ECE 6500
Transcript Abbreviation:
Convex & Stoch Opt
Course Description:
Convex and stochastic optimization theory and algorithms applied to selected electrical engineering application areas.
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:
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: Grad standing.
Electronically Enforced:
No
Exclusions:
Not open to student with credit for 7100.
Course Goals / Objectives:
Discuss convex formulation of problems with stochastic components from diverse ECE domains such as circuit design, communications, signal processing, control, estimation, learning, and/or electromagnetics.
Introduce primal and dual methods for the solutions of convex optimization problems.
Introduce probabilistic and control-theoretic methods for stochastic network analysis
Design decentralized and low-complexity algorithms and analyze their performance for decentralized operation.
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Modeling of convex optimization problems with applications in electrical and computer engineering, such as communications, networking, signal processing, learning, estimation, and/or electromagnetics. | 2.0 | 0.0 | 0.0 | 0 |
| Convex optimization theory - convex sets and functions, optimality conditions for unconstrained/constrained and non-smooth convex optimization; | 6.0 | 0.0 | 0.0 | 0 |
| Duality Theory and Methods – Geometric Duality, Lagrangian Duality, Strong/Weak Duality, KKT conditions. | 6.0 | 0.0 | 0.0 | 0 |
| Convex optimization algorithms - unconstrained methods; dual and primal-dual methods; gradient, sub gradient, Nesterov/Heavy-Ball, Proximal, Mirror-Descent Methods. | 11.0 | 0.0 | 0.0 | 0 |
| Stochastic design and analysis techniques - probability and random processes basics: Markov chains; stability theory, Foster-Lyapunov criteria; Lyapunov Drift Minimization; Heavy-traffic analysis; Stochastic GD, Random Gradient Descent, Variance Reduction Methods. | 8.0 | 0.0 | 0.0 | 0 |
| Optimization-based network algorithm design - cross-layer controller description; performance analysis: proof of optimality of the cross-layer controller; extensions – multi-cast traffic, asynchronous implementation; decentralized and low-complexity algorithms | 8.0 | 0.0 | 0.0 | 0 |
| Total | 41 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Midterm | 30% |
| Project | 35% |
| Final | 35% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| notes | Eryilmaz | |
| Convex Optimization, Cambridge University Press (reference) | S. Boyd and L. Vandenberghe | |
| Convex Optimization: Algorithms and Complexity | Sebastian Bubeck | |
| Stochastic network optimization with application to communication and queueing systems | Michael Neely | |
| Convex Optimization Algorithms | Dimitri P. Bertsekas |
ABET-CAC Criterion 3 Outcomes
(N/A)
ABET-ETAC Criterion 3 Outcomes
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Outcome | Contribution | Description |
|---|---|---|
| No outcome selected | ||
Embedded Literacies Info
(N/A)
Attachments
(N/A)
Additional Notes or Comments
(N/A)