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Improving Variable Orderings of Approximate Decision Diagrams Using Reinforcement Learning

Author

Listed:
  • Quentin Cappart

    (Ecole Polytechnique de Montréal, Montreal H3T 1J4, Canada; ServiceNow Research (formerly Element AI), Montreal H2S 3G9, Canada)

  • David Bergman

    (University of Connecticut, Stamford, Connecticut 06901)

  • Louis-Martin Rousseau

    (Ecole Polytechnique de Montréal, Montreal H3T 1J4, Canada)

  • Isabeau Prémont-Schwarz

    (ServiceNow Research (formerly Element AI), Montreal H2S 3G9, Canada)

  • Augustin Parjadis

    (Ecole Polytechnique de Montréal, Montreal H3T 1J4, Canada)

Abstract

Prescriptive analytics provides organizations with scalable solutions for large-scale, automated decision making. At the core of prescriptive analytics methodology is optimization, a field devoted to the study of algorithms that solve complex decision-making problems. Optimization algorithms rely heavily on generic methods for identifying tight bounds, which provide both solutions to problems and optimality guarantees. In the last decade, decision diagrams (DDs) have demonstrated significant advantages in obtaining bounds compared with the standard linear relaxation commonly used by commercial solvers. However, the quality of the bounds computed by DDs depends heavily on the variable ordering chosen for the construction. Besides, the problem of finding an ordering that optimizes a given metric is generally NP-hard. This paper studies how machine learning, specifically deep reinforcement learning (DRL), can be used to improve bounds provided by DDs, in particular through learning a good variable ordering. The introduced DRL models improve primal and dual bounds, even over standard linear programming relaxations, and are integrated in a full-fledged branch-and-bound algorithm. This paper, therefore, provides a novel mechanism for utilizing machine learning to tighten bounds, adding to recent research on using machine learning to obtain high-quality heuristic solutions and, for the first time, using machine learning to improve relaxation bounds through a generic bounding method. We apply the methods on a classic optimization problem, the maximum independent set, and demonstrate through computational testing that optimization bounds can be significantly improved through DRL. We provide the code to replicate the results obtained on the maximum independent set. Summary of Contribution: This paper studies the use of reinforcement learning to compute a variable ordering of decision diagram-based approximations for discrete optimization problems. This is among the first works to propose the use of machine learning to improve upon generic bounding methods for discrete optimization problems, thereby establishing a critical bridge between optimization and learning.

Suggested Citation

  • Quentin Cappart & David Bergman & Louis-Martin Rousseau & Isabeau Prémont-Schwarz & Augustin Parjadis, 2022. "Improving Variable Orderings of Approximate Decision Diagrams Using Reinforcement Learning," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2552-2570, September.
  • Handle: RePEc:inm:orijoc:v:34:y:2022:i:5:p:2552-2570
    DOI: 10.1287/ijoc.2022.1194
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    References listed on IDEAS

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    1. David Bergman & Andre A. Cire & Willem-Jan van Hoeve & J. N. Hooker, 2016. "Discrete Optimization with Decision Diagrams," INFORMS Journal on Computing, INFORMS, vol. 28(1), pages 47-66, February.
    2. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    3. Andrea Lodi & Giulia Zarpellon, 2017. "Rejoinder on: On learning and branching: a survey," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 247-248, July.
    4. Ruslan Sadykov & François Vanderbeck & Artur Pessoa & Issam Tahiri & Eduardo Uchoa, 2019. "Primal Heuristics for Branch and Price: The Assets of Diving Methods," INFORMS Journal on Computing, INFORMS, vol. 31(2), pages 251-267, April.
    5. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    6. Andrea Lodi & Giulia Zarpellon, 2017. "On learning and branching: a survey," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 207-236, July.
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