Sobre este cursoOmitir Sobre este curso
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.
Lo que aprenderásOmitir Lo que aprenderás
- How to recognize convex optimization problems that arise in applications.
- How to present the basic theory of such problems, concentrating on results that are useful in computation.
- A thorough understanding of how such problems are solved, and some experience in solving them.
- The background required to use the methods in your own research work or applications.
Conoce a tus instructores
Do I need to buy the textbook?
No, the textbook is available online at http://www.stanford.edu/~boyd/cvxbook/.
Do we need to purchase a Matlab license to take this course?
A Matlab licence or access is NOT included in this course. Trial versions of Matlab may be available at https://www.mathworks.com/
How hard is this class?
This is an advanced class, targeting MS and PhD level students in mathematically sophisticated fields.
¿Quién puede hacer este curso?
Lamentablemente, las personas de uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.