[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-267801-105":59,"doc-detail-267801-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","better-decision-heuristics-in-cdcl-through-local-search-and-target-phases","Better Decision Heuristics in CDCL through Local Search and Target Phases","","State-of-the-art SAT solvers typically rely on conflict-driven clause learning (CDCL), while local search methods often excel on random and hard satisfiable instances. Missing from prior work is a tight integration that improves performance broadly across applications. This paper maximizes CDCL’s assignment trail using target phases, extends CDCL while relaxing conflicts to build improved complete assignments, refines them via local search, and feeds results back into CDCL decision heuristics using local-search variable conflict frequencies.",{"@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/better-decision-heuristics-in-cdcl-through-local-search-and-target-phases/267801/",{"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/better-decision-heuristics-in-cdcl-through-local-search-and-target-phases/267801.png","ImageObject",300,407,{"name":92,"@type":93},"Mason","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-14",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How does the paper integrate local search with CDCL?","Question",{"text":113,"@type":114},"It constructs improved complete assignments using relaxed CDCL guidance, refines them with local search, then imports the results back into the CDCL loop to influence decision variable values and branching heuristics.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What are target phases in the proposed method?",{"text":118,"@type":114},"Target phases maximize the CDCL assignment trail by sticking to and extending promising assignments in a local-search-like fashion within CDCL.",{"name":120,"@type":111,"acceptedAnswer":121},"Which SAT solvers and benchmarks are used to evaluate the techniques?",{"text":122,"@type":114},"The implementation improves three representative CDCL solvers—Glucose, Maple Lcm Dist Chrono BT, and Kissat—and is tested on benchmarks from the main tracks of SAT Competitions 2019–2021 plus a spectrum allocation benchmark set.","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},267801,1789425069,{"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},5909887256941,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Better Decision Heuristics in CDCL through Local Search and Target Phases  \nShaowei Cai [shaoweicai.cs@gmail.com](shaoweicai.cs@gmail.com)  \nXindi Zhang [zhangxd@ios.ac.cn](zhangxd@ios.ac.cn)  \nState Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences Beijing, China  \nSchool of Computer Science and Technology, University of Chinese Academy of Sciences Beijing, China  \nMathias Fleury [fleury@cs.uni-freiburg.de](fleury@cs.uni-freiburg.de)  \nArmin Biere [biere@cs.uni-freiburg.de](biere@cs.uni-freiburg.de)  \n[Albert-Ludwigs-University Freiburg](Albert-Ludwigs-University Freiburg), [Freiburg](Freiburg), [Germany](Germany)  \n[Johannes-Kepler-University Linz](Johannes-Kepler-University Linz), [Linz](Linz), [Austria](Austria)  \nAbstract  \nOn practical applications, state-of-the-art SAT solvers dominantly use the con􀀍ictdriven clause learning (CDCL) paradigm. An alternative for satis􀀌able instances is local search solvers, which is more successful on random and hard combinatorial instances. Although there have been attempts to combine these methods in one framework, a tight integration which improves the state of the art on a broad set of application instances has been missing. We present a combination of techniques that achieves such an improvement. Our 􀀌rst contribution is to maximize in a local search fashion the assignment trail in CDCL, by sticking to and extending promising assignments via a technique called target phases. Second, we relax the CDCL framework by again extending promising branches to complete assignments while ignoring con􀀍icts. These assignments are then used as starting point of local search which tries to 􀀌nd improved assignments with fewer unsatis􀀌ed clauses. Third, these improved assignments are imported back to the CDCL loop where they are used to determine the value assigned to decision variables. Finally, the con􀀍ict frequency of variables in local search can be exploited during variable selection in branching heuristics of CDCL. We implemented these techniques to improve three representative CDCL solvers (Glucose, Maple Lcm Dist Chrono BT, and Kissat) . Experiments on benchmarks from the main tracks of the last three SAT Competitions from 2019 to 2021 and an additional benchmark set from spectrum allocation show that the techniques bring signi􀀌cant improvements, particularly and not surprisingly, on satis􀀌able real-world application instances. We claim that these techniques were essential to the large increase in performance witnessed in the SAT Competition 2020 where Kissat and Relaxed Lcmd CbDl NewTech were leading the 􀀌eld followed by CryptoMiniSAT-Ccnr, which also incorporated similar ideas.  \n©2022 AI Access Foundation. All rights reserved.  \nCai, Zhang, Fleury & Biere  \n1. Introduction  \nThe satis􀀌ability problem (SAT) asks to determine whether a given propositional formula is satis􀀌able or not. Propositional formulas are usually represented in conjunctive normal form (CNF) . A growing number of problem domains are successfully tackled by SAT solvers, including electronic design automation (EDA) (Silva & Sakallah, 2000), particularly hardware veri􀀌cation (Prasad, Biere, & Gupta, 2005) and model checking (Vizel, Weissenbacher,& Malik, 2015; Biere & Kr􀁿oning, 2018), mathematical theorem proving (Heule, Kullmann,& Marek, 2016), AI planning (Kautz & Selman, 1992), and spectrum allocation (Newman, Fr􀀓echette, & Leyton-Brown, 2018), among others. Additionally, SAT solvers are also often used as a core component of more complex tools such as solvers for satis􀀌ability modulo theory (SMT) (Barrett, Sebastiani, Seshia, & Tinelli, 2021), which form a crucial component of state-of-the-art program analysis and software veri􀀌cation.  \nMany approaches have been proposed to solve SAT, but con􀀍ict-driven clause learning (CDCL) and local search are the most popular ones. Since their inception in the mid-90s, CDCL-based SAT solvers have been applied, in many cases with remarkable succ","cbCainFD3fHDK0L3","https://ap.wps.com/l/cbCainFD3fHDK0L3","pdf",1442570,49,"English","# Introduction\n## SAT problem and solver paradigms\n## Local search and CDCL limitations\n## Prior hybrid attempts and motivation","[{\"question\":\"How does the paper integrate local search with CDCL?\",\"answer\":\"It constructs improved complete assignments using relaxed CDCL guidance, refines them with local search, then imports the results back into the CDCL loop to influence decision variable values and branching heuristics.\"},{\"question\":\"What are target phases in the proposed method?\",\"answer\":\"Target phases maximize the CDCL assignment trail by sticking to and extending promising assignments in a local-search-like fashion within CDCL.\"},{\"question\":\"Which SAT solvers and benchmarks are used to evaluate the techniques?\",\"answer\":\"The implementation improves three representative CDCL solvers—Glucose, Maple Lcm Dist Chrono BT, and Kissat—and is tested on benchmarks from the main tracks of SAT Competitions 2019–2021 plus a spectrum allocation benchmark set.\"}]","Better Decision Heuristics in CDCL through Local Search and Target Phases | PDF",123]