LINEAR OPTIMIZATION
Introduces optimization problems whose constraints are expressed by linear inequalities. Develops geometric and algebraic insights into the structure of the problem, with an emphasis on formal proofs. Presents the theory behind the simplex method, the main algorithm used to solve linear optimization problems. Explores duality theory and theorems of the alternatives.
Prerequisites & course information
Requisites: MATH 320 , 340 , 341 , 345 , 375 , or 443 or graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) Program Course Designation: Core GenEd - Natural Science & Wellness Breadth - Natural Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Use linear programming to formulate real world decision problems. Audience: Both Grad & Undergrad 2. Apply the theory of linear programming, including geometric properties of polyhedra and duality. Audience: Both Grad & Undergrad 3. Apply algorithms to solve linear programming problems, and demonstrate their correctness. Audience: Both Grad & Undergrad 4. Combine different proving techniques explored in class in an original way to show new results. Audience: Graduate Learning Outcomes: 1. Use linear programming to formulate real world decision problems. Audience: Both Grad & Undergrad 2. Apply the theory of linear programming, including geometric properties of polyhedra and duality. Audience: Both Grad & Undergrad 3. Apply algorithms to solve linear programming problems, and demonstrate their correctness. Audience: Both Grad & Undergrad 4. Combine different proving techniques explored in class in an original way to show new results. Audience: Graduate
Catalogue variants
- LINEAR OPTIMIZATION · 3 credits.