[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-135385-en":3,"doc-seo-135385-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},135385,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Probabilistic Techniques for Constraint Satisfaction Problems","Constraint satisfaction problems (CSPs) are central to practical tasks such as hardware/software verification, planning, and scheduling. Traditional solutions often rely on backtrack-style search or local search, while survey propagation (SP) has shown strong performance on large instances. This dissertation presents non-traditional methods combining probabilistic inference, statistical tools, and hybrid systematic/local search. It derives SP from combinatorial insights, develops SP-style procedures for varied CSPs, and addresses solution counting and optimization under violated constraints using scalable approaches for SAT, #SAT, and MaxSAT.","PROBABILISTIC TECHNIQUES FOR CONSTRAINT SATISFACTION PROBLEMS  \nA Dissertation  \nPresented to the Faculty of the Graduate School of Cornell University  \nIn Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy  \nby  \nLukas Kroc  \nPROBABILISTIC TECHNIQUES FOR CONSTRAINT SATISFACTION  \nPROBLEMS  \nLukas Kroc, Ph.D.  \nCornell University 2009  \nConstraint satisfaction problems (CSPs) are at the core of many tasks with direct practical relevance, such as hardware and software veri􀀌cation, planning, and scheduling to name a few. The two main solution paradigms for solving such problems are based on backtrack-style search and local search. However, recently, anew powerful technique, called survey propagation (SP), was introduced. SP can solve certain classes of problem instances with millions of variables and constraints. This discovery raised a question of whether the traditional techniques are the best one can do when tackling large and important constraint satisfaction problems.  \nThis dissertation discusses non-traditional approaches to solving CSPs. The techniques we use include probabilistic inference, statistical tools and a combination of systematic and local search. We provide a new derivation of SP based on purely combinatorial insights. Our method also enables us to derive SP-style procedures for a diverse range of constraint satisfaction problems, and provides insights into structure of solution spaces. The second part of the dissertation describes approaches to counting number of solutions of a CSP with the help of Belief Propagation and statistics. We also provide a novel hybrid algorithm for minimizing the number of violated constraints, which employs both systematic and local search, harnessing their complementary strengths. These methods are the most scalable algorithms for certain hard to solve classes of Boolean satis􀀌ability (SAT), model counting (\\#SAT), and maximum satis􀀌ability (MaxSAT) problems.  \nBIOGRAPHICAL SKETCH  \nLukas Kroc has earned his PhD degreed in computer science at Cornell University in 2009. His research interests lie in the 􀀌eld of arti􀀌cial intelligence, with focus on using techniques of probabilistic inference in combinatorial reasoning. Prior to his PhD time he worked at Los Alamos National Laboratory on large scale simulation design and development. Lukas received a MSc in computer science from Charles University, Prague, in 2004.  \nTo Beatrice, for being an eternal inspiration.  \nThus spoke Ulysses to Dante the Pilgrim about his journey to Ithaca [Alighieri and Musa, 2002]:  \nNot sweetness of a son, not reverence  \nfor an aging father, not the debt of love  \nI owed Penelope to make her happy,  \ncould quench deep in myself the burning wish  \nto know the world and have experience  \nof all man's vices, of all human worth.  \nACKNOWLEDGEMENTS  \nI would like to extend my sincerest gratitude towards people who have helped me with this dissertation, and have supported me during my time at Cornell. This, of course, includes the members of my committee, Michael Spivey, John Hopcroft, and my advisor Bart Selman. Bart's very encouraging and supportive attitude in particular was a great help during my studies. Above all, I appreciated very much his friendly approach to me, and his good understanding of basic human needs, the search for happiness being one of the more important ones.  \nMy stay in Ithaca would not have been possible without the constant support from many of my friends, who I dare not list here fearing that I might unintentionally omit some. I want to thank them all from the bottom of my heart for sharing parts of their lives with me, and for showing me many a thing that I did not even know I had been missing before. However, one name must be mentioned, as he not only supported me as a friend, but also very much as a researcher: Ashish Sabharwal. Ashish was a post-doctoral researcher at Cornell at the time, and all the research described in this dissertation was di","cbCainbCqirXqcwz","https://ap.wps.com/l/cbCainbCqirXqcwz","pdf",3938305,3,1,200,"English","en",105,"# Introduction\n## Problem Classes\n## Solution Techniques\n## Technical Contributions\n# Preliminaries\n## Boolean Satisfiability and Graph Coloring Problems\n## Factor Graphs and Probabilistic Inference\n## Approximate Probabilistic Inference Using Belief Propagation\n# Message-Passing and Local Heuristics as Decimation Strategies for Satisfiability\n## Solving SAT by Decimation\n## Decimation Strategies\n## Observable Differences In Decimation Strategies\n## Discussion\n# Survey Propagation Revisited\n## Covers of CNF Formulas\n## Problem Reformulation: From Solutions To Covers\n## Inference Over Covers\n## Derivation of the SP Equations\n## Experimental Results","[{\"question\":\"What are constraint satisfaction problems (CSPs) used for?\",\"answer\":\"CSPs appear in many real-world tasks, including hardware and software verification, planning, and scheduling.\"},{\"question\":\"How does the dissertation position survey propagation (SP) compared with traditional techniques?\",\"answer\":\"Traditional approaches use backtrack-style search or local search, while SP is presented as a powerful technique that can solve certain large instances. The dissertation investigates whether traditional methods are optimal for large, important CSPs.\"},{\"question\":\"What main contributions does the dissertation make for solving and analyzing CSPs?\",\"answer\":\"It derives and extends SP using combinatorial insights, develops SP-style procedures for diverse CSPs, addresses counting solutions via Belief Propagation and statistics, and proposes a novel hybrid algorithm to minimize violated constraints.\"}]","Probabilistic Techniques for Constraint Satisfaction Problems | PDF",1787310503,504,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"probabilistic-techniques-for-constraint-satisfaction-problems","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/probabilistic-techniques-for-constraint-satisfaction-problems/135385/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-09-01","2026-08-21",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What are constraint satisfaction problems (CSPs) used for?","Question",{"text":76,"@type":77},"CSPs appear in many real-world tasks, including hardware and software verification, planning, and scheduling.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the dissertation position survey propagation (SP) compared with traditional techniques?",{"text":81,"@type":77},"Traditional approaches use backtrack-style search or local search, while SP is presented as a powerful technique that can solve certain large instances. 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