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[02238] A Generalized Multi-Parameterized Proximal Point Algorithm

  • Session Time & Room : 4D (Aug.24, 15:30-17:10) @D408
  • Type : Contributed Talk
  • Abstract : Proximal point algorithm (PPA) is an important class of methods for solving convex problems. In this article, a generalized multi-parameterized proximal point algorithm (GM-PPA) is developed to solve linearly constrained convex optimization problems. Compared with existing PPAs, the proposed method is much more general as well as flexible. Many existing PPAs reduce to our algorithm when some newly introduced parameters are fixed. Furthermore, by appropriately setting the algorithm parameters, our GM-PPA is potentially able to reduce the computation time and iteration number whereas the convergence result can still be guaranteed. Numerical experiments on synthetic problem are conducted to demonstrate the efficiency of our algorithm.
  • Classification : 90C25, 90C30
  • Format : Talk at Waseda University
  • Author(s) :
    • Yuan Shen ( Nanjing University of Finance & Economics)