Deep Learning for Bipartite Assignment Problems*
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Deep Learning for Bipartite Assignment Problems *
- October 2019
- Conference: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
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Deep Learning for Bipartite Assignment Problems
2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
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| TASK | DATASET | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK | REMOVE |
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- DECISION MAKING
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Add a method, remove a method, edit datasets, deep policies for online bipartite matching: a reinforcement learning approach.
21 Sep 2021 · Mohammad Ali Alomrani , Reza Moravej , Elias B. Khalil · Edit social preview
The challenge in the widely applicable online matching problem lies in making irrevocable assignments while there is uncertainty about future inputs. Most theoretically-grounded policies are myopic or greedy in nature. In real-world applications where the matching process is repeated on a regular basis, the underlying data distribution can be leveraged for better decision-making. We present an end-to-end Reinforcement Learning framework for deriving better matching policies based on trial-and-error on historical data. We devise a set of neural network architectures, design feature representations, and empirically evaluate them across two online matching problems: Edge-Weighted Online Bipartite Matching and Online Submodular Bipartite Matching. We show that most of the learning approaches perform consistently better than classical baseline algorithms on four synthetic and real-world datasets. On average, our proposed models improve the matching quality by 3--10\% on a variety of synthetic and real-world datasets. Our code is publicly available at https://github.com/lyeskhalil/CORL.
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| ISBN | 9781728145693 (online) 9781728145709 (print) |
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| Keywords | deep learning; heuristics; weapon-target assignment problem; black box solver; deep neural networks |
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- DOI: 10.1109/SMC.2019.8914228
- Corpus ID: 208633297
Deep Learning for Bipartite Assignment Problems*
- Danièle Gibbons , C. Lim , Peng Shi
- Published in IEEE International Conference… 1 October 2019
- Computer Science, Mathematics
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Published in IEEE International Conference on Systems, Man and Cybernetics 2019
Danièle Gibbons C. Lim Peng Shi
The Application of Bipartite Matching in Assignment Problem
The optimized assignment of staff is of great significance for improving the production efficiency of the society. For specific tasks, the key to optimizing staffing is personnel scheduling. The assignment problem is classical in the personnel scheduling. In this paper, we abstract it as an optimal matching model of a bipartite graph and propose the Ultimate Hungarian Algorithm(UHA). By introducing feasible labels, iteratively searching for the augmenting path to get the optimal match(maximum-weight matching). And we compare the algorithm with the traditional brute force method, then conclude that our algorithm has lower time complexity and can solve the problems of maximum-weight matching more effectively.
Feiyang Chen
Hanyang Mao
Related Research
Revisiting the auction algorithm for weighted bipartite perfect matchings, restricted boltzmann machine assignment algorithm: application to solve many-to-one matching problems on weighted bipartite graph, max-product for maximum weight matching - revisited, r(qps-serena) and r(qps-serenade): two novel augmenting-path based algorithms for computing approximate maximum weight matching, nearly optimal communication and query complexity of bipartite matching, neural bipartite matching, an even faster and more unifying algorithm for comparing trees via unbalanced bipartite matchings.
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Many assignment problems cannot be solved to optimality in real-time. The existing literature tends to focus on the development of handcrafted heuristics that exploit the structure of a particular assignment problem. We instead seek a general-purpose approach that can automatically learn such heuristics. In this work, we present a deep learning approach that is designed to automatically learn ...
Deep Learning for Bipartite Assignment Problems<sup>*</sup> Authors: Daniel Gibbons, Cheng-Chew Lim, Peng Shi Authors Info & Claims. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) ... Deep Learning for Bipartite Assignment Problems<sup>*</sup> Pages 2318 - 2325.
a deep learning approach. Given a problem description, deep learning can be used to find near-optimal heuristics with minimal human input. The main contribution of this thesis is a deep learning architecture called Deep Bipartite Assignments (DBA), which can automatically learn heuristics for a large class of assignment problems.
This work presents a deep learning approach that is designed to automatically learn heuristics for a large class of assignment problems and demonstrates the effectiveness of the approach on the weapon-target assignment (WTA) problem, which is nonlinear and NP-complete. Many assignment problems cannot be solved to optimality in real-time. The existing literature tends to focus on the ...
The main contribution of this thesis is a deep learning architecture called Deep Bipartite Assignments (DBA), which can automatically learn heuristics for a large class of assignment problems. The effectiveness of DBA is demonstrated on two NP-Hard problems: the weapon-target assignment problem and the multi-resource generalised assignment problem.
