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001468311 001__ 1468311
001468311 003__ OCoLC
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001468311 008__ 230530s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001468311 019__ $$a1380389037$$a1380463868
001468311 020__ $$a9783031332715$$q(electronic bk.)
001468311 020__ $$a3031332717$$q(electronic bk.)
001468311 020__ $$z9783031332708
001468311 020__ $$z3031332709
001468311 0247_ $$a10.1007/978-3-031-33271-5$$2doi
001468311 035__ $$aSP(OCoLC)1380721651
001468311 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP
001468311 049__ $$aISEA
001468311 050_4 $$aQA76.612
001468311 08204 $$a005.1/16$$223/eng/20230530
001468311 1112_ $$aCPAIOR (Conference : 2014- )$$n(20th :$$d2023 :$$cNice, France)
001468311 24510 $$aIntegration of constraint programming, artificial intelligence, and operations research :$$b20th International Conference, CPAIOR 2023, Nice, France, May 29-June 1, 2023, Proceedings /$$cAndre A. Cire, editors.
001468311 2463_ $$aCPAIOR 2023
001468311 264_1 $$aCham, Switzerland :$$bSpringer,$$c2023.
001468311 300__ $$a1 online resource (xi, 512 pages) :$$billustrations (some color).
001468311 336__ $$atext$$btxt$$2rdacontent
001468311 337__ $$acomputer$$bc$$2rdamedia
001468311 338__ $$aonline resource$$bcr$$2rdacarrier
001468311 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v13884
001468311 500__ $$aIncludes author index.
001468311 5050_ $$aIntro -- Preface -- Organization -- Contents -- Efficiently Approximating High-Dimensional Pareto Frontiers for Tree-Structured Networks Using Expansion and Compression -- 1 Introduction -- 2 Problem Formulation -- 3 The Expansion Method -- 4 The Compression Method -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Evaluation Method -- 5.3 Experimental Results -- 5.4 Ablation Study -- 6 Conclusion -- References -- Objective-Based Counterfactual Explanations for Linear Discrete Optimization -- 1 Introduction -- 2 Background -- 2.1 Counterfactual Explanations
001468311 5058_ $$a2.2 Nearest Counterfactual Explanations -- 2.3 Inverse Combinatorial Optimization -- 3 Problem Definition -- 3.1 Existence of an Explanation -- 4 The NCXplain Algorithm -- 5 Experimental Method -- 5.1 Forward Problems -- 5.2 NCEMILP Instances -- 5.3 Computational Details -- 6 Experimental Results -- 7 Limitations and Future Work -- 8 Related Work -- 9 Conclusion -- References -- Column Elimination for Capacitated Vehicle Routing Problems -- 1 Introduction -- 2 Column Formulation for CVRP -- 3 Decision Diagram Formulation for CVRP -- 3.1 From Dynamic Programming to Decision Diagrams
001468311 5058_ $$a3.2 Dynamic Programming for Route Relaxations -- 3.3 Exact and Relaxed Decision Diagrams -- 3.4 Constrained Network Flow Formulation -- 4 Column Elimination Procedure -- 5 Lagrangian Relaxation -- 6 Cutting Planes -- 7 Reduced Cost-Based Arc Fixing -- 8 Experimental Results -- 9 Conclusion -- References -- Cutting Plane Selection with Analytic Centers and Multiregression -- 1 Introduction -- 2 Related Work -- 3 Contributions and Methodology -- 3.1 Analytic Center-Based Methods -- 3.2 Multiple LP Solutions -- 3.3 Properties and Limitations of the Distance Measures -- 3.4 Multi-output Regression
001468311 5058_ $$a4 Experiments -- 4.1 Root Node Results -- 4.2 Branch and Bound Generalisation -- 4.3 Regression Model Results -- 5 Conclusion -- References -- Handling Symmetries in Mixed-Integer Semidefinite Programs -- 1 Introduction -- 2 Computing Symmetries -- 3 Symmetry Detection -- 4 Computational Results -- References -- A Mixed-Integer Linear Programming Reduction of Disjoint Bilinear Programs via Symbolic Variable Elimination -- 1 Introduction -- 2 Reducing a DBLP to a MILP: A Worked Example -- 3 Symbolic Calculus with Case Representation -- 3.1 Case Representation -- 3.2 Basic Case Operators
001468311 5058_ $$a4 Symbolic Reduction of a DBLP to a MILP -- 4.1 Symbolic Minimization of Linear Piecewise Linear Functions -- 4.2 Symbolic Minimization of Disjointly Linear Piecewise Bilinear Functions -- 5 Empirical Analysis -- 6 Conclusion and Future Work -- References -- Local Branching Relaxation Heuristics for Integer Linear Programs -- 1 Introduction -- 2 Background -- 2.1 ILP and Its LP Relaxation -- 2.2 LNS for ILP Solving -- 2.3 LB Heuristic -- 3 Related Work -- 3.1 LNS for ILPs -- 3.2 LNS-Based Primal Heuristics in BnB -- 3.3 LNS for Other COPs -- 4 The Local Branching Relaxation Heuristic
001468311 506__ $$aAccess limited to authorized users.
001468311 520__ $$aThis book constitutes the proceedings of the 20th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2022, held in Nice, France, during May 29-June 1, 2023. The 26 full papers and the 6 short papers presented in this book were carefully reviewed and selected from a total of 71 submissions. The content of the papers present new techniques or new applications, and provide an opportunity for researchers in one area to learn about techniques in the others. Besides they give researchers the opportunity to show how the integration of techniques from different fields can lead to interesting results on large and complex problems.
001468311 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed May 30, 2023).
001468311 650_0 $$aConstraint programming (Computer science)$$vCongresses.
001468311 650_0 $$aCombinatorial optimization$$xData processing$$vCongresses.
001468311 650_0 $$aArtificial intelligence$$vCongresses.
001468311 655_0 $$aElectronic books.
001468311 7001_ $$aCire, Andre A.$$eeditor.$$0(orcid)0000-0001-5993-4295$$1https://orcid.org/0000-0001-5993-4295
001468311 77608 $$iPrint version: $$z3031332709$$z9783031332708$$w(OCoLC)1375992250
001468311 830_0 $$aLecture notes in computer science ;$$v13884.$$x1611-3349
001468311 852__ $$bebk
001468311 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-33271-5$$zOnline Access$$91397441.1
001468311 909CO $$ooai:library.usi.edu:1468311$$pGLOBAL_SET
001468311 980__ $$aBIB
001468311 980__ $$aEBOOK
001468311 982__ $$aEbook
001468311 983__ $$aOnline
001468311 994__ $$a92$$bISE