DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems

• URL: http://arxiv.org/abs/2210.04123v1
• Date: Sat, 8 Oct 2022 23:24:37 GMT
• Title: DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems
• Authors: Ruizhong Qiu, Zhiqing Sun, Yiming Yang
• Abstract summary: Deep reinforcement learning (DRL) models have shown promising results in solving NP-hard Combinatorial Optimization problems. This paper addresses the scalability challenge in large-scale optimization by proposing a novel approach, namely, DIMES. Unlike previous DRL methods which suffer from costly autoregressive decoding or iterative refinements of discrete solutions, DIMES introduces a compact continuous space for parameterizing the underlying distribution of candidate solutions. Extensive experiments show that DIMES outperforms recent DRL-based methods on large benchmark datasets for Traveling Salesman Problems and Maximal Independent Set problems.
• Score: 41.57773395100222
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, deep reinforcement learning (DRL) models have shown promising results in solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers can only scale to a few hundreds of nodes for combinatorial optimization problems on graphs, such as the Traveling Salesman Problem (TSP). This paper addresses the scalability challenge in large-scale combinatorial optimization by proposing a novel approach, namely, DIMES. Unlike previous DRL methods which suffer from costly autoregressive decoding or iterative refinements of discrete solutions, DIMES introduces a compact continuous space for parameterizing the underlying distribution of candidate solutions. Such a continuous space allows stable REINFORCE-based training and fine-tuning via massively parallel sampling. We further propose a meta-learning framework to enable the effective initialization of model parameters in the fine-tuning stage. Extensive experiments show that DIMES outperforms recent DRL-based methods on large benchmark datasets for Traveling Salesman Problems and Maximal Independent Set problems.

Related papers

• DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems [37.205311971072405]
  DISCO is an efficient DIffusion solver for large-scale Combinatorial Optimization problems. It constrains the sampling space to a more meaningful domain guided by solution residues, while preserving the multi-modal properties of the output distributions. It delivers strong performance on large-scale Traveling Salesman Problems and challenging Maximal Independent Set benchmarks, with inference time up to 5.28 times faster than other diffusion alternatives.
  arXiv  Detail & Related papers  (2024-06-28T07:36:31Z)
• Leader Reward for POMO-Based Neural Combinatorial Optimization [8.301694061287565]
  We propose Leader Reward to enhance the model's ability to generate optimal solutions. We demonstrate that Leader Reward greatly improves the quality of the optimal solutions generated by the model.
  arXiv  Detail & Related papers  (2024-05-22T19:27:03Z)
• Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems [3.3266268089678257]
  This paper proposes a framework that integrates MOEAs with adaptive parameter control using Deep Reinforcement Learning (DRL) The DRL policy is trained to adaptively set the values that dictate the intensity and probability of mutation for solutions during optimization. We show the learned policy is transferable, i.e., the policy trained on a simple benchmark problem can be directly applied to solve the complex warehouse optimization problem.
  arXiv  Detail & Related papers  (2022-11-01T22:08:34Z)
• Pareto Set Learning for Neural Multi-objective Combinatorial Optimization [6.091096843566857]
  Multiobjective optimization (MOCO) problems can be found in many real-world applications. We develop a learning-based approach to approximate the whole Pareto set for a given MOCO problem without further search procedure. Our proposed method significantly outperforms some other methods on the multiobjective traveling salesman problem, multiconditioned vehicle routing problem and multi knapsack problem in terms of solution quality, speed, and model efficiency.
  arXiv  Detail & Related papers  (2022-03-29T09:26:22Z)
• Neural Stochastic Dual Dynamic Programming [99.80617899593526]
  We introduce a trainable neural model that learns to map problem instances to a piece-wise linear value function. $nu$-SDDP can significantly reduce problem solving cost without sacrificing solution quality.
  arXiv  Detail & Related papers  (2021-12-01T22:55:23Z)
• Meta-Learning-based Deep Reinforcement Learning for Multiobjective Optimization Problems [11.478548460936837]
  This paper proposes a concise meta-learning-based DRL approach. It first trains a meta-model by meta-learning. The meta-model is fine-tuned with a few update steps to derive submodels for the corresponding subproblems.
  arXiv  Detail & Related papers  (2021-05-06T15:09:35Z)
• Modeling the Second Player in Distributionally Robust Optimization [90.25995710696425]
  We argue for the use of neural generative models to characterize the worst-case distribution. This approach poses a number of implementation and optimization challenges. We find that the proposed approach yields models that are more robust than comparable baselines.
  arXiv  Detail & Related papers  (2021-03-18T14:26:26Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)
• Automatically Learning Compact Quality-aware Surrogates for Optimization Problems [55.94450542785096]
  Solving optimization problems with unknown parameters requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values. Recent work has shown that including the optimization problem as a layer in a complex training model pipeline results in predictions of iteration of unobserved decision making. We show that we can improve solution quality by learning a low-dimensional surrogate model of a large optimization problem.
  arXiv  Detail & Related papers  (2020-06-18T19:11:54Z)
• Learning What to Defer for Maximum Independent Sets [84.00112106334655]
  We propose a novel DRL scheme, coined learning what to defer (LwD), where the agent adaptively shrinks or stretch the number of stages by learning to distribute the element-wise decisions of the solution at each stage. We apply the proposed framework to the maximum independent set (MIS) problem, and demonstrate its significant improvement over the current state-of-the-art DRL scheme.
  arXiv  Detail & Related papers  (2020-06-17T02:19:31Z)

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.