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MTHE 434 Optimization Theory with Applications to Machine Learning Units: 3.50
Theory of convex sets and functions; separation theorems; primal-dual properties; geometric treatment of optimization problems; algorithmic procedures for solving constrained optimization programs; applications of optimization theory to machine learning.
NOT OFFERED 2024-2025
(Lec: 3, Lab: 0, Tut: 0.5)
NOT OFFERED 2024-2025
(Lec: 3, Lab: 0, Tut: 0.5)
Requirements: Prerequisites: MTHE 281 (MATH 281), MTHE 212 (MATH 212), or permission of the instructor
Corequisites:
Exclusions:
Offering Term: W
CEAB Units:
Mathematics 15
Natural Sciences 0
Complementary Studies 0
Engineering Science 15
Engineering Design 12
Offering Faculty: Smith Engineering
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.