#combinatorial-problems News & Analysis

3 articles tagged with #combinatorial-problems. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBullisharXiv – CS AI · Mar 37/104

🧠

DRAGON: LLM-Driven Decomposition and Reconstruction Agents for Large-Scale Combinatorial Optimization

Researchers introduce DRAGON, a new framework that combines Large Language Models with metaheuristic optimization to solve large-scale combinatorial optimization problems. The system decomposes complex problems into manageable subproblems and achieves near-optimal results on datasets with over 3 million variables, overcoming the scalability limitations of existing LLM-based solvers.

$NEAR

AIBullisharXiv – CS AI · Mar 37/103

🧠

REMS: a unified solution representation, problem modeling and metaheuristic algorithm design for general combinatorial optimization problems

Researchers introduce REMS, a unified framework for solving combinatorial optimization problems that views problems as resource allocation tasks. The framework enables reusable metaheuristic algorithms and outperforms established solvers like GUROBI and SCIP on large-scale instances across 10 different problem types.

AINeutralarXiv – CS AI · Apr 156/10

🧠

Modeling Co-Pilots for Text-to-Model Translation

Researchers introduce Text2Model and Text2Zinc, frameworks that use large language models to translate natural language descriptions into formal optimization and satisfaction models. The work represents the first unified approach combining both problem types with a solver-agnostic architecture, though experiments reveal LLMs remain imperfect at this task despite showing competitive performance.