ISE 7200
Transcript Abbreviation:
Nonlinear Opt
Course Description:
Unconstrained and constrained nonlinear optimization, covering applications, theory dealing with convexity, optimality conditions, duality, and algorithms.
Course Levels:
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
(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: Calculus, linear algebra, computer programming, and an introductory optimization course, or permission of instructor.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for both 820 and 821.
Course Goals / Objectives:
Recognize the need for different assumptions in nonlinear programming, and the need for derivative-free as well as gradient-based algorithms
Understand the commonality between Newton-type methods and extensions for both unconstrained and constrained optimization
Gain knowledge of specialized optimization and numerical software for nonlinear optimization
Lagrangian duality
Convex sets and functions
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Convergence theory for nonlinear optimization algorithms | 3.0 | 0.0 | 0.0 | 0 |
| Derivative-free optimization and line search | 6.0 | 0.0 | 0.0 | 0 |
| Newton’s method and self concordance (unconstrained optimization) | 6.0 | 0.0 | 0.0 | 0 |
| Quasi-Newton and Conjugate Gradient Methods | 6.0 | 0.0 | 0.0 | 0 |
| Automatic Differentiation and Numerical Tools for Factorizations | 6.0 | 0.0 | 0.0 | 0 |
| Linearly constrained optimization, and interior point methods | 6.0 | 0.0 | 0.0 | 0 |
| Nonlinear constrained optimization | 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 |
|---|---|
| Homework/projects | 40% |
| Midterms/quizzes | 30% |
| Final exam | 30% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Nonlinear Programming: Theory and Algorithms | Bazaara, Sherali, Shetty | |
| Convex Optimization | Boyd, Vandenberghe |
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)