Mingyi Hong

Problem-Parameter-Free Decentralized Nonconvex Stochastic Optimization

  • Jiaxiang Li
  • Xuxing Chen
  • Shiqian Ma
  • Mingyi Hong
    • Categories Nonlinear Optimization, Stochastic Programming

    Existing decentralized algorithms usually require knowledge of problem parameters for updating local iterates. For example, the hyperparameters (such as learning rate) usually require the knowledge of Lipschitz constant of the global gradient or topological information of the communication networks, which are usually not accessible in practice. In this paper, we propose D-NASA, the first algorithm … Read more

    A Block Successive Upper Bound Minimization Method of Multipliers for Linearly Constrained Convex Optimization

  • Tsung-Hui Chang
  • Zhi-Quan Luo
  • Shiqian Ma
  • Mingyi Hong
  • Meisam Razaviyayn
  • Xiangfeng Wang
    • Categories Convex Optimization, Nonsmooth Optimization Tags , ,

    Consider the problem of minimizing the sum of a smooth convex function and a separable nonsmooth convex function subject to linear coupling constraints. Problems of this form arise in many contemporary applications including signal processing, wireless networking and smart grid provisioning. Motivated by the huge size of these applications, we propose a new class of … Read more

    Alternating direction method of multipliers for sparse zero-variance discriminant analysis and principal component analysis

  • Brendan Ames
  • Mingyi Hong
    • Categories Data-Mining, Statistics

    We consider the task of classification in the high-dimensional setting where the number of features of the given data is significantly greater than the number of observations. To accomplish this task, we propose sparse zero-variance discriminant analysis (SZVD) as a method for simultaneouslyperforming linear discriminant analysis and feature selection on high-dimensional data. This method combines … Read more