LARGE-SCALE OPTIMIZATION
Decomposition of large-scale linear, nonlinear and integer programs. Minkowski representation of polyhedra. Benders' and Dantzig-Wolfe decomposition. Relaxations, including Lagrangian relaxation. Examples include multicommodity flow and stochastic linear programs. Design and testing of computational strategies for difficult optimization problems. Students will implement projects in Python. Graduate/Undergraduate Equivalency: INDE 546 .
Prerequisites & course information
Short Title: LARGE-SCALE OPTIMIZATION Department: Comp Appl Math Operations Rsch Grade Mode: Standard Letter Course Type: Lecture Credit Hours: 3 Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students. Course Level: Undergraduate Upper-Level Prerequisite(s): CMOR 441 and COMP 140 and ( CMOR 302 or CMOR 303 or MATH 354 or MATH 355 )
Sources
- Course catalogue ↗2026–2027