Main Subjects
Mathematical optimization
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Major Courses
- Advanced Models for Decision Making
- Optimization for Decision Making
- Models and Applications
- Optimization Algorithms
- Device-based Models with TensorFlow Lite
- Quadratic forms and matrix factorization
- Convexity
- Separating planes and Farkas’ Lemma
- The theory of optimization with and without constraints: Lagrange functions, Karush-Kuhn-Tucker theory
- Duality
- Introduction to methods for optimization without constraints: line search, steepest descent, Newton methods, conjugate directions, non-linear least squares optimization
- The Nelder-Mead search algorithm without derivatives
- Introduction to methods with constraints: linear optimization, quadratic programming, penalty, and barrier methods
