RIDGECUT: Learning Graph Partitioning with Rings and Wedges

• URL: http://arxiv.org/abs/2505.13986v3
• Date: Mon, 11 Aug 2025 06:26:08 GMT
• Title: RIDGECUT: Learning Graph Partitioning with Rings and Wedges
• Authors: Qize Jiang, Linsey Pang, Alice Gatti, Mahima Aggarwal, Giovanna Vantini, Xiaosong Ma, Weiwei Sun, Sourav Medya, Sanjay Chawla,
• Abstract summary: We propose RIDGECUT, the first RL framework that constrains the action space to enforce structure-aware partitioning in the Normalized Cut problem.<n>Our method reshapes the graph into a linear or circular structure to simplify the partitioning task.
• Score: 15.380998112133389
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning (RL) has proven to be a powerful tool for combinatorial optimization (CO) problems due to its ability to learn heuristics that can generalize across problem instances. However, integrating knowledge that will steer the RL framework for CO solutions towards domain appropriate outcomes remains a challenging task. In this paper, we propose RIDGECUT, the first RL framework that constrains the action space to enforce structure-aware partitioning in the Normalized Cut problem. Using transportation networks as a motivating example, we introduce a novel concept that leverages domain knowledge about urban road topology -- where natural partitions often take the form of concentric rings and radial wedges. Our method reshapes the graph into a linear or circular structure to simplify the partitioning task so that we can apply sequential transformers and enables efficient learning via Proximal Policy Optimization. The resulting partitions are not only aligned with expected spatial layouts but also achieve lower normalized cuts compared to existing methods. While we focus on traffic data, our approach is broadly applicable and offers a mechanism for embedding structural priors into RL for graph partitioning.

