ISE 7270
Transcript Abbreviation:
ComputationalOptim
Course Description:
This course focuses on how to determine, empirically and theoretically, if one optimization algorithm is 'better' than another.
Course Levels:
Graduate
Designation:
Elective
General Education Course
(N/A)
Cross-Listings
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours
(N/A)
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:
Some familiarity with programming basics, complexity theory, Optimization (e.g. ISE 5200, ECE 5759, etc.), optimality conditions (e.g., global v. local, KKT, duality).
Electronically Enforced:
No
Exclusions
(N/A)
Course Goals / Objectives:
Design computational experiments, perform analyses, and present such results comparing optimization algorithms
Identify what computational complexity theory, and computational experiments can and cannot tell us about algorithm performance
Understand how hardware, compiler, programming language, and optimization solver choice can affect optimization algorithm performance
Apply scientific computing and numerical analysis to help analyze and design optimization algorithms
Identify different types of optimization algorithms (deterministic vs random, local vs global, etc.) and navigate inherent apples-to-oranges comparisons
Identify both theoretical and practical complications inherent in analyzing parallel optimization algorithms
Check if concurrence sought:
Yes
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Computing systems | 5.5 | 0 | 0 | 0 |
| Mathematical programming | 3 | 0 | 0 | 0 |
| Computational complexity: theory vs reality | 5.5 | 0 | 0 | 0 |
| Heuristic vs exact, local vs global algorithms | 5.5 | 0 | 0 | 0 |
| Experiment design and analysis | 8.5 | 0 | 0 | 0 |
| Numerical analysis and scientific computing | 5.5 | 0 | 0 | 0 |
| Parallel computing for optimization | 5.5 | 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 assignments | 50% |
| project | 29.99% |
| participation | 20% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| The Elements of Computing Systems, Nisan and Schocken | ||
| Numerical Optimization, Nocedal and Wright | ||
| Computational Complexity: A Modern Approach, Arora and Barak; Numerical Computing with MATLAB, Moler; Introduction to High Performance Computing for Scientists and Engineers, Hager and Wellein |
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)