An anytime tree search algorithm for the 2018 ROADEF/EURO challenge glass cutting problem

• URL: http://arxiv.org/abs/2004.00963v1
• Date: Thu, 2 Apr 2020 12:51:26 GMT
• Title: An anytime tree search algorithm for the 2018 ROADEF/EURO challenge glass cutting problem
• Authors: Luc Libralesso, Florian Fontan
• Abstract summary: We present the anytime tree search algorithm we designed for the 2018 ROADEF/EURO challenge glass cutting problem proposed by the French company Saint-Gobain. Its key components are: a new search algorithm called Memory Bounded A* (MBA*) with guide functions, a symmetry breaking strategy, and a pseudo-dominance rule. In addition, we designed a second tree search algorithm fully based on the pseudo-dominance rule and dedicated to some of the challenge instances with strong precedence constraints.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this article, we present the anytime tree search algorithm we designed for the 2018 ROADEF/EURO challenge glass cutting problem proposed by the French company Saint-Gobain. The resulting program was ranked first among 64 participants. Its key components are: a new search algorithm called Memory Bounded A* (MBA*) with guide functions, a symmetry breaking strategy, and a pseudo-dominance rule. We perform a comprehensive study of these components showing that each of them contributes to the algorithm global performances. In addition, we designed a second tree search algorithm fully based on the pseudo-dominance rule and dedicated to some of the challenge instances with strong precedence constraints. On these instances, it finds the best-known solutions very quickly.

Related papers

• RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation [65.5353313491402]
  We introduce RethinkMCTS, which employs the Monte Carlo Tree Search (MCTS) algorithm to conduct thought-level searches before generating code. We construct verbal feedback from fine-turbo code execution feedback to refine erroneous thoughts during the search. We demonstrate that RethinkMCTS outperforms previous search-based and feedback-based code generation baselines.
  arXiv  Detail & Related papers  (2024-09-15T02:07:28Z)
• Pathfinding with Lazy Successor Generation [12.02023514105999]
  We study a pathfinding problem where only locations are given, and edges are implicitly defined. Despite its simple structure, this problem becomes non-trivial with a massive number of locations. We propose a novel LaCAS* algorithm, which does not generate successors all at once but gradually generates successors as the search progresses.
  arXiv  Detail & Related papers  (2024-08-27T23:25:25Z)
• LiteSearch: Efficacious Tree Search for LLM [70.29796112457662]
  This study introduces a novel guided tree search algorithm with dynamic node selection and node-level exploration budget. Experiments conducted on the GSM8K and TabMWP datasets demonstrate that our approach enjoys significantly lower computational costs compared to baseline methods.
  arXiv  Detail & Related papers  (2024-06-29T05:14:04Z)
• UNSAT Solver Synthesis via Monte Carlo Forest Search [10.754275929551593]
  We introduce Monte Carlo Forest Search (MCFS), a class of reinforcement learning (RL) algorithms for learning policies in tree MDPs Examples of such problems include proving unsatisfiability of a SAT formula; counting the number of solutions of a satisfiable SAT formula. We dub Knuth Synthesis, an MCFS algorithm that learns DPLL branching policies for solving the satisfiability (SAT) problem.
  arXiv  Detail & Related papers  (2022-11-22T20:52:50Z)
• HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection [75.84584400866254]
  We propose a new algorithm selector leveraging special forests, combining the strengths of both approaches while alleviating their weaknesses. HARRIS' decisions are based on a forest model, whose trees are created based on optimized on a hybrid ranking and regression loss function.
  arXiv  Detail & Related papers  (2022-10-31T14:06:11Z)
• A Metaheuristic Algorithm for Large Maximum Weight Independent Set Problems [58.348679046591265]
  Given a node-weighted graph, find a set of independent (mutually nonadjacent) nodes whose node-weight sum is maximum. Some of the graphs airsing in this application are large, having hundreds of thousands of nodes and hundreds of millions of edges. We develop a new local search algorithm, which is a metaheuristic in the greedy randomized adaptive search framework.
  arXiv  Detail & Related papers  (2022-03-28T21:34:16Z)
• Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
  This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
  arXiv  Detail & Related papers  (2021-01-07T08:00:02Z)
• Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems [0.6882042556551609]
  This approach makes use of a algorithm to explore the search space tree of a problem instance. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees.
  arXiv  Detail & Related papers  (2020-10-22T08:33:58Z)
• Efficient Computation of Expectations under Spanning Tree Distributions [67.71280539312536]
  We propose unified algorithms for the important cases of first-order expectations and second-order expectations in edge-factored, non-projective spanning-tree models. Our algorithms exploit a fundamental connection between gradients and expectations, which allows us to derive efficient algorithms.
  arXiv  Detail & Related papers  (2020-08-29T14:58:26Z)
• An anytime tree search algorithm for two-dimensional two- and three-staged guillotine packing problems [0.0]
  algorithm was ranked first among 64 participants. We generalize it and show that it is not only effective for the specific problem it was originally designed for, but is also very competitive. The algorithm is implemented in a new software package called Packingr.
  arXiv  Detail & Related papers  (2020-04-02T13:41:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.