Deep Learning for Bipartite Assignment Problems *. October 2019. DOI: 10.1109/SMC.2019.8914228. Conference: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) Authors: Daniel ...
A Deep Learning Architecture for Bipartite Assignment Problems 5.2 Overview From X, DBA performs a series of operations to construct a valid assignment matrix Y. A general overview of DBA is depicted in Figure 5.1. 5.2.1 Embedding DBA begins by processing X through an embedding operation E .
- "Deep Learning for Bipartite Assignment Problems*" Fig. 1: Empirical CDFs for all of the methods from this work. OG is a random variable that represents the optimality gap for a random problem instance from the test dataset.
The main contribution of this thesis is a deep learning architecture called Deep Bipartite Assignments (DBA), which can automatically learn heuristics for a large class of assignment problems. The effectiveness of DBA is demonstrated on two NP-Hard problems: the weapon-target assignment problem and the multi-resource generalised assignment problem.
The proposed Ranked Assignment Prediction Graph Neural Network (RAPNet) uses bipartite graphs to model the problem, harnessing the computational capabilities of deep learning. The conclusive evaluation compares the RAPNet with Murty's algorithm and the Gibbs sampler, showing accuracy improvements compared to the Gibbs sampler.
Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach. The challenge in the widely applicable online matching problem lies in making irrevocable assignments while there is uncertainty about future inputs. Most theoretically-grounded policies are myopic or greedy in nature. In real-world applications where the matching ...
The most popular used method is to construct a bipartite graph between two frames and adopt Hungarian algorithm [27] to solve it. ... stream of work is to treat the assignment problem as a su-pervised learning problem directly, and use the data fitting power of deep learning to learn the projection from input graphs to output assignment ...
Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach. The challenge in the widely applicable online matching problem lies in making irrevocable assignments while there is uncertainty about future inputs. Most theoretically-grounded policies are myopic or greedy in nature. In real-world applications where the matching ...
size. In this paper, we propose a learnable linear assignment solver based on deep graph networks. Specifically, we first transform the cost matrix to a bipartite graph and con-vert the assignment task to the problem of selecting reliable edges from the constructed graph. Subsequently, a deep graph network is developed to aggregate and update the
The VU Research Repository (previously known as VUIR) is an open access repository that contains the research papers and theses of VU staff and higher degree research students.
The approaches developed in this line of research are known as deep bipartite assignment (DBA) or deep bipartite matching. This method was first introduced by Gibbons et al. (2019), who studied the weapon-target assignment problem with a machine learning algorithm. In this line of research, solution approaches are based on deep models to ...
Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach. From assigning computing tasks to servers and advertisements to users, sequential online matching problems arise in a wide variety of domains. The challenge in online matching lies in making irrevocable assignments while there is uncertainty about future inputs.
the generalized assignment problems (GAP). GAP is a generic form of linear sum assignment problem (LSAP) and is more challenging to solve owing to the presence of both equality and inequality constraints. We propose a novel deep unsupervised learning (DUL) approach to solve GAP in a time-efficient manner.
Recent advances in deep reinforcement learning (DRL) have allowed it to contribute to areas which were previously the domain of traditional algorithms. ... A 2019 paper introduced Deep Bipartite Assignment (DBA), a neural network architecture which allows DRL to be applied to simple bipartite assignment problems such as the Weapon-Target ...
DOI: 10.1109/SMC.2019.8914228 Corpus ID: 208633297; Deep Learning for Bipartite Assignment Problems* @article{Gibbons2019DeepLF, title={Deep Learning for Bipartite Assignment Problems*}, author={Dani{\`e}le Gibbons and Cheng-Chew Lim and Peng Shi}, journal={2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)}, year={2019}, pages={2318-2325} }
2.2.2 Deep reinforcement learning-based methods for pickup and delivery problem DRL has become increasingly popular recently as a way to solve CO problems (Sutton & Barto, 2018 ). Vinyals et al. ( 2015 ) presented the Pointer Network, which incorporates the attention mechanism into the sequence-to-sequence model and is trained to solve TSP in ...
The assignment problem is classical in the personnel scheduling. In this paper, we abstract it as an optimal matching model of a bipartite graph and propose the Ultimate Hungarian Algorithm (UHA). By introducing feasible labels, iteratively searching for the augmenting path to get the optimal match (maximum-weight matching).
Gibbons D Lim C Shi P (2019) Deep Learning for Bipartite Assignment Problems * 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) 10.1109/SMC.2019.8914228 (2318-2325) Online publication date: 6-Oct-2019