Methods
[Lecture 9-2] Second-order Methods II - Quasi-Newton Methods