ISE 5201
Transcript Abbreviation:
Theory Lin Optim
Course Description:
Introduction to linear optimization with an emphasis on theory. Topics include model formulation, solution methods, polyhedral and duality theory, sensitivity analysis, and software.
Course Levels:
Undergraduate (1000-5000 level)
Graduate
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits:
(N/A)
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: Math 2174, 2415, 2568, or 4568, and permission of instructor; or Grad standing.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 5200 (720).
Course Goals / Objectives:
Understand how to model problems with linear objective and constraints.
Understand the details of the simplex method and the associated polyhedral theory.
Understand duality and conduct sensitivity and parametric analysis.
Understand methods for LP Decomposition.
Understand interior point methods for linear programs.
Use modeling and optimization software packages to model and solve linear programs.
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Linear Programming Applications | 6.0 | 0.0 | 0.0 | 0 |
| Simplex Method | 9.0 | 0.0 | 0.0 | 0 |
| Polyhedral Theory | 9.0 | 0.0 | 0.0 | 0 |
| Duality, sensitivity, and Parametric Analysis | 9.0 | 0.0 | 0.0 | 0 |
| Decomposition methods | 8.5 | 0.0 | 0.0 | 0 |
| Interior point methods | 6.0 | 0.0 | 0.0 | 0 |
| Software | 3.0 | 0.0 | 0.0 | 0 |
| Total | 50.5 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 30% |
| Midterm | 30% |
| Final exam | 40% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| ntroduction to Linear Optimization, Athena Scientific Series in Optimization and Neural Computation | Dimitris Bertsimas and John N. Tsitsiklis |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info:
Attachments:
(N/A)
Additional Notes or Comments:
(N/A)
Basic Course Overview:
ISE_5201_basic.pdf
(10.11 KB)