Month: March 2026

Finding Minimal Discretizations in Dynamic Discretization Discovery for Continuous-Time Service Network Design

  • Tom Wüllner
  • Alexander Helber
    • Categories Combinatorial Optimization, Network Optimization, Transportation Tags , , ,

    The dynamic discretization discovery framework is a powerful tool for solving network design problems with a temporal component by iteratively refining a time-discretized model. Existing approaches refine the time discretization in ways that guarantee eventual termination. However, refinement choices are not unique, and better choices can yield smaller and easier-to-solve time-discretized models. We pose the … Read more

    Normal cones and subdifferentials at infinity for convex analysis and optimization

  • Thanh-Hung Pham
    • Categories Convex Optimization Tags , , ,

    Motivated by recent developments, this paper further investigates normal cones and subdifferentials at infinity within the framework of convex analysis. We establish fundamental properties of these constructions and derive basic calculus rules. The obtained results extend and refine existing concepts in variational analysis and nonsmooth optimization, providing new insights into the asymptotic structure of functions … Read more

    Compact Lifted Relaxations for Low-Rank Optimization

  • Ryan Cory-Wright
  • Jean Pauphilet
    • Categories Semi-definite Programming Tags , ,

    We develop tractable convex relaxations for rank-constrained quadratic optimization problems over $n \times m$ matrices, a setting for which tractable relaxations are typically only available when the objective or constraints admit spectral (permutation-invariant) structure. We derive lifted semidefinite relaxations that do not require such spectral terms. Although a direct lifting introduces a large semidefinite constraint … Read more

    Data-driven Policies For Two-stage Stochastic Linear Programs

  • chhavisharma
  • Harsha Gangammanavar
    • Categories Linear Programming, Optimization in Data Science, Stochastic Programming Tags , , ,

    A stochastic program typically involves several parameters, including deterministic first-stage parameters and stochastic second-stage elements that serve as input data. These programs are re-solved whenever any input parameter changes. However, in practical applications, quick decision-making is necessary, and solving a stochastic program from scratch for every change in input data can be computationally costly. This … Read more

    Preconditioned Proximal Gradient Methods with Conjugate Momentum: A Subspace Perspective

  • Jian Chen
  • Xinmin Yang
    • Categories Constrained Nonlinear Optimization, Convex Optimization, Nonsmooth Optimization Tags , , ,

    In this paper, we propose a descent method for composite optimization problems with linear operators. Specifically, we first design a structure-exploiting preconditioner tailored to the linear operator so that the resulting preconditioned proximal subproblem admits a closed-form solution through its dual formulation. However, such a structure-driven preconditioner may be poorly aligned with the local curvature … Read more

    Strong convergence, perturbation resilience and superiorization of Generalized Modular String-Averaging with infinitely many input operators

  • Kay Barshad
  • Yair Censor
    • Categories Constrained Nonlinear Optimization, Convex Optimization Tags , , , , , , , , ,

    We study the strong convergence and bounded perturbation resilience of iterative algorithms based on the Generalized Modular String-Averaging (GMSA) procedure for infinite sequences of input operators under a general admissible control. These methods address a variety of feasibility-seeking problems in real Hilbert spaces, including the common fixed point problem and the convex feasibility problem. In … Read more

    Optimal Transport on Lie Group Orbits

  • Bahar Taskesen
    • Categories Infinite Dimensional Optimization, Other Topics

    In its most general form, the optimal transport problem is an infinite-dimensional optimization problem, yet certain notable instances admit closed-form solutions. We identify the common source of this tractability as symmetry and formalize it using Lie group theory. Fixing a Lie group action on the outcome space and a reference distribution, we study optimal transport … Read more

    Modeling Network Congestion under Demand Uncertainty Using Wardrop Principles

  • Yasmine Beck
  • Francesca Giancola
  • Ivana Ljubić
  • Sara Mattia
    • Categories (Mixed) Integer Nonlinear Programming, Bilevel Optimization, Global Optimization Tags , , ,

    Motivated by the need for reliable traffic management under fluctuating travel demand, we study the problem of determining the worst-case congestion in a multi-commodity traffic network subject to demand uncertainty. To this end, we stress-test a given network by identifying demand realizations and corresponding travelers’ route choices that maximize congestion. The users of the traffic … Read more

    Solving Chance Constrained Programs via a Penalty based Difference of Convex Approach

  • ZhipingLi
  • Nan Jiang
  • Rujun Jiang
    • Categories Nonlinear Optimization, Stochastic Programming Tags , , ,

    We develop two penalty based difference of convex (DC) algorithms for solving chance constrained programs. First, leveraging a rank-based DC decomposition of the chance constraint, we propose a proximal penalty based DC algorithm in the primal space that does not require a feasible initialization. Second, to improve numerical stability in the general nonlinear settings, we … Read more

    Folding Mixed-Integer Linear Programs and Reflection Symmetries

  • Rolf van der Hulst
    • Categories (Mixed) Integer Linear Programming, Linear Programming Tags , , , ,

    For mixed-integer linear programming and linear programming it is well known that symmetries can have a negative impact on the performance of branch-and-bound and linear optimization algorithms. A common strategy to handle symmetries in linear programs is to reduce the dimension of the linear program by aggregating symmetric variables and solving a linear program of … Read more