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While distributed asynchronous local search and inference methods for CA-DCOPs are less sensitive to latency than standard DCOP approaches, existing local search guarantees differ: regular DCOP algorithms can reach k-opt solutions, whereas CA-DCOP local search is limited to 1-opt solutions. This work presents LAMDLS-2, a monotonic paired-agent local search that coordinates bilateral replacements and converges to a 2-opt solution robustly under message latency.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/latency-aware-2-opt-monotonic-local-search-for-distributed-constraint-optimization/156007/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/latency-aware-2-opt-monotonic-local-search-for-distributed-constraint-optimization/156007.png","ImageObject",300,407,{"name":92,"@type":93},"Evangeline","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-08-28",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the paper address?","Question",{"text":113,"@type":114},"The paper targets distributed constraint optimization scenarios where communication delays occur, modeled via Communication-Aware DCOPs (CA-DCOPs). It aims to achieve stronger local-search optimality despite latency.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Why are standard DCOP local search algorithms limited under latency?",{"text":118,"@type":114},"Existing CA-DCOP local search methods are restricted to 1-opt solutions, unlike synchronous DCOP local search algorithms that can converge to k-opt solutions. The paper attributes the gap to communication assumptions that do not match real-world delayed networks.",{"name":120,"@type":111,"acceptedAnswer":121},"What is LAMDLS-2 and what guarantee does it provide?",{"text":122,"@type":114},"LAMDLS-2 forms agents into pairs and coordinates bilateral assignment replacements. It is monotonic, converges to a 2-opt solution, and remains robust to message latency, achieving faster convergence than the MGM-2 benchmark in tested latency scenarios.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},156007,1787942612,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},13056703019662,"https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188","arXiv :2504 .08737v1 [ cs .AI] 21 Feb 2025  \nLatency-Aware 2-Opt Monotonic Local Search for Distributed Constraint Optimization  \nBen Rachmut \\#   \nBen-Gurion University of the Negev Roie Zivan \\#   \nBen-Gurion University of the Negev William Yeoh \\#  Washington University in St. Louis  \n~~ Abstract ~~  \nResearchers recently extended Distributed Constraint Optimization Problems (DCOPs) to Communication-Aware DCOPs so that they are applicable in scenarios in which messages can be arbitrarily delayed. Distributed asynchronous local search and inference algorithms designed for CA-DCOPs are less vulnerable to message latency than their counterparts for regular DCOPs. However, unlike local search algorithms for (regular) DCOPs that converge to k-opt solutions (with k > 1), that is, they converge to solutions that cannot be improved by a group of k agents), local search CA-DCOP algorithms are limited to 1-opt solutions only.  \nIn this paper, we introduce Latency-Aware Monotonic Distributed Local Search-2 (LAMDLS-2), where agents form pairs and coordinate bilateral assignment replacements. LAMDLS-2 is monotonic, converges to a 2-opt solution, and is also robust to message latency, making it suitable for CA-DCOPs. Our results indicate that LAMDLS-2 converges faster than MGM-2, a benchmark algorithm, to a similar 2-opt solution, in various message latency scenarios.  \n2012 ACM Subject Classification Theory of computation → Distributed algorithms  \nKeywords and phrases Distributed Constraint Optimization Problems; Distributed Local Search Algorithms; Latency Awareness; Multi-Agent Optimization  \nDigital Object Identifier 10.4230/LIPIcs.CP.2024.9  \nSupplementary Material Software: [https://github.com/benrachmut/CADCOP_CP_2024](https://github.com/benrachmut/CADCOP_CP_2024)  \nAcknowledgements This research is partially supported by US-Israel Binational Science Foundation (BSF) grant \\#2018081 .  \n 1  Introduction  \nA promising multi-agent approach for addressing distributed applications, where agents aim to achieve mutual optimization goals, is by modeling them as Distributed Constraint Optimization Problems (DCOPs) [12, 16 , 5] . An illustrative example of such an application is a smart home, where various smart devices must coordinate to create a schedule that optimizes user preferences and satisfies constraints [6, 19] . In this context, decision-makers are represented as “agents” that assign “values” to their respective “variables”, and the objective is to optimize a global objective in a decentralized manner.  \nDCOPs are NP-hard [12] and, thus, considerable research effort has been devoted to developing incomplete algorithms for finding good solutions quickly [23, 10 , 24 , 3 , 4 , 20 , 8 , 14] . Distributed local search algorithms such as Distributed Stochastic Algorithm (DSA) [24] and Maximum Gain Message (MGM) [10] are two of the most popular incomplete DCOP algorithms.  \nMost state-of-the-art local search DCOP algorithms (including DSA and MGM) are synchronous. However, the general setting in which agents operate is inherently asynchronous.  \n© Ben Rachmut, Roie Zivan and William Yeoh;  \nlicensed under Creative Commons License CC-BY 4.0  \n30th International Conference on Principles and Practice of Constraint Programming (CP 2024) .  \nEditor: Paul Shaw; Article No. 9; pp. 9:1–9:17  \nLeibniz International Proceedings in Informatics  \nSchloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Germany  \n9:2 Latency-Aware 2-Opt Monotonic Local Search for DCOPs  \nSynchronization is achieved through message exchanges in each iteration of the algorithm, in which an agent receives messages sent by its neighbors in the previous iteration, performs computation, and sends messages to all its neighbors [24, 26] . This ensures that at iteration k , an agent has access to all information sent to it during iteration k −1 . The synchronous design enables the attainment of some desirable properties. For example, MGM agents achieve mono","cbCaifNUvJqOJy1a","https://ap.wps.com/l/cbCaifNUvJqOJy1a","pdf",876343,17,"English","# Introduction\n## Background on DCOPs and local search\n## Latency challenges and communication-aware settings\n## Related work on k-opt and asynchronous algorithms\n## LAMDLS-2 approach (latency-aware, monotonic, 2-opt)","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper targets distributed constraint optimization scenarios where communication delays occur, modeled via Communication-Aware DCOPs (CA-DCOPs). It aims to achieve stronger local-search optimality despite latency.\"},{\"question\":\"Why are standard DCOP local search algorithms limited under latency?\",\"answer\":\"Existing CA-DCOP local search methods are restricted to 1-opt solutions, unlike synchronous DCOP local search algorithms that can converge to k-opt solutions. The paper attributes the gap to communication assumptions that do not match real-world delayed networks.\"},{\"question\":\"What is LAMDLS-2 and what guarantee does it provide?\",\"answer\":\"LAMDLS-2 forms agents into pairs and coordinates bilateral assignment replacements. It is monotonic, converges to a 2-opt solution, and remains robust to message latency, achieving faster convergence than the MGM-2 benchmark in tested latency scenarios.\"}]","Latency-Aware 2-Opt Monotonic Local Search for Distributed Constraint Optimization | PDF",43]