INDENG 164: Introduction to Optimization Modeling
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%