machine learning

Machine Learning Algorithms for Assisting Solvers for Constraint Satisfaction Problems

  • Morteza Kimiaei
  • Vyacheslav Kungurtsev
    • Categories Applications - OR and Management Sciences, Combinatorial Optimization, Optimization in Data Science Tags , , , , , , , , , ,

    This survey proposes a unifying conceptual framework and taxonomy that systematically integrates Machine Learning (ML) and Reinforcement Learning (RL) with classical paradigms for Constraint Satisfaction and Boolean Satisfiability solving. Unlike prior reviews that focus on individual applications, we organize the literature around solver architecture, linking each major phase—constraint propagation, heuristic decision-making, conflict analysis, and meta-level … Read more

    Machine Learning Algorithms for Assisting Solvers for Decision Optimization Problems

  • Morteza Kimiaei
  • Vyacheslav Kungurtsev
    • Categories Applications - OR and Management Sciences, Integer Programming, Optimization in Data Science Tags , , , , , , , ,

    Combinatorial decision problems lie at the intersection of Operations Research (OR) and Artificial Intelligence (AI), encompassing structured optimization tasks such as submodular selection, dynamic programming, planning, and scheduling. These problems exhibit exponential growth in decision complexity, driven by interdependent choices coupled through logical, temporal, and resource constraints.  Classical optimization frameworks—including integer programming, submodular optimization, and … Read more

    Counterfactual explanations with the k-Nearest Neighborhood classifier and uncertain data

  • Emilio Carrizosa
  • Renato De Leone
  • Marica Magagnini
    • Categories Bilevel Optimization, Robust Optimization Tags , , , ,

    Counterfactual Analysis is a powerful tool in Explainable Machine Learning. Given a classifier and a record, one seeks the smallest perturbation necessary to have the perturbed record, called the counterfactual explanation, classified in the desired class. Feature uncertainty in data reflects the inherent variability and noise present in real-world scenarios, and therefore, there is a … Read more

    Optimizing pricing strategies through learning the market structure

  • Khosroparviz Naderivarandi
  • Ezgi Karabulut
  • Burak Gokgur
    • Categories Optimization in Data Science, Yield Management Tags , , ,

    This study explores the integration of market structure learning into pricing strategies to maximize revenue in e-commerce and retail environments. We consider the problem of determining the revenue maximizing price of a single product in a market of heterogeneous consumers segmented by their product valuations; and analyze the pricing strategies for varying levels of prior … 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

    Machine Learning Algorithms for Improving Black Box Optimization Solvers

  • Morteza Kimiaei
  • Vyacheslav Kungurtsev
    • Categories Global Optimization, Nonlinear Optimization, Optimization in Data Science Tags , , , , ,

    Black-box optimization (BBO) addresses problems where objectives are accessible only through costly queries without gradients or explicit structure. Classical derivative-free methods—line search, direct search, and model-based solvers such as Bayesian optimization—form the backbone of BBO, yet often struggle in high-dimensional, noisy, or mixed-integer settings. Recent advances use machine learning (ML) and reinforcement learning (RL) to … Read more

    Combining Simulation with Machine Learning and Optimization to Assess Green Hydrogen Production via Offshore Wind in the Dutch North Sea

  • Justin Starreveld
  • Dick den Hertog
  • Kenneth Bruninx
  • Alessandro Bianchini
  • Riccardo Travaglini
  • R.J van 't Veer
  • Laurens Frowijn
    • Categories Applications - OR and Management Sciences, Energy, Optimization of Simulated Systems Tags , , ,

    As countries seek to decarbonize their energy systems, green hydrogen has emerged as a promising energy carrier. This paper studies the production of green hydrogen from offshore wind in the Dutch North Sea, with particular emphasis on understanding how system design decisions and uncertain parameters affect key performance indicators. The analysis builds on a detailed … Read more

    Toward Decision-Oriented Prognostics: An Integrated Estimate-Optimize Framework for Predictive Maintenance

  • Zhuojun Xie
  • Adam Abdin
  • Yiping Fang
    • Categories Optimization in Data Science, Stochastic Programming Tags , , , ,

    Recent research increasingly integrates machine learning (ML) into predictive maintenance (PdM) to reduce operational and maintenance costs in data-rich operational settings. However, uncertainty due to model misspecification continues to limit widespread industrial adoption. This paper investigates a PdM framework in which sensor-driven prognostics inform decision-making under economic trade-offs within a finite decision space. We investigate … Read more

    An Optimization-Based Algorithm for Fair and Calibrated Synthetic Data Generation

  • Maria Eduarda Pinheiro
  • Jan Pablo Burgard
  • João Vitor Pamplona
    • Categories Applications - OR and Management Sciences Tags , , ,

      For agent based micro simulations, as used for example for epidemiological modeling during the COVID-19 pandemic, a realistic base population is crucial. Beyond demographic variables, health-related variables should also be included. In Germany, health-related surveys are typically small in scale, which presents several challenges when generating these variables. Specifically, strongly imbalanced classes and insufficient … Read more

    Deterministic global optimization with trained neural networks: Is the envelope of single neurons worth it?

  • Clara Witte
  • Jannik Lüthje
  • Victor Schulte
  • Alexander Mitsos
  • Dominik Bongartz
    • Categories Global Optimization, Global Optimization Applications, Optimization in Data Science Tags , , ,

    Optimization problems containing trained neural networks remain challenging due to their nonconvexity. Deterministic global optimization relies on relaxations which should be tight, quickly convergent, and cheap to evaluate. While envelopes of common activation functions have been established for several years, the envelope of an entire neuron had not. Recently, Carrasco and Mu\~{n}oz (arXiv.2410.23362, 2024) proposed … Read more