Optimization for Information Systems


CMPE Degree: This course is Elective for the CMPE degree.

EE Degree: This course is Elective for the EE degree.

Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.

Technical Interest Group(s) / Course Type(s): Digital Signal Processing, Systems and Controls

Course Coordinator: Mark Andrew Davenport

Prerequisites: MATH 2551 or equivalent and CS 1301 or equivalent

Corequisites: None

Catalog Description

An introduction to the fundamentals of optimization with a focus on algorithms and applications in signal processing, control systems, machine learning, and robotics.


Course Outcomes

  1. Formulate inference problems in the language of linear algebra and optimization.
  2. Analyze and compute the solutions to least-squares problems in the context of regression.
  3. Implement and use basic computational methods for solving optimization problems.
  4. Map descriptions of real-world problems into quantitative computational problems.

Student Outcomes

In the parentheses for each Student Outcome:
"P" for primary indicates the outcome is a major focus of the entire course.
“M” for moderate indicates the outcome is the focus of at least one component of the course, but not majority of course material.
“LN” for “little to none” indicates that the course does not contribute significantly to this outcome.

1. ( Not Applicable ) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics

2. ( Not Applicable ) 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. ( Not Applicable ) An ability to communicate effectively with a range of audiences

4. ( Not Applicable ) 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

5. ( Not Applicable ) 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

6. ( Not Applicable ) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions

7. ( Not Applicable ) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Strategic Performance Indicators (SPIs)

Course Objectives

Topical Outline

  1. Regression and least squares
  2. First-order and second-order conditions for optimality
  3. Gradient descent algorithms and accelerated methods
  4. Neural networks and back propagation
  5. Constrained optimization and Lagrange duality
  6. Calculus of variations and principle of least action
  7. Markov decision processes
  8. Dynamic programming and reinforcement learning
  9. Optimal control theory
  10. The role of convexity
  11. A taxonomy of convex optimization problems