Stochastic Algorithms: Foundations and Applications: 5th International Symposium, SAGA 2009, Sapporo, Japan, October 26-28, 2009. Proceedings PDF
Preview Stochastic Algorithms: Foundations and Applications: 5th International Symposium, SAGA 2009, Sapporo, Japan, October 26-28, 2009. Proceedings
Lecture Notes in Computer Science 5792 CommencedPublicationin1973 FoundingandFormerSeriesEditors: GerhardGoos,JurisHartmanis,andJanvanLeeuwen EditorialBoard DavidHutchison LancasterUniversity,UK TakeoKanade CarnegieMellonUniversity,Pittsburgh,PA,USA JosefKittler UniversityofSurrey,Guildford,UK JonM.Kleinberg CornellUniversity,Ithaca,NY,USA AlfredKobsa UniversityofCalifornia,Irvine,CA,USA FriedemannMattern ETHZurich,Switzerland JohnC.Mitchell StanfordUniversity,CA,USA MoniNaor WeizmannInstituteofScience,Rehovot,Israel OscarNierstrasz UniversityofBern,Switzerland C.PanduRangan IndianInstituteofTechnology,Madras,India BernhardSteffen UniversityofDortmund,Germany MadhuSudan MicrosoftResearch,Cambridge,MA,USA DemetriTerzopoulos UniversityofCalifornia,LosAngeles,CA,USA DougTygar UniversityofCalifornia,Berkeley,CA,USA GerhardWeikum Max-PlanckInstituteofComputerScience,Saarbruecken,Germany Osamu Watanabe Thomas Zeugmann (Eds.) Stochastic Algorithms: Foundations and Applications 5th International Symposium, SAGA 2009 Sapporo, Japan, October 26-28, 2009 Proceedings 1 3 VolumeEditors OsamuWatanabe TokyoInstituteofTechnology DepartmentofMathematicalandComputingSciences W8-25,Tokyo152-8552,Japan E-mail:[email protected] ThomasZeugmann HokkaidoUniversity DivisionofComputerScience N-14,W-9,Sapporo060-0814,Japan E-mail:[email protected] LibraryofCongressControlNumber:2009935906 CRSubjectClassification(1998):F.2,F.1.2,G.1.2,G.1.6,G.2,G.3 LNCSSublibrary:SL1–TheoreticalComputerScienceandGeneralIssues ISSN 0302-9743 ISBN-10 3-642-04943-5SpringerBerlinHeidelbergNewYork ISBN-13 978-3-642-04943-9SpringerBerlinHeidelbergNewYork Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerialis concerned,specificallytherightsoftranslation,reprinting,re-useofillustrations,recitation,broadcasting, reproductiononmicrofilmsorinanyotherway,andstorageindatabanks.Duplicationofthispublication orpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLawofSeptember9,1965, initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Violationsareliable toprosecutionundertheGermanCopyrightLaw. springer.com ©Springer-VerlagBerlinHeidelberg2009 PrintedinGermany Typesetting:Camera-readybyauthor,dataconversionbyScientificPublishingServices,Chennai,India Printedonacid-freepaper SPIN:12773497 06/3180 543210 Preface The 5th Symposium on Stochastic Algorithms, Foundations and Applications (SAGA 2009) took place during October 26–28, 2009, at Hokkaido University, Sapporo(Japan).ThesymposiumwasorganizedbytheDivisionofComputerSci- ence,GraduateSchoolofComputerScienceandTechnology,HokkaidoUniversity. It offered the opportunity to present original research on the design and analysisofrandomizedalgorithms,randomcombinatorialstructures,implemen- tation, experimental evaluation and real-world application of stochastic algo- rithms/heuristics. In particular, the focus of the SAGA symposia series is on investigating the power of randomization in algorithms, and on the theory of stochastic processes especially within realistic scenarios and applications. Thus, thescopeofthesymposiumrangesfromthestudyoftheoreticalfundamentalsof randomizedcomputationtoexperimentalinvestigationsonalgorithms/heuristics and related stochastic processes. The SAGA symposium series is a biennial meeting. Previous SAGA sym- posiatookplaceinBerlin,Germany(2001,LNCSvol.2264),Hatfield,UK(2003, LNCS vol.2827),Moscow,Russia (2005,LNCS vol.3777),and Zu¨rich,Switzer- land (2007, LNCS vol. 4665). This year 22 submissions were received, and the Program Committee selected 15 submissions for presentation. All papers were evaluatedbyatleastthreemembersoftheProgramCommittee,partlywiththe assistance of subreferees. The present volume contains the texts of the 15 