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Event Details

  • Monday, November 6, 2017
  • 13:30 - 14:00

Optimization Techniques for Large Scale Data Science Problems

The alternating direction method of multipliers (ADMM) is widely used to solve large-scale linearly constrained optimization problems, convex or nonconvex, which arise in numerous data science applications. However there is a general lack of theoretical understanding of the algorithm when the objective function is nonconvex or if the algorithm is implemented distributedly. In this talk we discuss the design and convergence of ADMM type algorithms for solving nonconvex optimization problems and if distributed asynchronous implementation is required.