ISE 5200
Transcript Abbreviation:
Linear Opt
Course Description:
Introduction to the linear optimization and applications. Topics include model formulation, solution methods, polyhedral and duality theory, sensitivity analysis, and software usage.
Course Levels:
Undergraduate (1000-5000 level)
Graduate
Designation:
Elective
Required
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:
3.00
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 720 or IndEng 702.
Course Goals / Objectives:
Model problems with linear objective and constraints.
Use simplex algorithm to solve linear programs.
Understand polyhedral theory as it relates to the simplex method.
Understand duality and conduct sensitivity and parametric analysis.
Understand the need for LP Decomposition, and learn some of these methods.
Use 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 | 5.0 | 0.0 | 0.0 | 0 |
| Simplex Method | 8.0 | 0.0 | 0.0 | 0 |
| Polyhedral Theory | 6.0 | 0.0 | 0.0 | 0 |
| Duality, sensitivity, and Parametric Analysis | 6.0 | 0.0 | 0.0 | 0 |
| Decomposition methods | 6.0 | 0.0 | 0.0 | 0 |
| Interior point methods | 6.0 | 0.0 | 0.0 | 0 |
| Software | 2.0 | 0.0 | 0.0 | 0 |
| Total | 39 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework/projects | 30% |
| Midterms/Quizzes | 40% |
| Final exam | 30% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Introduction to Linear Optimization | Bertsimas and 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
(N/A)
Attachments
(N/A)
Additional Notes or Comments
(N/A)