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End-to-End Constrained Optimization Learning: A Survey

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arxiv 2103.16378 v1 pith:ZEYA6OQP submitted 2021-03-30 cs.LG cs.AI

James Kotary , Ferdinando Fioretto , Pascal Van Hentenryck , Bryan Wilder This is my paper · ORCID
classification cs.LGcs.AI
keywords learningoptimizationmachinecombinatorialconstrainedmethodsproblemsrecent
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This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems. It focuses on surveying the work on integrating combinatorial solvers and optimization methods with machine learning architectures. These approaches hold the promise to develop new hybrid machine learning and optimization methods to predict fast, approximate, solutions to combinatorial problems and to enable structural logical inference. This paper presents a conceptual review of the recent advancements in this emerging area.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Optimization with Dynamic Constraint Learning (DCL)

    cs.LG 2026-07 conditional novelty 6.0 of 10

    DCL replaces a single global surrogate of an unqueryable constraint with a sequence of locally fitted surrogates and data-hull trust regions, matching global-model solutions on three examples with simpler subproblems.

  2. Improving Access to Essential Medicines via Decision-Aware Machine Learning

    cs.LG 2026-07 conditional novelty 6.0 of 10

    A decision-aware ML system for allocating essential medicines, evaluated in a staggered nationwide deployment in Sierra Leone, increased measured consumption of allocated products by roughly 19% in treated districts.

  3. Retrospective Approximation Sequential Quadratic Programming for Stochastic Optimization with General Deterministic Nonlinear Constraints

    math.OC 2025-05 conditional novelty 6.0 of 10

    RA-SQP achieves optimal O(epsilon^-4) gradient and O(epsilon^-2) linear-system complexity for equality-constrained stochastic optimization, and handles general nonlinear constraints via robust subproblems.

  4. Soft-Constrained Optimization of Latent Space in Variational Autoencoders

    cs.LG 2026-07 conditional novelty 5.0 of 10

    An entropy soft-constraint raises VAE latent capacity and a weight filter prunes unused dimensions, improving activation and FactorVAE scores on dSprites and cutting MNIST latent dim from 10 to 2.

  5. Decision-Focused Learning Enhanced by Automated Feature Engineering for Energy Storage Optimisation

    cs.AI 2025-09 conditional novelty 5.0 of 10

    On 55 days of UK household data, SPO+ decision-focused learning with automated features gave the lowest battery-scheduling regret, though gains over no-AFE were not statistically robust.

  6. Flow-Through Tensors: A Unified Computational Graph Architecture for Multi-Layer Transportation Network Optimization

    math.OC 2025-06 conditional novelty 3.0 of 10

    A tensor computational graph that chains OD flows, path flows, link flows, and travel times, plus a two-route rotation theorem showing Pareto gains from alternating route assignments.

  7. Learning to Optimize by Differentiable Programming

    cs.MS 2026-01 unverdicted novelty 2.0 of 10

    A tutorial survey of differentiable-programming-based first-order optimization, with dual-based PyTorch case studies and no new results.

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