Looking for a great math-based elective course for this semester?
ME/ECE/MATH 497/597 Optimization Theory and Practice
Tuesday/Thursday – 10:30-11:45 AM
Instructor: Aykut Satici
Optimization Theory is a branch of mathematics devoted to solving optimization problems…where we want to minimize or maximize the function value. These types of problems are found numerously in computer science and applied mathematics. Finding good solutions to these problems is what gives data science and machine learning models good accuracy, as they themselves are built off numerical methods used for finding good solutions to minimize error functions.*
Course Description: Basics of optimization theory, numerical algorithms and applications. The course is divided into three main parts: linear programming (simplex method, duality theory), unconstrained methods (optimality conditions, descent algorithms and convergence theorems), and constrained minimization (Lagrange multipliers, Karush-Kuhn-Tucker conditions, active set, penalty and interior point methods, and global optimization methods such as genetic algorithm and simulated annealing). Applications in engineering, operations, finance, statistics, etc. will be emphasized. Students will also use optimization packages to obtain practical experience with the material.
This course satisfies the computation requirements for MSME and MEngrME degrees
*Morgan, B. (2021, June 24). Unit 1) Optimization Theory. Towards Data Science. Retrieved July 19, 2023, from https://towardsdatascience.com/unit-1-optimization-theory-e416dcf30ba8