Albert S. Berahas

Modified Line Search Sequential Quadratic Methods for Equality-Constrained Optimization with Unified Global and Local Convergence Guarantees

  • Albert S. Berahas
  • Raghu Bollapragada
  • Jiahao Shi
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Other Topics Tags , ,

    In this paper, we propose a method that has foundations in the line search sequential quadratic programming paradigm for solving general nonlinear equality constrained optimization problems. The method employs a carefully designed modified line search strategy that utilizes second-order information of both the objective and constraint functions, as required, to mitigate the Maratos effect. Contrary … Read more

    Adaptive Consensus: A network pruning approach for decentralized optimization

  • Suhail Mohmad Shah
  • Albert S. Berahas
  • Raghu Bollapragada
    • Categories Convex Optimization, Network Optimization, Nonlinear Optimization Tags , ,

    We consider network-based decentralized optimization problems, where each node in the network possesses a local function and the objective is to collectively attain a consensus solution that minimizes the sum of all the local functions. A major challenge in decentralized optimization is the reliance on communication which remains a considerable bottleneck in many applications. To … Read more

    Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized Optimization

  • Albert S. Berahas
  • Raghu Bollapragada
  • Shagun Gupta
    • Categories Convex Optimization, Nonlinear Optimization, Optimization in Data Science Tags , , , ,

    Gradient tracking methods have emerged as one of the most popular approaches for solving decentralized optimization problems over networks. In this setting, each node in the network has a portion of the global objective function, and the goal is to collectively optimize this function. At every iteration, gradient tracking methods perform two operations (steps): (1) … Read more

    An Adaptive Sampling Sequential Quadratic Programming Method for Equality Constrained Stochastic Optimization

  • Albert S. Berahas
  • Raghu Bollapragada
  • Baoyu Zhou
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization Tags , , ,

    This paper presents a methodology for using varying sample sizes in sequential quadratic programming (SQP) methods for solving equality constrained stochastic optimization problems. The first part of the paper deals with the delicate issue of dynamic sample selection in the evaluation of the gradient in conjunction with inexact solutions to the SQP subproblems. Under reasonable … Read more

    First- and Second-Order High Probability Complexity Bounds for Trust-Region Methods with Noisy Oracles

  • Liyuan Cao
  • Albert S. Berahas
  • Katya Scheinberg
    • Categories Nonlinear Optimization, Unconstrained Optimization Tags , , ,

    In this paper, we present convergence guarantees for a modified trust-region method designed for minimizing objective functions whose value is computed with noise and for which gradient and Hessian estimates are inexact and possibly random. In order to account for the noise, the method utilizes a relaxed step acceptance criterion and a cautious trust-region radius … Read more

    Accelerating Stochastic Sequential Quadratic Programming for Equality Constrained Optimization using Predictive Variance Reduction

  • Albert S. Berahas
  • Jiahao Shi
  • Zihong Yi
  • Baoyu Zhou
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization Tags , , ,

    In this paper, we propose a stochastic variance reduction method for solving equality constrained optimization problems. Specifically, we develop a method based on the sequential quadratic programming paradigm that utilizes gradient approximations via predictive variance reduction techniques. Under reasonable assumptions, we prove that a measure of first-order stationarity evaluated at the iterates generated by our … Read more

    Full-low evaluation methods for derivative-free optimization

  • Albert S. Berahas
  • Luis Nunes Vicente
  • Oumaima Sohab
    • Categories Applications - Science and Engineering, Convex and Nonsmooth Optimization, Nonlinear Optimization Tags , , ,

    We propose a new class of rigorous methods for derivative-free optimization with the aim of delivering efficient and robust numerical performance for functions of all types, from smooth to non-smooth, and under different noise regimes. To this end, we have developed Full-Low Evaluation methods, organized around two main types of iterations. The first iteration type … Read more

    A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear Equality Constrained Optimization with Rank-Deficient Jacobians

  • Albert S. Berahas
  • Frank E. Curtis
  • Michael J. O'Neill
  • Daniel P. Robinson
    • Categories Constrained Nonlinear Optimization, Statistics Tags , , , ,

    A sequential quadratic optimization algorithm is proposed for solving smooth nonlinear equality constrained optimization problems in which the objective function is defined by an expectation of a stochastic function. The algorithmic structure of the proposed method is based on a step decomposition strategy that is known in the literature to be widely effective in practice, … Read more

    Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization

  • Albert S. Berahas
  • Frank E. Curtis
  • Daniel P. Robinson
  • Baoyu Zhou
    • Categories Nonlinear Optimization Tags , ,

    Sequential quadratic optimization algorithms are proposed for solving smooth nonlinear optimization problems with equality constraints. The main focus is an algorithm proposed for the case when the constraint functions are deterministic, and constraint function and derivative values can be computed explicitly, but the objective function is stochastic. It is assumed in this setting that it … Read more

    A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization

  • Albert S. Berahas
  • Liyuan Cao
  • Katya Scheinberg
  • Krzysztof Choromanski
    • Categories Nonlinear Optimization, Unconstrained Optimization Tags , , , ,

    In this paper, we analyze several methods for approximating gradients of noisy functions using only function values. These methods include finite differences, linear interpolation, Gaussian smoothing and smoothing on a unit sphere. The methods differ in the number of functions sampled, the choice of the sample points, and the way in which the gradient approximations … Read more