Winter-Spring 2022, MATH 670 & 671:   Methods of Optimization

Instructor: Thomas Yu, email: , office: Korman 268, office hours: Monday, Tuesday, Thursday 4:00-5:30p.m

Course Info. and Syllabus

Assigned Textbooks:

  1. Jorge Nocedal and Stephen Wright, Numerical Optimization, 2nd edition
  2. Amir Beck, Introduction to Nonlinear Optimization
  3. Stephen Boyd and Lieven Vandenberghe, Convex Optimization

Pre-requisite: Linear Algebra, Multivariate Calculus

Grading policy: 80% HW, 20% a final oral exam

Lecture notes:

Theory of Constrained Optimization 1

Theory of Constrained Optimization 2

Theory of Constrained Optimization 3

Theory of Constrained Optimization 4

Linear Programming and Simplex Method 1

Linear Programming and Simplex Method 2

Linear Programming, Shadow Price and Game Theory

Linear Programming and Primal Dual Interior Point Method 1

Linear Programming and Primal Dual Interior Point Method 2

Unconstrained Optimization 1

Unconstrained Optimization 2

Unconstrained Optimization 3 : Some convergence results of line search methods

Unconstrained Optimization 4: Newton and Quasi-Newton methods

Unconstrained Optimization 5: Quasi-Newton methods continued

Methods of Constrained Optimization 1

Methods of Constrained Optimization 2: Quadratic programming

Methods of Constrained Optimization 3: Sequential quadratic programming (to be cont'd)