Month: January 2025

Newtonian Methods with Wolfe Linesearch in Nonsmooth Optimization and Machine Learning

  • Miantao Chao
  • Boris S. Mordukhovich
  • Zijian Shi
  • Jin Zhang
    • Categories Nonlinear Optimization Tags , , , , , ,

    This paper introduces and develops coderivative-based Newton methods with Wolfe linesearch conditions to solve various classes of problems in nonsmooth optimization and machine learning. We first propose a generalized regularized Newton method with Wolfe linesearch (GRNM-W) for unconstrained $C^{1,1}$ minimization problems (which are second-order nonsmooth) and establish global as well as local superlinear convergence of … Read more

    Mathematical models for the kidney exchange problem with reserve arcs

  • Maxence Delorme
  • David Manlove
    • Categories Combinatorial Optimization, Integer Programming Tags , , , ,

    The kidney exchange problem with reserve arcs (KEP-RA) is an extension of the classical kidney exchange problem in which one is allowed to select in the solution a limited number of arcs that do not belong to the compatibility graph. This problem is motivated by recent breakthroughs in the field of kidney transplantation involving immunosuppressants … Read more

    Pareto sensitivity, most-changing sub-fronts, and knee solutions

  • Tommaso Giovannelli
  • Marcos M. Raimundo
  • Luis Nunes Vicente
    • Categories Multi-Criteria Optimization Tags , , ,

    When dealing with a multi-objective optimization problem, obtaining a comprehensive representation of the Pareto front can be computationally expensive. Furthermore, identifying the most representative Pareto solutions can be difficult and sometimes ambiguous. A popular selection are the so-called Pareto knee solutions, where a small improvement in any objective leads to a large deterioration in at … Read more

    Pessimistic bilevel optimization approach for decision-focused learning

  • Diego Jiménez
  • Bernardo K. Pagnoncelli
  • Hande Yaman
    • Categories 0-1 Programming, Stochastic Programming Tags , ,

    The recent interest in contextual optimization problems, where randomness is associated with side information, has led to two primary strategies for formulation and solution. The first, estimate-then-optimize, separates the estimation of the problem’s parameters from the optimization process. The second, decision-focused optimization, integrates the optimization problem’s structure directly into the prediction procedure. In this work, … Read more

    Local Upper Bounds for Polyhedral Cones

  • Gabriele Eichfelder
  • Firdevs Ulus
    • Categories Multi-Criteria Optimization Tags , , ,

    The concept of local upper bounds plays an important role for numerical algorithms in nonconvex, integer, and mixed-integer multiobjective optimization with respect to the componentwise partial ordering, that is, where the ordering cone is the nonnegative orthant. In this paper, we answer the question on whether and how this concept can be extended to arbitrary … Read more

    A Decomposition Framework for Nonlinear Nonconvex Two-Stage Optimization

  • Yuchen Lou
  • Andreas Wächter
  • Xinyi Luo
  • Ermin Wei
    • Categories Nonlinear Optimization

    We propose a new decomposition framework for continuous nonlinear constrained two-stage optimization, where both first- and second-stage problems can be nonconvex. A smoothing technique based on an interior-point formulation renders the optimal solution of the second-stage problem differentiable with respect to the first-stage parameters. As a consequence, efficient off-the-shelf optimization packages can be utilized. We … Read more

    Fully Adaptive Zeroth-Order Method for Minimizing Functions with Compressible Gradients

  • Daniel McKenzie
  • Geovani Nunes Grapiglia
    • Categories Unconstrained Optimization Tags , ,

    We propose an adaptive zeroth-order method for minimizing differentiable functions with L-Lipschitz continuous gradients. The method is designed to take advantage of the eventual compressibility of the gradient of the objective function, but it does not require knowledge of the approximate sparsity level s or the Lipschitz constant L of the gradient. We show that … Read more

    Variable metric proximal stochastic gradient methods with additional sampling

  • Ilaria Trombini
  • Federica Porta
  • Nataša Krklec Jerinkić
  • Valeria Ruggiero
    • Categories Stochastic Approaches Tags , , ,

    Regularized empirical risk minimization problems arise in a variety of applications, including machine learning, signal processing, and image processing. Proximal stochastic gradient algorithms are a standard approach to solve these problems due to their low computational cost per iteration and a relatively simple implementation. This paper introduces a class of proximal stochastic gradient methods built … Read more

    Descent Scheme for a Class of Bilevel Programming Problems

  • Bhuvnesh Khatana
    • Categories Approximation Algorithms, Combinatorial Optimization, Complementarity and Variational Inequalities, Convex and Nonsmooth Optimization, Nonlinear Optimization Tags , , ,

    In this paper, a class of bilevel programming problems is studied, in which the lower level is a quadratic programming problem, and the upper level problem consists of a nonlinear objective function with coupling constraints. An iterative process is developed to generate a sequence of points, which converges to the solution of this problem. In … Read more