AGENTIC-SYSTEMSJuly 14, 20261 min read1 views

GES-TSP: Graph Edge Sparsification for TSP

Researchers have proposed GES-TSP, a learning-based graph edge sparsification method that accelerates solving large-scale Traveling Salesman Problems (TSP). This approach can prune up to 99% of unnecessary edges while maintaining near-optimal accuracy.

Computer Science > Artificial Intelligence

Title:GES-TSP: Graph Edge Sparsification for TSP

View PDF HTML (experimental)Abstract:Solving large-scale instances of the Traveling Salesman Problem (TSP) exactly is computationally expensive. Researchers often employ graph sparsification methods to improve computational efficiency. Traditional sparsification methods typically rely on fixed heuristics and fail to fully exploit instance-specific structural information. In this paper, we propose Graph Edge Sparsification (GES), a learning-based sparsification approach for Euclidean TSP. By incorporating geometric structural information and combinatorial optimization technology, our proposed method adaptively generates a sparsification graph for different instances, significantly reducing the graph size and accelerating the solving process. Experimental results demonstrate that our sparsification method can prune up to 95% of edges on the MATILDA dataset, while keeping the solution gap within 1% of the optimal value. Moreover, our approach exhibits strong generalization capability on the TSPLIB this http URL some large-scale instances, the pruning rate exceeds 99%, while the optimality gap remains below 1%.

Current browse context:

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

BatteryLake: Agentic, Physics-Grounded Curation of Heterogeneous Battery Aging Data and Benchmarking

Researchers have introduced BatteryLake, a governed data lakehouse that automates the curation of heterogeneous battery aging data using LLM agents and physics-grounded rules. This framework aims to accelerate research in battery health management (SOH and RUL) by providing a standardized benchmark of 41 datasets.

agentic-systems

LegalFarePlan: A Label-Setting Framework for Fare-Transparent Urban Rail Route Planning under Non-Additive Fare Rules

Researchers have proposed LegalFarePlan, a route-planning framework that optimizes urban rail fares by leveraging non-additive fare rules through legal exit-and-reentry operations.

agentic-systems

Task-Conditioned Synthetic Data Generation for Improving Machine Learning Performance in Agricultural Prediction Tasks

Researchers have proposed Task-Conditioned Synthetic Data Generation (TCSDG), a novel algorithm that addresses data scarcity in agricultural machine learning. By combining Bayesian Networks with transformer-based models, TCSDG significantly improves the accuracy of crop yield prediction and crop type classification.

NOW LET US Related – A Dynamic Scene Interaction Reasoning Framework for Scene-level Lane-Change Intention and Trajectory Prediction of Multiple Interacting Vehicles

agentic-systems

A Dynamic Scene Interaction Reasoning Framework for Scene-level Lane-Change Intention and Trajectory Prediction of Multiple Interacting Vehicles

Researchers have proposed DSiGAT, a novel dynamic scene graph attention framework that predicts both the lane-change intentions and future trajectories of multiple interacting vehicles simultaneously, significantly improving autonomous driving safety.

agentic-systems

Scaffolding the Strategist: Architecture-Dependent Reasoning Interventions in Hotelling Spatial Markets

A new study reveals a significant crossover interaction between reasoning scaffolding and LLM architecture, showing that interventions helping standard models can degrade reasoning-optimized models.

agentic-systems

Coresets Before Score Sets: Evaluation-Unsupervised Prompt Subset Selection for LLM Benchmarks

Researchers propose an evaluation-unsupervised prompt subset selection method to compress massive LLM benchmarks while preserving evaluation accuracy, significantly reducing computation costs.