Ji Liu

Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity

  • Ji Liu
  • Shiqian Ma
  • Conghui Tan
  • Tong Zhang
    • Categories Convex and Nonsmooth Optimization

    Regularized empirical risk minimization problem with linear predictor appears frequently in machine learning. In this paper, we propose a new stochastic primal-dual method to solve this class of problems. Different from existing methods, our proposed methods only require O(1) operations in each iteration. We also develop a variance-reduction variant of the algorithm that converges linearly. … Read more

    Asynchronous Stochastic Coordinate Descent: Parallelism and Convergence Properties

  • Ji Liu
  • Stephen Wright
    • Categories Convex and Nonsmooth Optimization Tags ,

    We describe an asynchronous parallel stochastic proximal coordinate descent algorithm for minimizing a composite objective function, which consists of a smooth convex function plus a separable convex function. In contrast to previous analyses, our model of asynchronous computation accounts for the fact that components of the unknown vector may be written by some cores simultaneously … Read more