papers presented at SAGA 2009, divided into groups of papers on learning, graphs, testing, optimization, and caching as well as on stochastic algorithms in bioinformatics. The volume also contains the paper and abstract of the invited talks, respectively: – Werner Ro¨misch (Humboldt University, Berlin, Germany): Scenario Reduc- tion Techniques in Stochastic Programming – TaisukeSato(TokyoInstituteofTechnology,Tokyo,Japan):StatisticalLearn- ing of Probabilistic BDDs We would like to thank the many people and institutions who contributed to the success of the symposium. Thanks to the authors of the papers for their submissions,andtothe invitedspeakers,WernerR¨omischandTaisukeSato,for presentingexcitingoverviewsofimportantrecentresearchdevelopments.Weare very grateful to the sponsors of the symposium. SAGA 2009 was supported in part by two MEXT Global COE Programs: Center for Next-Generation Infor- mation Technology based on Knowledge Discovery and Knowledge Federation,at the Graduate School of Information Science and Technology, Hokkaido Univer- sity,and Computationism as a Foundation of the Sciences atTokyoInstitute of Technology. VI Preface We are grateful to the members of the ProgramCommittee for SAGA 2009. Theirhardworkinreviewinganddiscussingthepapersmadesurethatwehadan interestingandstrongprogram.WealsothankthesubrefereesassistingthePro- gram Committee. Special thanks go to Local Chair Shin-ichi Minato (Hokkaido University, Sapporo, Japan) for the local organization of the symposium. Last but not least, we thank Springer for the support provided by Springer’s On- line Conference Service (OCS), and for their excellent support in preparing and publishing this volume of the Lecture Notes in Computer Science series. October 2009 Osamu Watanabe Thomas Zeugmann Organization Conference Chair Thomas Zeugmann Hokkaido University, Sapporo, Japan Program Committee Andreas Albrecht Queen’s University Belfast, UK Amihood Amir Bar-Ilan University, Israel Artur Czumaj University of Warwick, UK Josep Dıaz Universitat Polit`ecnica de Catalunya, Barcelona, Spain Walter Gutjahr University of Vienna, Austria Juraj Hromkoviˇc ETH Zu¨rich, Switzerland Mitsunori Ogihara University of Miami, USA Pekka Orponen Helsinki University of Technology TKK, Finland Kunsoo Park Seoul National University, Korea Francesca Shearer Queen’s University Belfast, UK Paul Spirakis University of Patras, Greece Kathleen Steinho¨fel King’s College London, UK Masashi Sugiyama Tokyo Institute of Technology, Japan Osamu Watanabe (Chair) Tokyo Institute of Technology, Japan Masafumi Yamashita Kyushu University, Fukuoka, Japan Thomas Zeugmann Hokkaido University, Sapporo, Japan Local Arrangements Shin-ichi Minato Hokkaido University, Sapporo, Japan Subreferees Marta Arias Alex Fukunaga Mituhiro Fukuda Masaharu Taniguchi Sponsoring Institutions MEXTGlobalCOEProgramCenter for Next-Generation Information Technol- ogy based on Knowledge Discovery and Knowledge Federation at the Graduate School of Information Science and Technology, Hokkaido University. MEXT Global COE ProgramComputationism as a Foundation of the Sciences at Tokyo Institute of Technology. Table of Contents Invited Papers Scenario Reduction Techniques in Stochastic Programming............ 1 Werner Ro¨misch Statistical Learning of Probabilistic BDDs .......................... 15 Taisuke Sato Regular Contributions Learning Learning Volatility of Discrete Time Series Using Prediction with Expert Advice................................................... 16 Vladimir V. V’yugin Prediction of Long-Range Dependent Time Series Data with Performance Guarantee........................................... 31 Mikhail Dashevskiy and Zhiyuan Luo Bipartite Graph Representation of Multiple Decision Table Classifiers....................................................... 46 Kazuya Haraguchi, Seok-Hee Hong, and Hiroshi Nagamochi Bounds for Multistage Stochastic Programs Using Supervised Learning Strategies ....................................................... 