Discovering Heuristics with Large Language Models (LLMs) for Mixed-Integer Programs: Single-Machine Scheduling

  • I. Esra Buyuktahtakin Toy
    • Categories (Mixed) Integer Linear Programming, Combinatorial Optimization, Meta Heuristics Tags , , , , , , ,

    TitleDiscovering Heuristics with Large Language Models (LLMs) for Mixed-Integer Programs: Single-Machine Scheduling Authorsİbrahim Oğuz Çetinkaya^1; İ. Esra Büyüktahtakın^1*; Parshin Shojaee^2; Chandan K. Reddy^2 Affiliations^1 Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA^2 Department of Computer Science, Virginia Tech, Arlington, VA, USA Abstract: Our study contributes to the scheduling and combinatorial optimization … Read more

    Optimizing Expeditionary Logistics: Dynamic Discretization for Fleet Management

  • Haden Boone
  • Mathieu Dahan
    • Categories (Mixed) Integer Linear Programming, Network Optimization, Production and Logistics Tags , , ,

    We introduce the Expeditionary Logistics Network Design Problem (ELNDP), a new formulation for operational-level planning in expeditionary environments where multi-modal vehicle coordination is critical and penalties for unmet demand dominate transportation costs. ELNDP extends the classical Scheduled Service Network Design Problem by incorporating flexible commodity sourcing and heterogeneous vehicle capabilities, both essential in military logistics. … Read more

    Adaptive Conditional Gradient Descent

  • Abbas Khademi
  • Antonio Silveti-Falls
    • Categories Nonlinear Optimization, Optimization in Data Science Tags , , , , , , ,

    Selecting an effective step-size is a fundamental challenge in first-order optimization, especially for problems with non-Euclidean geometries. This paper presents a novel adaptive step-size strategy for optimization algorithms that rely on linear minimization oracles, as used in the Conditional Gradient or non-Euclidean Normalized Steepest Descent algorithms. Using a simple heuristic to estimate a local Lipschitz … Read more

    Counterfactual Explanations for Integer Optimization Problems

  • Felix Engelhardt
  • Jannis Kurtz
  • Ş. İlker Birbil
  • Ted K. Ralphs
    • Categories Bilevel Optimization, Integer Programming Tags , , ,

    Counterfactual explanations (CEs) offer a human-understandable way to explain decisions by identifying specific changes to the input parameters of a base or present model that would lead to a desired change in the outcome. For optimization models, CEs have primarily been studied in limited contexts and little research has been done on CEs for general … Read more

    Towards robust optimal control of chromatographic separation processes with controlled flow reversal

  • Dominik H. Cebulla
  • Christian Kirches
  • Andreas Potschka
    • Categories Chemical Engineering, Robust Optimization, Systems governed by Differential Equations Optimization Tags , ,

    Column liquid chromatography is an important technique applied in the production of biopharmaceuticals, specifically for the separation of biological macromolecules such as proteins. When setting up process conditions, it is crucial that the purity of the product is sufficiently high, even in the presence of perturbations in the process conditions, e.g., altered buffer salt concentrations. … Read more

    A Heuristic for Complementarity Problems Using Difference of Convex Functions

  • Steven A. Gabriel
  • Dominic C. Flocco
  • Trine Krogh Boomsma
  • Miguel A. Lejeune
  • Martin Schmidt
    • Categories Complementarity and Variational Inequalities Tags , , , ,

    We present a new difference of convex functions algorithm (DCA) for solving linear and nonlinear mixed complementarity problems (MCPs). The approach is based on the reformulation of bilinear complementarity constraints as difference of convex (DC) functions, more specifically, the difference of scalar, convex quadratic terms. This reformulation gives rise to a DC program, which is … Read more

    On the Complexity of Lower-Order Implementations of Higher-Order Methods

  • Nikita Doikov
  • Geovani Nunes Grapiglia
    • Categories Nonlinear Optimization Tags , , , ,

    In this work, we propose a method for minimizing non-convex functions with Lipschitz continuous \(p\)th-order derivatives, starting from \(p \geq 1\). The method, however, only requires derivative information up to order \((p-1)\), since the \(p\)th-order derivatives are approximated via finite differences. To ensure oracle efficiency, instead of computing finite-difference approximations at every iteration, we reuse … Read more

    Global Multi-Objective Simulation Optimization: Error Bounds and Convergence Rates

  • Burla E. Ondes
  • Susan R. Hunter
    • Categories Global Optimization, Multi-Criteria Optimization, Optimization of Simulated Systems Tags , , , ,

    Consider the context of solving a multi-objective simulation optimization problem with one or more continuous objective functions to global optimality on a compact feasible set. For a simple algorithm that consists of selecting a finite set of feasible points using a space-filling design, expending the same number of simulation replications at each point to estimate … Read more

    A Simple Adaptive Proximal Gradient Method for Nonconvex Optimization

  • Ye Zilong
  • Shiqian Ma
  • Junfeng Yang
  • Danqing Zhou
    • Categories Constrained Nonlinear Optimization, Nonsmooth Optimization Tags , , , ,

    Consider composite nonconvex optimization problems where the objective function consists of a smooth nonconvex term (with Lipschitz-continuous gradient) and a convex (possibly nonsmooth) term. Existing parameter-free methods for such problems often rely on complex multi-loop structures, require line searches, or depend on restrictive assumptions (e.g., bounded iterates). To address these limitations, we introduce a novel … Read more