- Schedule of Classes - June 18, 2017 7:14PM EDT
- Course Catalog - June 14, 2017 7:15PM EDT
Course information provided by the Courses of Study 2016-2017.
Convex optimization generalizes least-squares, linear and quadratic programming, and semidefinite programming, and forms the basis of many methods for non-convex optimization. This course focuses on recognizing and solving convex optimization problems that arise in applications, and introduces a few algorithms for convex optimization. Topics include: 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. Algorithms: interior-point, subgradient, proximal gradient, splitting methods such as ADMM. Applications to statistics and machine learning, signal processing, control and mechanical engineering, and finance.
When Offered Spring.
Regular Academic Session. Combined with: ORIE 6326
Credits and Grading Basis
3 Credits Stdnt Opt(Letter or S/U grades)
Class Number & Section Details
- MW Cornell Tech
Instruction Mode: Distance Learning - WWW
Taught in NYC. Enrollment limited to: Cornell Tech students. Taught via distance learning, streamed from Ithaca.
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