Convex and Nonsmooth Optimization

Multi-cut stochastic approximation methods for solving stochastic convex composite optimization

  • Jiaming Liang
  • Honghao Zhang
  • Renato D.C. Monteiro
    • Categories Convex Optimization, Stochastic Programming Tags , , ,

    The development of a multi-cut stochastic approximation (SA) method for solving stochastic convex composite optimization (SCCO) problems has remained an open challenge. The difficulty arises from the fact that the stochastic multi-cut model, constructed as the pointwise maximum of individual stochastic linearizations, provides a biased estimate of the objective function, with the error being uncontrollable. … Read more

    Inverse Optimization via Learning Feasible Regions

  • Ke Ren
  • Peyman Mohajerin Esfahani
  • Angelos Georghiou
    • Categories (Mixed) Integer Linear Programming, Nonsmooth Optimization, Robust Optimization Tags , , ,

    We study inverse optimization (IO), where the goal is to use a parametric optimization program as the hypothesis class to infer relationships between input-decision pairs. Most of the literature focuses on learning only the objective function, as learning the constraint function (i.e., feasible regions) leads to nonconvex training programs. Motivated by this, we focus on … Read more

    cuHALLaR: A GPU accelerated low-rank augmented Lagrangian method for large-scale semidefinite programming

  • Jacob Aguirre
  • Diego Cifuentes
  • Vincent Guigues
  • Renato D.C. Monteiro
  • Victor Hugo Nascimento
  • Arnesh Sujanani
    • Categories Convex and Nonsmooth Optimization, Linear, Cone and Semidefinite Programming, Optimization Software and Modeling Systems Tags , , , , ,

    This paper introduces cuHALLaR, a GPU-accelerated implementation of the HALLaR method proposed in Monteiro et al. 2024 for solving large-scale semidefinite programming (SDP) problems. We demonstrate how our Julia-based implementation efficiently uses GPU parallelism through optimization of simple, but key, operations, including linear maps, adjoints, and gradient evaluations. Extensive numerical experiments across three problem classes—maximum … Read more

    Subgradient Regularization: A Descent-Oriented Subgradient Method for Nonsmooth Optimization

  • Hanyang Li
  • Ying Cui
    • Categories Convex and Nonsmooth Optimization, Nonlinear Optimization, Nonsmooth Optimization Tags , , ,

    In nonsmooth optimization, a negative subgradient is not necessarily a descent direction, making the design of convergent descent methods based on zeroth-order and first-order information a challenging task. The well-studied bundle methods and gradient sampling algorithms construct descent directions by aggregating subgradients at nearby points in seemingly different ways, and are often complicated or lack … Read more

    An Adaptive and Parameter-Free Nesterov’s Accelerated Gradient Method for Convex Optimization

  • Jaewook J. Suh
  • Shiqian Ma
    • Categories Convex Optimization Tags , , , ,

    We propose AdaNAG, an adaptive accelerated gradient method based on Nesterov’s accelerated gradient method. AdaNAG is line-search-free, parameter-free, and achieves the accelerated convergence rates \( f(x_k) – f_\star = \mathcal{O}\left(1/k^2\right) \) and \( \min_{i\in\left\{1,\dots, k\right\}} \|\nabla f(x_i)\|^2 = \mathcal{O}\left(1/k^3\right) \) for \( L \)-smooth convex function \( f \). We provide a Lyapunov analysis for … Read more

    Steepest descent method using novel adaptive stepsizes for unconstrained nonlinear multiobjective programming

  • Pham Thi Hoai
    • Categories Convex Optimization, Multi-Criteria Optimization, Nonlinear Optimization Tags , , , ,

    We propose new adaptive strategies to compute stepsizes for the steepest descent method to solve unconstrained nonlinear multiobjective optimization problems without employing any linesearch procedure. The resulting algorithms can be applied to a wide class of nonconvex unconstrained multi-criteria optimization problems satisfying a global Lipschitz continuity condition imposed on the gradients of all objectives. In … Read more

    A Symmetric Primal-Dual method with two extrapolation steps for Composite Convex Optimization

  • Feng Ma
    • Categories Convex Optimization Tags , , ,

    Symmetry is a recurring feature in algorithms for monotone operator theory and convex optimization, particularly in problems involving the sum of two operators, as exemplified by the Peaceman–Rachford splitting scheme. However, in more general settings—such as composite optimization problems with three convex functions or structured convex-concave saddle-point formulations—existing algorithms often exhibit inherent asymmetry. In particular, … Read more

    A double-accelerated proximal augmented Lagrangian method with applications in signal reconstruction

  • Jianchao Bai
    • Categories Convex and Nonsmooth Optimization Tags , , ,

    The Augmented Lagrangian Method (ALM), firstly proposed in 1969, remains a vital framework in large-scale constrained optimization. This paper addresses a linearly constrained composite convex minimization problem and presents a general proximal ALM that incorporates both Nesterov acceleration and relaxed acceleration, while enjoying indefinite proximal terms. Under mild assumptions (potentially without requiring prior knowledge of … Read more

    Negative Stepsizes Make Gradient-Descent-Ascent Converge

  • Henry Shugart
  • Jason M. Altschuler
    • Categories Convex Optimization

    Efficient computation of min-max problems is a central question in optimization, learning, games, and controls. Arguably the most natural algorithm is gradient-descent-ascent (GDA). However, since the 1970s, conventional wisdom has argued that GDA fails to converge even on simple problems. This failure spurred an extensive literature on modifying GDA with additional building blocks such as … Read more

    On the Acceleration of Proximal Bundle Methods

  • David Fersztand
  • Xu Andy Sun
    • Categories Convex and Nonsmooth Optimization, Convex Optimization, Nonsmooth Optimization Tags , , ,

    The proximal bundle method (PBM) is a fundamental and computationally effective algorithm for solving nonsmooth optimization problems. In this paper, we present the first variant of the PBM for smooth objectives, achieving an accelerated convergence rate of \(\frac{1}{\sqrt{\epsilon}}\log(\frac{1}{\epsilon})\), where \(\epsilon\) is the desired accuracy. Our approach addresses an open question regarding the convergence guarantee of … Read more