ISE 5230
Transcript Abbreviation:
DecompTechMathProg
Course Description:
This course provides a description of decomposition techniques to solve large-scale optimization problems with decomposable structure. Partitioning techniques considered include Dantzig-Wolfe, Benders and Lagrangian decompositions. The considered techniques are illustrated using examples and case studies from the energy sector.
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)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: 3200 and 3210, or Grad standing.
Electronically Enforced:
No
Exclusions
(N/A)
Course Goals / Objectives:
Objective 1: To comprehend decomposition techniques in linear programming
Objective 2: To comprehend decomposition techniques in nonlinear programming
Objective 2: To comprehend decomposition techniques in nonlinear programming
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Topic 1: Decomposition in linear programming. Complicating variables. Complicating constraints. Dantzig-Wolfe Decomposition. | 12.0 | 0.0 | 0.0 | 0 |
| Topic 2: Duality in nonlinear programming. Sensitivities. | 9.0 | 0.0 | 0.0 | 0 |
| Topic 3: Decomposition in nonlinear programming. Complicating variables. Complicating constraints. Augmented Lagrangian Decomposition. Optimality Condition Decomposition. | 9.0 | 0.0 | 0.0 | 0 |
| Topic 4: Decomposition in mixed-integer programming. Complicating variables. Complicating constraints. | 12.0 | 0.0 | 0.0 | 0 |
| Total | 42 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homeworks (four, equal value) | 40% |
| Exam | 50% |
| Class participation | 10% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Decomposition Techniques in Mathematical Programming. Engineering and Science Applications | A. J. Conejo, et al. |
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)