UNCO: Towards Unifying Neural Combinatorial Optimization through Large Language Model

• URL: http://arxiv.org/abs/2408.12214v1
• Date: Thu, 22 Aug 2024 08:42:44 GMT
• Title: UNCO: Towards Unifying Neural Combinatorial Optimization through Large Language Model
• Authors: Xia Jiang, Yaoxin Wu, Yuan Wang, Yingqian Zhang,
• Abstract summary: We propose a unified neural optimization framework to solve different types of optimization problems (COPs) by a single model. We use natural language to formulate text-attributed instances for different COPs and encode them in the same embedding space by the large language model (LLM) Experiments show that the UNCO model can solve multiple COPs after a single-session training, and achieves satisfactory performance that is comparable to several traditional or learning-based baselines.
• Score: 21.232626415696267
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recently, applying neural networks to address combinatorial optimization problems (COPs) has attracted considerable research attention. The prevailing methods always train deep models independently on specific problems, lacking a unified framework for concurrently tackling various COPs. To this end, we propose a unified neural combinatorial optimization (UNCO) framework to solve different types of COPs by a single model. Specifically, we use natural language to formulate text-attributed instances for different COPs and encode them in the same embedding space by the large language model (LLM). The obtained embeddings are further advanced by an encoder-decoder model without any problem-specific modules, thereby facilitating a unified process of solution construction. We further adopt the conflict gradients erasing reinforcement learning (CGERL) algorithm to train the UNCO model, delivering better performance across different COPs than vanilla multi-objective learning. Experiments show that the UNCO model can solve multiple COPs after a single-session training, and achieves satisfactory performance that is comparable to several traditional or learning-based baselines. Instead of pursuing the best performance for each COP, we explore the synergy between tasks and few-shot generalization based on LLM to inspire future work.

Related papers

• Large Language Models for Combinatorial Optimization of Design Structure Matrix [4.513609458468522]
  Combinatorial optimization (CO) is essential for improving efficiency and performance in engineering applications. When it comes to real-world engineering problems, algorithms based on pure mathematical reasoning are limited and incapable to capture the contextual nuances necessary for optimization. This study explores the potential of Large Language Models (LLMs) in solving engineering CO problems by leveraging their reasoning power and contextual knowledge.
  arXiv  Detail & Related papers  (2024-11-19T15:39:51Z)
• Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
  Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving. Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods. We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
  arXiv  Detail & Related papers  (2024-10-10T17:00:06Z)
• Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization [62.16747639440893]
  Large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering. Our proposed framework incorporates retrieval-augmented generation (RAG) to enhance the system's ability to acquire domain-specific knowledge and generate solutions.
  arXiv  Detail & Related papers  (2024-08-07T08:43:32Z)
• Solving General Natural-Language-Description Optimization Problems with Large Language Models [34.50671063271608]
  We propose a novel framework called OptLLM that augments LLMs with external solvers. OptLLM accepts user queries in natural language, convert them into mathematical formulations and programming codes, and calls the solvers to calculate the results. Some features of OptLLM framework have been available for trial since June 2023.
  arXiv  Detail & Related papers  (2024-07-09T07:11:10Z)
• Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT [9.682499180341273]
  Large language models (LLMs) have significantly advanced text generation, but the human-like quality of their outputs presents major challenges. We propose CUDRT, a comprehensive evaluation framework and bilingual benchmark in Chinese and English. This framework supports scalable, reproducible experiments and enables analysis of how operational diversity, multilingual training sets, and LLM architectures influence detection performance.
  arXiv  Detail & Related papers  (2024-06-13T12:43:40Z)
• Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization [15.476478159958416]
  We employ a large language model (LLM) to enhance evolutionary search for solving constrained multi-objective optimization problems. Our aim is to speed up the convergence of the evolutionary population.
  arXiv  Detail & Related papers  (2024-05-09T13:44:04Z)
• Characterization of Large Language Model Development in the Datacenter [55.9909258342639]
  Large Language Models (LLMs) have presented impressive performance across several transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster resources to develop LLMs. We present an in-depth characterization study of a six-month LLM development workload trace collected from our GPU datacenter Acme.
  arXiv  Detail & Related papers  (2024-03-12T13:31:14Z)
• SparseLLM: Towards Global Pruning for Pre-trained Language Models [12.057369029549534]
  We propose SparseLLM, a novel framework that redefines the global pruning process into manageable, coordinated subproblems. SparseLLM's approach conceptualizes LLMs as a chain of modular functions and leverages auxiliary variables for problem decomposition. It demonstrates significant performance improvements, particularly in high-sparsity regimes.
  arXiv  Detail & Related papers  (2024-02-28T00:09:07Z)
• LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models [56.25156596019168]
  This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs) Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
  arXiv  Detail & Related papers  (2023-11-30T03:59:31Z)
• Improving Machine Translation with Large Language Models: A Preliminary Study with Cooperative Decoding [73.32763904267186]
  Large Language Models (LLMs) present the potential for achieving superior translation quality. We propose Cooperative Decoding (CoDec) which treats NMT systems as a pretranslation model and MT-oriented LLMs as a supplemental solution.
  arXiv  Detail & Related papers  (2023-11-06T03:41:57Z)
• Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration [83.4031923134958]
  Corex is a suite of novel general-purpose strategies that transform Large Language Models into autonomous agents. Inspired by human behaviors, Corex is constituted by diverse collaboration paradigms including Debate, Review, and Retrieve modes. We demonstrate that orchestrating multiple LLMs to work in concert yields substantially better performance compared to existing methods.
  arXiv  Detail & Related papers  (2023-09-30T07:11:39Z)

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.