Course Description
Optimization entails seeking decisions that maximize objectives while satisfying constraints, with applications across engineering, business, economics, statistics, data analysis, and everyday life. This course provides an in-depth and rigorous introduction to mathematical optimization, covering how to formulate, analyze, and solve real-world problems using modern optimization theory and software. Topics include finite-dimensional linear optimization problems with continuous and discrete variables, sensitivity and duality, basic elements of convex analysis, first- and second-order optimality conditions for nonlinear optimization problems, and a discussion of important algorithmic and computational aspects related to optimization. Prerequisites: MATH 113, 115, or equivalent.
Grading Basis
GOP - GSB Student Option LTR/PF
Min
3
Max
3
Course Repeatable for Degree Credit?
No
Course Component
Case/Problem Study
Enrollment Optional?
No
Schedule
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Programs
OIT676 is a completion requirement for: