ORIE 6326

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.

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Enrollment Information
Syllabi: none
  •   Combined with: ORIE 6326

  • 3 Credits Stdnt Opt

  • 18065ORIE 6326  LEC 001

Enrollment Information
Syllabi: none
  •   Combined with: ORIE 6326

  • 3 Credits Stdnt Opt

  • 18073ORIE 6326  LEC 031

  • Taught in NYC. Enrollment limited to: Cornell Tech students. Taught via distance learning, streamed from Ithaca.