LEADER 00822nam2 2200325 450 001 990001350800203316 005 20040116151119.0 035 $a000135080 035 $aUSA01000135080 035 $a(ALEPH)000135080USA01 035 $a000135080 100 $a20040116d1962----km-y0itay0103----ba 101 0 $aeng 102 $aUK 105 $a||||||||001yy 200 1 $a<<2>>: B: The basic material 215 $a3 v. 410 0$12001 454 1$12001 461 1$1001000135074$12001 700 1$aORTON,$bHarold$0191454 801 0$aIT$bsalbc$gISBD 912 $a990001350800203316 951 $aXII W 203$bL.M.$cXII W 959 $aBK 969 $aUMA 979 $aSIAV7$b10$c20040116$lUSA01$h1511 979 $aPATRY$b90$c20040406$lUSA01$h1735 996 $aB: The basic material$9926971 997 $aUNISA LEADER 03652nam 22006015 450 001 9910624393203321 005 20251113195022.0 010 $a3-031-13078-2 024 7 $a10.1007/978-3-031-13078-6 035 $a(MiAaPQ)EBC7127691 035 $a(Au-PeEL)EBL7127691 035 $a(CKB)25219360300041 035 $a(PPN)265856507 035 $a(OCoLC)1350356494 035 $a(DE-He213)978-3-031-13078-6 035 $a(EXLCZ)9925219360300041 100 $a20221029d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMaximum-Entropy Sampling $eAlgorithms and Application /$fby Marcia Fampa, Jon Lee 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (206 pages) 225 1 $aSpringer Series in Operations Research and Financial Engineering,$x2197-1773 311 08$aPrint version: Fampa, Marcia Maximum-Entropy Sampling Cham : Springer International Publishing AG,c2022 9783031130779 320 $aIncludes bibliographical references (pages 183-191) and index. 327 $aOverview -- Notation -- The problem and basic properties -- Branch-and-bound -- Upper bounds -- Environmental monitoring -- Opportunities -- Basic formulae and inequalities -- References -- Index. 330 $aThis monograph presents a comprehensive treatment of the maximum-entropy sampling problem (MESP), which is a fascinating topic at the intersection of mathematical optimization and data science. The text situates MESP in information theory, as the algorithmic problem of calculating a sub-vector of pre-specificed size from a multivariate Gaussian random vector, so as to maximize Shannon's differential entropy. The text collects and expands on state-of-the-art algorithms for MESP, and addresses its application in the field of environmental monitoring. While MESP is a central optimization problem in the theory of statistical designs (particularly in the area of spatial monitoring), this book largely focuses on the unique challenges of its algorithmic side. From the perspective of mathematical-optimization methodology, MESP is rather unique (a 0/1 nonlinear program having a nonseparable objective function), and the algorithmic techniques employed are highly non-standard. In particular, successful techniques come from several disparate areas within the field of mathematical optimization; for example: convex optimization and duality, semidefinite programming, Lagrangian relaxation, dynamic programming, approximation algorithms, 0/1 optimization (e.g., branch-and-bound), extended formulation, and many aspects of matrix theory. The book is mainly aimed at graduate students and researchers in mathematical optimization and data analytics. . 410 0$aSpringer Series in Operations Research and Financial Engineering,$x2197-1773 606 $aMathematical optimization 606 $aOperations research 606 $aManagement science 606 $aOptimization 606 $aOperations Research, Management Science 615 0$aMathematical optimization. 615 0$aOperations research. 615 0$aManagement science. 615 14$aOptimization. 615 24$aOperations Research, Management Science. 676 $a519.3 676 $a519.54 700 $aFampa$b Marcia$01264085 702 $aLee$b Jon 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910624393203321 996 $aMaximum-entropy sampling$93065688 997 $aUNINA