ISE-5406: Optimization: Linear and Nonlinear Programming
Description: Introduction to theory of linear and nonlinear programming. A mix of theoretical concepts and numerical algorithms to solve the linear and nonlinear programming problems. 5405 (Linear Programming): Modeling for real world problems using linear programming and integer linear programming. Geometric foundations for linear programs – characterization for polyhedral sets and convex analysis. Numerical algorithms for linear programs: simplex method (its geometry and algebra), primal-dual simplex algorithm, revised simplex, two-phase and big-M methods. Farkas’ Lemma and Optimality Karush-Kuhn-Tucker (KKT) conditions for linear programs. Duality theory, sensitivity analysis, state-of-the-art modeling language and solvers. 5406 (Nonlinear Programming): Convex analysis and optimization. Fritz John and KKT optimality conditions and numerical algorithms for nonlinear programs. Unconstrained and constrained nonlinear optimization. Convex optimization problems. Numerical methods: Line search methods, steepest descent method, Newton’s method, conjugate directions method, projection gradient method, affine scaling method. Pre: Graduate standing for 5405; 5405 for 5406.
Pathways: N/A
Course Hours: 3 credits
Corequisites: N/A
Crosslist: N/A
Repeatability: N/A
Sections Taught: 6
Average GPA: 3.82 (rounds to A-)
Strict A Rate (No A-) : 72.63%
Average Withdrawal Rate: 0.98%
| Manish Bansal | 2023 | 90.0% | 8.5% | 0.0% | 0.0% | 0.0% | 1.5% | 3.91 | 4 |
| Hanumanthrao Kannan | 2024 | 53.9% | 46.2% | 0.0% | 0.0% | 0.0% | 0.0% | 3.58 | 1 |
| Robert Hildebrand | 2019 | 68.8% | 31.3% | 0.0% | 0.0% | 0.0% | 0.0% | 3.73 | 1 |