INDENG 164: Introduction to Optimization Modeling

Course description: This course, designed for upper-division students across science and engineering disciplines, offers an in-depth exploration of optimization models. Emphasizing the importance of modeling in the application of optimization techniques, the course provides a comprehensive introduction to optimization modeling languages and their integration with optimization solvers. A key aspect of this course is its focus on the art and science of modeling, especially in the context of uncertainty, a concept highly relevant to data science. Students will engage in extensive modeling exercises, gaining hands-on experience with software packages and programming that is essential for solving optimization problems.

Prerequisites: COMPSCI C8, Math 53 and Math 54.

Recommendation: Students are strongly encouraged to take at least one course from the following list before taking this course: INDENG 160, INDENG 162, EECS 127A

Lecture: Monday and Wednesday 9:00am - 10:00am, Etcheverry 3107

Discussion: Friday 2:00pm - 3:00pm, Etcheverry 3113

Software: The course will be primarily taught with Pyomo, mainly during the discussion sections/labs.

Textbook: Hands-On Mathematical Optimization with Python, by Postek, Krzysztof and Zocca, Alessandro and Gromicho, Joaquim and Kantor, Jeffrey

Course topics: 

Part 1: Deterministic optimization

• Week 1-2: Linear programs

Discussion 1: Treatment planning for intensity-modulated radiation therapy (IMRT)

Discussion 2: Google Ads

• Week 3-4: (Mixed) integer programs

Discussion: NFL sports scheduling

• Week 5: Network flow

Discussion: Cryptocurrency arbitrage search

• Week 6-7: Convex programs

Discussion: Sparse learning

• Week 8: Conic programs

Discussion: Matrix completion

****************(Week 9) Review & Midterm Exam*****************

Part 2: Optimization under uncertainty

• Week 10: Single-stage stochastic programs

      Discussion: Economic dispatch

• Week 11-12: Robust optimization

Discussion: adversarial machine learning

• Week 13-14: Two-stage stochastic programs

     Discussion: facility location

Grading: 

• Participation: 5%

• Homework: 40%

• Mini-project: 15%

• Midterm: 20%

• Final: 20%