DASH: Dynamic Approach for Switching Heuristics

Liberto, Giovanni Di; Kadioglu, Serdar; Leo, Kevin; Malitsky, Yuri

doi:10.1016/j.ejor.2015.08.018

Cited by 35 publications

(24 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, our generic framework allows us to change dynamically the configuration during the solving process. Therefore, we plan to extend our work to dynamic configuration as proposed, for example, in [33] or [34].…”

Section: Resultsmentioning

confidence: 99%

Experimental Comparison of BTD and Intelligent Backtracking: Towards an Automatic Per-instance Algorithm Selector

Blet

Ndiaye

Solnon

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

We consider a generic binary CSP solver parameterized by high-level design choices, i.e., backtracking mechanisms, constraint propagation levels, and variable ordering heuristics. We experimentally compare 24 different configurations of this generic solver on a benchmark of around a thousand instances. This allows us to understand the complementarity of the different search mechanisms, with an emphasis on Backtracking with Tree Decomposition (BTD). Then, we use a per-instance algorithm selector to automatically select a good solver for each new instance to be solved. We introduce a new strategy for selecting the solvers of the portfolio, which aims at maximizing the number of instances for which the portfolio contains a good solver, independently from a time limit.

show abstract

Section: Resultsmentioning

confidence: 99%

Experimental Comparison of BTD and Intelligent Backtracking: Towards an Automatic Per-instance Algorithm Selector

Blet

Ndiaye

Solnon

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Based on the observation of the highly dynamic and sequential nature of B&B, Di Liberto et al [85] believed that there is no single branching heuristics given in Section 2.2 that would perform the best on all problems, even on different subproblems induced from the same MILP. Thus, the efficiency of the search can be much improved if we adopt the correct branching method at the right time during the B&B search.…”

Section: Dynamic Approach For Switching Branching Heuristicsmentioning

confidence: 99%

Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trends

Huang¹,

Chen²,

Huo³

et al. 2021

Preprint

View full text Add to dashboard Cite

In this paper, we surveyed the existing literature studying different approaches and algorithms for the four critical components in the general branch and bound (B&B) algorithm, namely, branching variable selection, node selection, node pruning, and cutting-plane selection. However, the complexity of the B&B algorithm always grows exponentially with respect to the increase of the decision variable dimensions. In order to improve the speed of B&B algorithms, learning techniques have been introduced in this algorithm recently. We further surveyed how machine learning can be used to improve the four critical components in B&B algorithms. In general, a supervised learning method helps to generate a policy that mimics an expert but significantly improves the speed. An unsupervised learning method helps choose different methods based on the features. In addition, models trained with reinforcement learning can beat the expert policy, given enough training and a supervised initialization. Detailed comparisons between different algorithms have been summarized in our survey. Finally, we discussed some future research directions to accelerate and improve the algorithms further in the literature.

show abstract

“…Learning mechanisms have been successfully applied within search procedures to select which heuristics to apply online. The DASH method, introduced by Liberto et al (2016), learns a model offline for branch heuristic selection of a MIP. In contrast to DASH, DLTS learns the branching heuristic itself, rather than selecting among different options provided by domain experts.…”

Section: Heuristic and Algorithm Selectionmentioning

confidence: 99%

Deep learning assisted heuristic tree search for the container pre-marshalling problem

Hottung

Tanaka

Tierney

2020

Computers & Operations Research

View full text Add to dashboard Cite

One of the key challenges for operations researchers solving real-world problems is designing and implementing high-quality heuristics to guide their search procedures. In the past, machine learning techniques have failed to play a major role in operations research approaches, especially in terms of guiding branching and pruning decisions. We integrate deep neural networks into a heuristic tree search procedure to decide which branch to choose next and to estimate a bound for pruning the search tree of an optimization problem. We call our approach Deep Learning assisted heuristic Tree Search (DLTS) and apply it to a well-known problem from the container terminals literature, the container pre-marshalling problem (CPMP). Our approach is able to learn heuristics customized to the CPMP solely through analyzing the solutions to CPMP instances, and applies this knowledge within a heuristic tree search to produce the highest quality heuristic solutions to the CPMP to date.

show abstract

DASH: Dynamic Approach for Switching Heuristics

Cited by 35 publications

References 21 publications

Experimental Comparison of BTD and Intelligent Backtracking: Towards an Automatic Per-instance Algorithm Selector

Experimental Comparison of BTD and Intelligent Backtracking: Towards an Automatic Per-instance Algorithm Selector

Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trends

Deep learning assisted heuristic tree search for the container pre-marshalling problem

Contact Info

Product

Resources

About