This course concentrates on recognizing and solving convex optimization problems that arise in applications. The syllabus includes: convex sets, functions, and optimization problems; basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory, theorems of alternative, and applications; interior-point methods; applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.
This course should benefit anyone who uses or will use scientific computing or optimization in engineering or related work (e.g., machine learning, finance). More specifically, people from the following fields: Electrical Engineering (especially areas like signal and image processing, communications, control, EDA & CAD); Aero & Astro (control, navigation, design), Mechanical & Civil Engineering (especially robotics, control, structural analysis, optimization, design); Computer Science (especially machine learning, robotics, computer graphics, algorithms & complexity, computational geometry); Operations Research; Scientific Computing and Computational Mathematics. The course may be useful to students and researchers in several other fields as well: Mathematics, Statistics, Finance, Economics.
Additional Instructors / Contributors
Neal Parikh is a 5th year Ph.D. Candidate in Computer Science at Stanford University. He has previously taught Convex Optimization (EE 364A) at Stanford University and holds a B.A.S., summa cum laude, in Mathematics and Computer Science from the University of Pennsylvania and an M.S. in Computer Science from Stanford University.
Ernest Ryu is a PhD candidate in Computational and Mathematical Engineering at Stanford University. He has served as a TA for EE364a at Stanford. His research interested include stochastic optimization, convex analysis, and scientific computing.
Madeleine Udell is a PhD candidate in Computational and Mathematical Engineering at Stanford University. She has served as a TA and as an instructor for EE364a at Stanford. Her research applies convex optimization techniques to a variety of non-convex applications, including sigmoidal programming, biconvex optimization, and structured reinforcement learning problems, with applications to political science, biology, and operations research.
Kai1986 completed this course, spending 10 hours a week on it and found the course difficulty to be medium.
the cvx101 is a very good course that displays the topic of the mathematical convex programming from a very practital point of view with a lot of very interesting applications and showing how to solve them. I suggest this course mainly to people that have at least a Bachelor degree in engineering field
Anonymous is taking this course right now.
This is simply outstanding. I don't know how I missed this course before. It has to be one of the top mathematics courses I've ever taken. The conversational tone of the professor, makes a hard topic look very easy. His humor keeps you awake :-) This is very well worth a go through, even though it is an archived course. It's not for the certificate but for the knowledge. The professor and his intuitions indicate an indepth understanding of the field, which he is able to transfer to students.
The derivations are slightly glossed over, but note that this is an introductory course.
Anonymous completed this course.
Very helpful and enthusiastic instructors. The homeworks are well designed with lots of interesting examples. Highly recommend to anyone who wants to excel this topic.
Anonymous completed this course.
This is an amazing course. Teaches the theory behind and to solve numerically convex optimization problems. Hws are solved writing progams in Matlab making use of the cvx library (developed by Prof. Boyd among others) which make programming convex optimization problem very natural and easy
Zahra completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
I audited the course. The course covers most of the recent and practical subjects in optimization and it was very helpful for me. Thanks to Prof. Boyd.