ISE 3210
Transcript Abbreviation:
Nonlinr & Dyn Opt
Course Description:
Introduction to nonlinear, dynamic, and network optimization models and solution techniques.
Course Levels:
Undergraduate (1000-5000 level)
Designation:
Required
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)
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: 3200 and enrollment in ISE or Engineering Physics major.
Electronically Enforced:
No
Exclusions:
(N/A)
Course Goals / Objectives:
Model decision problems with nonlinear, dynamic, or multiple objectives
Recognize model convexity
Use descent algorithms to solve nonlinear programs and recognize optimality of solutions
Set up dynamic programming recursions for deterministic models
Draw upon background in engineering sciences to model decision problems that arise within engineering applications
Apply nonlinear programming techniques to model decisions with multiple stakeholders and with game theoretic considerations
Use modeling and optimization software packages to model and solve nonlinear and dynamic programs
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Nonlinear programming models | 9.0 | 0.0 | 0.0 | 0 |
| Optimality conditions for nonlinear programs | 4.5 | 0.0 | 0.0 | 0 |
| Direct search and steepest descent algorithms | 6.0 | 0.0 | 0.0 | 0 |
| Dynamic programming | 6.0 | 0.0 | 0.0 | 0 |
| Multiobjective modeling | 4.5 | 0.0 | 0.0 | 0 |
| Application of nonlinear programming to game theory | 3.0 | 0.0 | 0.0 | 0 |
| Software | 6.0 | 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 |
|---|---|
| Projects | 40% |
| Midterm Exams | 30% |
| Final Exam | 30% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Engineering Optimization | A. Ravindran, K. M. Ragsdell, and G. V. Reklaitis | |
| Introduction to Mathematical Programming: Applications and | Wayne L. Winston and Munirpallam Venkataramanan |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Outcome | Contribution | Description |
|---|---|---|
| 1 | Significant contribution (7+ hours) | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| 2 | Significant contribution (7+ hours) | an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
| 3 | Some contribution (1-2 hours) | an ability to communicate effectively with a range of audiences - pre-2019 EAC SLO (g) |
| 4 | Significant contribution (7+ hours) | an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
| 6 | Some contribution (1-2 hours) | an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
| 7 | Some contribution (1-2 hours) | an ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
Embedded Literacies Info:
Attachments:
(N/A)
Additional Notes or Comments:
and added ABET crit.
Basic Course Overview:
ISE_3210_basic.pdf
(10.28 KB)