Related papers

• A Distributed Training Architecture For Combinatorial Optimization [0.0]
  We propose a distributed graph neural network (GNN)-based training framework for optimization.<n>Experiments are conducted on both real large-scale social network datasets and synthetically generated high-complexity graphs.<n>Our framework outperforms state-of-the-art approaches in both solution quality and computational efficiency.
  arXiv  Detail & Related papers  (2025-11-12T12:22:10Z)
• A Minimum Description Length Approach to Regularization in Neural Networks [2.446672595462589]
  We show that the choice of regularization method plays a crucial role when trained on formal languages.<n>We propose that unlike existing regularization techniques, MDL introduces the appropriate inductive bias to effectively counteract overfitting and promote generalization.
  arXiv  Detail & Related papers  (2025-05-19T17:34:56Z)
• Towards graph neural networks for provably solving convex optimization problems [5.966097889241178]
  We propose an iterative MPNN framework to solve convex optimization problems with provable feasibility guarantees.<n> Experimental results show that our approach outperforms existing neural baselines in solution quality and feasibility.
  arXiv  Detail & Related papers  (2025-02-04T16:11:41Z)
• Evolving Generalizable Parallel Algorithm Portfolios via Domain-Agnostic Instance Generation [27.138566940625385]
  Generalization is the core objective when trainings from data.<n>Co-evolutionary approaches address this challenge by simultaneously evolving a parallel algorithm portfolio (PAP) and an instance population.<n>This work proposes a general-purpose, off-the-shelf PAP construction approach, named domain-agnostic co-evolution of parameterized search (DACE)
  arXiv  Detail & Related papers  (2025-01-06T10:29:48Z)
• Learning Constrained Optimization with Deep Augmented Lagrangian Methods [54.22290715244502]
  A machine learning (ML) model is trained to emulate a constrained optimization solver. This paper proposes an alternative approach, in which the ML model is trained to predict dual solution estimates directly. It enables an end-to-end training scheme is which the dual objective is as a loss function, and solution estimates toward primal feasibility, emulating a Dual Ascent method.
  arXiv  Detail & Related papers  (2024-03-06T04:43:22Z)
• Symmetry-preserving graph attention network to solve routing problems at multiple resolutions [1.9304772860080408]
  We introduce the first-ever completely equivariant model and training to solve problems. It is essential to capture the multiscale structure of the input graph. We propose a Multiresolution scheme in combination with Equi Graph Attention network (mEGAT) architecture.
  arXiv  Detail & Related papers  (2023-10-24T06:22:20Z)
• Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods [52.0617030129699]
  We introduce a novel theoretical framework for analyzing the effectiveness of DeepMatching Networks and Reinforcement Learning methods. Our main contribution holds for a broad class of problems including Max-and Min-Cut, Max-$k$-Bipartite-Bi, Maximum-Weight-Bipartite-Bi, and Traveling Salesman Problem. As a byproduct of our analysis we introduce a novel regularization process over vanilla descent and provide theoretical and experimental evidence that it helps address vanishing-gradient issues and escape bad stationary points.
  arXiv  Detail & Related papers  (2023-10-08T23:39:38Z)
• One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering [56.81492360414741]
  We show how to enforce a one-step normalized cut for bipartite graphs, especially with linear-time complexity. In this paper, we first characterize a novel one-step bipartite graph cut criterion with normalized constraints, and theoretically prove its equivalence to a trace problem. We extend this cut criterion to a scalable subspace clustering approach, where adaptive anchor learning, bipartite graph learning, and one-step normalized bipartite graph partitioning are simultaneously modeled.
  arXiv  Detail & Related papers  (2023-05-12T11:27:20Z)
• Linearization Algorithms for Fully Composite Optimization [61.20539085730636]
  This paper studies first-order algorithms for solving fully composite optimization problems convex compact sets. We leverage the structure of the objective by handling differentiable and non-differentiable separately, linearizing only the smooth parts.
  arXiv  Detail & Related papers  (2023-02-24T18:41:48Z)
• Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver. This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
  arXiv  Detail & Related papers  (2023-01-28T01:50:42Z)
• An Enhanced Graph Representation for Machine Learning Based Automatic Intersection Management [0.5161531917413708]
  We build upon a previously presented graph-based scene representation and graph neural network to approach the problem using reinforcement learning. The paper provides an in-depth evaluation of the proposed method against baselines that are commonly used in automatic intersection management.
  arXiv  Detail & Related papers  (2022-07-18T14:53:50Z)
• Towards Principled Disentanglement for Domain Generalization [90.9891372499545]
  A fundamental challenge for machine learning models is generalizing to out-of-distribution (OOD) data. We first formalize the OOD generalization problem as constrained optimization, called Disentanglement-constrained Domain Generalization (DDG) Based on the transformation, we propose a primal-dual algorithm for joint representation disentanglement and domain generalization.
  arXiv  Detail & Related papers  (2021-11-27T07:36:32Z)
• Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks [75.33431791218302]
  We study the training problem of deep neural networks and introduce an analytic approach to unveil hidden convexity in the optimization landscape. We consider a deep parallel ReLU network architecture, which also includes standard deep networks and ResNets as its special cases.
  arXiv  Detail & Related papers  (2021-10-18T18:00:36Z)
• Clustered Federated Learning via Generalized Total Variation Minimization [83.26141667853057]
  We study optimization methods to train local (or personalized) models for local datasets with a decentralized network structure. Our main conceptual contribution is to formulate federated learning as total variation minimization (GTV) Our main algorithmic contribution is a fully decentralized federated learning algorithm.
  arXiv  Detail & Related papers  (2021-05-26T18:07:19Z)
• SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems [4.777801093677586]
  We study problems with real world applications such as machine scheduling, routing, and assignment. We propose a method that combines Reinforcement Learning (RL) and planning. This method can equally be applied to both the offline, as well as online, variants of the problem, in which the problem components are not known in advance, but rather arrive during the decision-making process.
  arXiv  Detail & Related papers  (2021-04-04T17:12:24Z)
• Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation [101.22379613810881]
  We consider data-driven optimization problems where one must maximize a function given only queries at a fixed set of points. This problem setting emerges in many domains where function evaluation is a complex and expensive process. We propose a tractable approximation that allows us to scale our method to high-capacity neural network models.
  arXiv  Detail & Related papers  (2021-02-16T06:04:27Z)
• Reversible Action Design for Combinatorial Optimization with Reinforcement Learning [35.50454156611722]
  Reinforcement learning (RL) has recently emerged as a new framework to tackle these problems. We propose a general RL framework that not only exhibits state-of-the-art empirical performance but also generalizes to a variety class of COPs.
  arXiv  Detail & Related papers  (2021-02-14T18:05:42Z)
• Overcoming Catastrophic Forgetting via Direction-Constrained Optimization [43.53836230865248]
  We study a new design of the optimization algorithm for training deep learning models with a fixed architecture of the classification network in a continual learning framework. We present our direction-constrained optimization (DCO) method, where for each task we introduce a linear autoencoder to approximate its corresponding top forbidden principal directions. We demonstrate that our algorithm performs favorably compared to other state-of-art regularization-based continual learning methods.
  arXiv  Detail & Related papers  (2020-11-25T08:45:21Z)
• Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems [2.335152769484957]
  We derive and detail a theoretical analysis of an efficient consensus algorithm based on gradient proximal (PG) approach. The proposed algorithm is also applied to another particular convolutional problem for the anomaly detection task.
  arXiv  Detail & Related papers  (2020-11-19T20:52:48Z)
• Constrained Combinatorial Optimization with Reinforcement Learning [0.30938904602244344]
  This paper presents a framework to tackle constrained optimization problems using deep Reinforcement Learning (RL) We extend the Neural Combinatorial Optimization (NCO) theory in order to deal with constraints in its formulation. In that context, the solution is iteratively constructed based on interactions with the environment.
  arXiv  Detail & Related papers  (2020-06-22T03:13:07Z)
• FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data [59.50904660420082]
  Federated Learning (FL) has become a popular paradigm for learning from distributed data. To effectively utilize data at different devices without moving them to the cloud, algorithms such as the Federated Averaging (FedAvg) have adopted a "computation then aggregation" (CTA) model.
  arXiv  Detail & Related papers  (2020-05-22T23:07:42Z)
• Neural Subdivision [58.97214948753937]
  This paper introduces Neural Subdivision, a novel framework for data-driven coarseto-fine geometry modeling. We optimize for the same set of network weights across all local mesh patches, thus providing an architecture that is not constrained to a specific input mesh, fixed genus, or category. We demonstrate that even when trained on a single high-resolution mesh our method generates reasonable subdivisions for novel shapes.
  arXiv  Detail & Related papers  (2020-05-04T20:03:21Z)
• Supervised Domain Adaptation using Graph Embedding [86.3361797111839]
  Domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. We propose a generic framework based on graph embedding. We show that the proposed approach leads to a powerful Domain Adaptation framework.
  arXiv  Detail & Related papers  (2020-03-09T12:25:13Z)
• Graph Ordering: Towards the Optimal by Learning [69.72656588714155]
  Graph representation learning has achieved a remarkable success in many graph-based applications, such as node classification, prediction, and community detection. However, for some kind of graph applications, such as graph compression and edge partition, it is very hard to reduce them to some graph representation learning tasks. In this paper, we propose to attack the graph ordering problem behind such applications by a novel learning approach.
  arXiv  Detail & Related papers  (2020-01-18T09:14:16Z)

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.