sublinear convergence

Doubly stochastic primal dual splitting algorithm with variance reduction for saddle point problems

  • Dimitri Papadimitriou
    • Categories Convex Optimization, Nonlinear Optimization, Unconstrained Optimization Tags , , , , ,

    The (structured) saddle-point problem involving the infimal convolution in real Hilbert spaces finds applicability in many applied mathematics disciplines. For this purpose, we develop a stochastic primal-dual splitting (PDS) algorithm with loopless variance-reduction (VR) for solving this generic problem. A PDS algorithm aims to overcome the well-known shortcomings of common splitting methods by solving the … Read more

    Gradient methods for convex minimization: better rates under weaker conditions

  • Wotao Yin
  • Hui Zhang
    • Categories Convex Optimization Tags , , , , , , ,

    The convergence behavior of gradient methods for minimizing convex differentiable functions is one of the core questions in convex optimization. This paper shows that their well-known complexities can be achieved under conditions weaker than the commonly assumed ones. We relax the common gradient Lipschitz-continuity condition and strong convexity condition to ones that hold only over … Read more