6.S098, IAP 2022 – Applied Convex Optimization
Lectures: Tuesday/Thursday 1:00-2:30pm January 4th - January 27th
Optimization is a cornerstone of many disciplines in engineering. Surprisingly, many problems such as balancing bipedal robots, designing your own index fund, and allocating power in the energy grid can be formulated as convex optimization problems. Convex optimization is a mature technol- ogy which can be used to solve a huge number of applied problems and can be scaled to problems with millions of variables. However, recognizing what can be transformed into a convex optimiza- tion problem can be challenging. This course will teach you how to recognize, formulate, and solve these problems.
Lectures will be motivated by realistic case studies from areas where convex optimization is used in practice. We will briefly survey theoretical results in convex analysis, but the majority of the course will focus on formulating and solving problems that come up in practice. Applications will include robotics, signal processing, statistics and machine learning, finance, control, power systems, and other areas based on student interest. This course is designed for advanced undergraduates and beginning graduate students.
About the Course
Comfort with multivariable calculus (18.02) and linear algebra (18.06) as well as basic programming in Python or Julia. Familiarity with basic probability and/or controls will aid in understanding many of the case studies.
Anyone who expects to use optimization as a tool for scientific computing. Beginning graduate and advanced undergraduate students are the particular target audience.
The following schedule is tentative and subject to change. See Syallabus for in depth topic list
The textbook is Convex Optimization by Boyd and Vandenberghe, available online.
* Thank you to Nikolai Matni for providing this template