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MATH 434 Optimization Theory with Applications to Machine Learning Units: 3.00
Theory of convex sets and functions; separation theorems; primal-duel properties; geometric treatment of optimization problems; algorithmic procedures for solving constrained optimization programs; engineering and economic applications.
Learning Hours: 132 (36 Lecture, 96 Private Study)
Offering Faculty: Faculty of Arts and Science
Course Learning Outcomes:
- Computing necessary conditions for optimality.
- Solving constrained optimization problems.
- Understanding the mathematical properties of convex sets and convex functions.
- Rigorously using separation theorems for solving optimization problems.
- Using numerical methods in the study of optimization problems.
- Solving resource allocation problems using duality theory.