Convex and Nonsmooth Optimization

Primal-dual extrapolation methods for monotone inclusions under local Lipschitz continuity with applications to variational inequality, conic constrained saddle point, and convex conic optimization problems

  • Zhaosong Lu
  • Sanyou Mei
    • Categories Convex and Nonsmooth Optimization Tags , , , , , , , ,

    In this paper we consider a class of structured monotone inclusion (MI) problems that consist of finding a zero in the sum of two monotone operators, in which one is maximal monotone while another is locally Lipschitz continuous. In particular, we first propose a primal-dual extrapolation (PDE) method for solving a structured strongly MI problem … Read more

    Accelerated first-order methods for convex optimization with locally Lipschitz continuous gradient

  • Zhaosong Lu
  • Sanyou Mei
    • Categories Convex and Nonsmooth Optimization Tags , , , , , ,

    In this paper we develop accelerated first-order methods for convex optimization with locally Lipschitz continuous gradient (LLCG), which is beyond the well-studied class of convex optimization with Lipschitz continuous gradient. In particular, we first consider unconstrained convex optimization with LLCG and propose accelerated proximal gradient (APG) methods for solving it. The proposed APG methods are … Read more

    Multi-fidelity robust controller design with gradient sampling

  • Steffen W. R. Werner
  • Michael L. Overton
  • Benjamin Peherstorfer
    • Categories Control Applications, Nonsmooth Optimization Tags , , , ,

    Robust controllers that stabilize dynamical systems even under disturbances and noise are often formulated as solutions of nonsmooth, nonconvex optimization problems. While methods such as gradient sampling can handle the nonconvexity and nonsmoothness, the costs of evaluating the objective function may be substantial, making robust control challenging for dynamical systems with high-dimensional state spaces. In … Read more

    Wasserstein Logistic Regression with Mixed Features

  • Aras Selvi
  • Mohammad Reza Belbasi
  • Martin Haugh
  • Wolfram Wiesemann
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming, Robust Optimization Tags , ,

    Recent work has leveraged the popular distributionally robust optimization paradigm to combat overfitting in classical logistic regression. While the resulting classification scheme displays a promising performance in numerical experiments, it is inherently limited to numerical features. In this paper, we show that distributionally robust logistic regression with mixed (i.e., numerical and categorical) features, despite amounting … Read more

    Complexity-optimal and Parameter-free First-order Methods for Finding Stationary Points of Composite Optimization Problems

  • Weiwei Kong
    • Categories Convex and Nonsmooth Optimization, Nonlinear Optimization Tags , , , , , ,

    This paper develops and analyzes an accelerated proximal descent method for finding stationary points of nonconvex composite optimization problems. The objective function is of the form f+h where h is a proper closed convex function, f is a differentiable function on the domain of h, and ∇f is Lipschitz continuous on the domain of h. … Read more

    Accelerating Frank-Wolfe via Averaging Step Directions

  • Zhaoyue Chen
  • Yifan Sun
    • Categories Convex Optimization Tags ,

    The Frank-Wolfe method is a popular method in sparse constrained optimization, due to its fast per-iteration complexity. However, the tradeoff is that its worst case global convergence is comparatively slow, and importantly, is fundamentally slower than its flow rate–that is to say, the convergence rate is throttled by discretization error. In this work, we consider … Read more

    A Proximal Gradient Method for Multi-objective Optimization Problems Using Bregman Functions

  • Md Abu Talhamainuddin Ansary
  • Joydeep Dutta
    • Categories Convex Optimization, Multi-Criteria Optimization, Nonsmooth Optimization Tags , , , ,

    In this paper, a globally convergent proximal gradient method is developed for convex multi-objective optimization problems using Bregman distance. The proposed method is free from any kind of a priori chosen parameters or ordering information of objective functions. At every iteration of the proposed method, a subproblem is solved to find a descent direction. This … Read more

    Integral Global Optimality Conditions and an Algorithm for Multiobjective Problems

  • Everton Silva
  • Elizabeth Wegner Karas
  • Lucelina Santos
    • Categories Multi-Criteria Optimization, Nonlinear Optimization, Nonsmooth Optimization Tags , , , ,

    In this work, we propose integral global optimality conditions for multiobjective problems not necessarily differentiable. The integral characterization, already known for single objective problems, are extended to multiobjective problems by weighted sum and Chebyshev weighted scalarizations. Using this last scalarization, we propose an algorithm for obtaining an approximation of the weak Pareto front whose effectiveness … Read more

    Identifiability, the KL property in metric spaces, and subgradient curves

  • Adrian Lewis
  • Tonghua Tian
    • Categories Nonsmooth Optimization Tags , , , , ,

    Identifiability, and the closely related idea of partial smoothness, unify classical active set methods and more general notions of solution structure. Diverse optimization algorithms generate iterates in discrete time that are eventually confined to identifiable sets. We present two fresh perspectives on identifiability. The first distills the notion to a simple metric property, applicable not … Read more

    Duality in convex stochastic optimization

  • Teemu Pennanen
  • Ari-Pekka Perkkiö
    • Categories Convex Optimization, Infinite Dimensional Optimization, Stochastic Programming Tags , , ,

    This paper studies duality and optimality conditions in general convex stochastic optimization problems introduced by Rockafellar and Wets in \cite{rw76}. We derive an explicit dual problem in terms of two dual variables, one of which is the shadow price of information while the other one gives the marginal cost of a perturbation much like in … Read more