61 Boris Defourny, Damien Ernst, and Louis Wehenkel On Evolvability: The Swapping Algorithm, Product Distributions, and Covariance ...................................................... 74 Dimitrios I. Diochnos and Gyo¨rgy Tura´n Graphs A Generic Algorithm for Approximately Solving Stochastic Graph Optimization Problems ........................................... 89 Ei Ando, Hirotaka Ono, and Masafumi Yamashita How to Design a Linear Cover Time Random Walk on a Finite Graph .......................................................... 104 Yoshiaki Nonaka, Hirotaka Ono, Kunihiko Sadakane, and Masafumi Yamashita X Table of Contents PropagationConnectivity of Random Hypergraphs................... 117 Robert Berke and Mikael Onsj¨o Graph Embedding through Random Walk for Shortest Paths Problems ....................................................... 127 Yakir Berchenko and Mina Teicher Testing, Optimization, and Caching Relational Properties Expressible with One Universal Quantifier Are Testable ........................................................ 141 Charles Jordan and Thomas Zeugmann Theoretical Analysis of Local Search in Software Testing .............. 156 Andrea Arcuri Firefly Algorithms for Multimodal Optimization ..................... 169 Xin-She Yang Economical Caching with Stochastic Prices.......................... 179 Matthias Englert, Berthold V¨ocking, and Melanie Winkler Stochastic Algorithms in Bioinformatics Markov Modelling of Mitochondrial BAK Activation Kinetics during Apoptosis....................................................... 191 C. Grills, D.A. Fennell, and S.F.C. Shearer Stochastic Dynamics of Logistic Tumor Growth...................... 206 S.F.C. Shearer, S. Sahoo, and A. Sahoo Author Index.................................................. 221 Scenario Reduction Techniques in Stochastic Programming Werner R¨omisch Humboldt-UniversityBerlin, Department of Mathematics, RudowerChaussee 25, 12489 Berlin, Germany Abstract. Stochastic programming problems appear as mathematical models for optimization problems under stochastic uncertainty. Most computational approaches for solvingsuch models arebased on approx- imating the underlying probability distribution by a probability mea- surewithfinitesupport.Sincethecomputationalcomplexityfor solving stochasticprogramsgetsworsewhenincreasingthenumberofatoms(or scenarios),itissometimesnecessarytoreducetheirnumber.Techniques forscenarioreductionoftenrequirefastheuristicsforsolvingcombinato- rial subproblems. Available techniques are reviewed and open problems are discussed. 1 Introduction Many stochastic programming problems may be reformulated in the form (cid:2) (cid:3) (cid:4) min E(f (x,ξ))= f (x,ξ)P(dξ):x∈X , (1) 0 0 Rs where X denotes a closed subset of Rm, the function f maps from Rm ×Rs 0 to the extended real numbers R=R∪{−∞,+∞}, E denotes expectation with respect to P and P is a probability distribution on Rs. For example, models of the form (1) appear as follows in economic applica- tions.Letξ ={ξt}Tt=1 denoteadiscrete-timestochasticprocessofd-dimensional random vectors ξt at each t ∈ {1,...,T} and assume that decisions xt have to bemadesuchthatthetotalcostsappearinginaneconomicprocessareminimal. Such an optimization model may often be formulated as (cid:2) (cid:5)(cid:6)T (cid:7) (cid:6)t−1 (cid:4) min E ft(xt,ξt) :xt ∈Xt, Atτ(ξt)xt−τ =ht(ξt), t=1,...,T . (2) t=1 τ=0 Typically,the sets Xt arepolyhedral,butthey mayalsocontainintegralitycon- ditions. In addition, the decision vector (x1,...,xT) has to satisfy a dynamic constraint (i.e. xt depends on the former decisions) and certain balancing con- ditions. The matrices Atτ(ξt), τ = 0,...,t−1, (e.g. containing technical pa- rameters)andtheright-handsidesht(ξt)(e.g.demands)are(partially)random. Thefunctionsft describethecostsattimetandmayalsobe(partially)random O.WatanabeandT.Zeugmann(Eds.):SAGA2009,LNCS5792,pp.1–14,2009. (cid:2)c Springer-VerlagBerlinHeidelberg2009
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