MEMOIR adds branch-local and global memory with a reflection step to tree search for LLM solver synthesis, reaching 96.7% solution validity and 7.3-point score gains over baselines on seven CO problems with lower run-to-run variance.
48550/arXiv.1906.01227, http://arxiv.org/abs/1906.01227, arXiv:1906.01227 [cs, stat]
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GOAL uses conditioned diffusion on relational graphs with typed edges to produce feasible multi-objective solutions for scheduling problems, reporting 100% feasibility and sub-0.2% MAPE on FSP, JSP, and FJSP up to 20 jobs.
A two-stage ML sparsifier for TSP candidate graphs combines alpha-Nearest and POPMUSIC for high recall then trains a model to cut density while preserving coverage across distance types and instance sizes up to 500.
HyperNS clusters TSP cities with a sparse heatmap, builds a hyper tour over supernodes, and restricts neighborhood search to hyper-tour-relevant edges to improve solution quality on large instances.
citing papers explorer
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Memory-Guided Tree Search with Cross-Branch Knowledge Transfer for LLM Solver Synthesis
MEMOIR adds branch-local and global memory with a reflection step to tree search for LLM solver synthesis, reaching 96.7% solution validity and 7.3-point score gains over baselines on seven CO problems with lower run-to-run variance.
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GOAL: Graph-based Objective-Aligned Diffusion Solvers for Dynamic Multi-Objective Optimization
GOAL uses conditioned diffusion on relational graphs with typed edges to produce feasible multi-objective solutions for scheduling problems, reporting 100% feasibility and sub-0.2% MAPE on FSP, JSP, and FJSP up to 20 jobs.
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Machine Learning for Two-Stage Graph Sparsification for the Travelling Salesman Problem
A two-stage ML sparsifier for TSP candidate graphs combines alpha-Nearest and POPMUSIC for high recall then trains a model to cut density while preserving coverage across distance types and instance sizes up to 500.
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Empowering Targeted Neighborhood Search via Hyper Tour for Large-Scale TSP
HyperNS clusters TSP cities with a sparse heatmap, builds a hyper tour over supernodes, and restricts neighborhood search to hyper-tour-relevant edges to improve solution quality on large instances.