Ruobing Chen

Randomized Derivative-Free Optimization of Noisy Convex Functions

  • Ruobing Chen
  • Stefan M. Wild
    • Categories Stochastic Programming, Unconstrained Optimization Tags , , ,

    We propose STARS, a randomized derivative-free algorithm for unconstrained optimization when the function evaluations are contaminated with random noise. STARS takes dynamic, noise-adjusted smoothing step-sizes that minimize the least-squares error between the true directional derivative of a noisy function and its finite difference approximation. We provide a convergence rate analysis of STARS for solving convex … Read more

    Stochastic Optimization using a Trust-Region Method and Random Models

  • Ruobing Chen
  • Matt Menickelly
  • Katya Scheinberg
    • Categories Stochastic Programming, Unconstrained Optimization Tags , , ,

    In this paper, we propose and analyze a trust-region model-based algorithm for solving unconstrained stochastic optimization problems. Our framework utilizes random models of an objective function $f(x)$, obtained from stochastic observations of the function or its gradient. Our method also utilizes estimates of function values to gauge progress that is being made. The convergence analysis … Read more