ECE 5500
Transcript Abbreviation:
Optimization
Course Description:
Introduction to Optimization, including unconstrained optimization, gradient descent, Newton’s method, convexity, constrained optimization, KKT, duality, dynamic programming, basic reinforcement learning, and applications of optimization in ECE.
Course Levels:
Undergraduate (1000-5000 level)
Graduate (5000-8000 level)
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)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Distance Learning (100% online)
Prerequisites and Co-requisites:
Prereq: Math 2568 and MATH 2415; or Grad standing in Engineering or Math and Physical Sciences.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 5759.
Course Goals / Objectives:
Master computational and mathematical methods for optimization to solve engineering problems
Be exposed to posing engineering problems as optimization problems
Be competent with arguing which algorithm is suitable for solving a given optimization problem
Be familiar with convergence techniques for optimization algorithms
Be exposed to modern software packages for numerical optimization, such as MATLAB or Python
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Overview of basic background | 1.0 | 0.0 | 0 | 0.0 | 0 |
| Convex functions and convex sets, definition of global and local optimality | 3 | 0.0 | 0 | 0.0 | 0 |
| Unconstrained optimization, gradient methods and convergence properties, second-order algorithms such as Newton's method and convergence | 12 | 0.0 | 0 | 0.0 | 0 |
| Constrained optimization, Lagrange multiplier theorem and KKT conditions, duality, penalty method | 12 | 0.0 | 0 | 0.0 | 0 |
| Applications in electrical and computer engineering: deep learning, communications, estimation, and/or electro-magnetics | 6 | 0.0 | 0 | 0.0 | 0 |
| Dynamic programming, approximate dynamic programming, reinforcement learning | 5 | 0 | 0 | 0 | 0 |
| Total | 39 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 15% |
| Computer assignments | 15% |
| Quizzes | 10% |
| Exams | 60% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Nonlinear Programming | Dimitri P. Bertsekas |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Significant contribution (7+ hours) | 1 | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| Significant contribution (7+ hours) | 2 | 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 |
| Substantial contribution (3-6 hours) | 3 | an ability to communicate effectively with a range of audiences - pre-2019 EAC SLO (g) |
| Some contribution (1-2 hours) | 5 | an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives |
| Significant contribution (7+ hours) | 6 | an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
| Significant contribution (7+ hours) | 7 | an ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
Embedded Literacies Info: