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J.-C$93019879 997 $aUNINA LEADER 07302nam 2201861 a 450 001 9910789748903321 005 20220308025052.0 010 $a1-283-25611-8 010 $a9786613256119 010 $a1-4008-4110-0 024 7 $a10.1515/9781400841103 035 $a(CKB)2670000000114042 035 $a(EBL)768550 035 $a(OCoLC)749265038 035 $a(SSID)ssj0000545200 035 $a(PQKBManifestationID)11362484 035 $a(PQKBTitleCode)TC0000545200 035 $a(PQKBWorkID)10554795 035 $a(PQKB)10026176 035 $a(DE-B1597)447249 035 $a(OCoLC)1054881878 035 $a(OCoLC)979579454 035 $a(DE-B1597)9781400841103 035 $a(Au-PeEL)EBL768550 035 $a(CaPaEBR)ebr10496632 035 $a(CaONFJC)MIL325611 035 $a(MiAaPQ)EBC768550 035 $a(EXLCZ)992670000000114042 100 $a20060724d2006 uy 0 101 0 $aeng 135 $aurnnu---|u||u 181 $ctxt 182 $cc 183 $acr 200 14$aThe traveling salesman problem$b[electronic resource] $ea computational study /$fDavid L. Applegate ... [et al.] 205 $aCourse Book 210 $aPrinceton $cPrinceton University Press$dc2006 215 $a1 online resource (606 p.) 225 1 $aPrinceton series in applied mathematics 300 $a"A Princeton University Press e-book."--Cover. 311 0 $a0-691-12993-2 320 $aIncludes bibliographical references (p. [541]-581) and index. 327 $tFront matter --$tContents --$tPreface --$tChapter 1. The Problem --$tChapter 2. Applications --$tChapter 3. Dantzig, Fulkerson, and Johnson --$tChapter 4. History of TSP Computation --$tChapter 5. LP Bounds and Cutting Planes --$tChapter 6. Subtour Cuts and PQ-Trees --$tChapter 7. Cuts from Blossoms and Blocks --$tChapter 8. Combs from Consecutive Ones --$tChapter 9. Combs from Dominoes --$tChapter 10. Cut Metamorphoses --$tChapter 11. Local Cuts --$tChapter 12. Managing the Linear Programming Problems --$tChapter 13. The Linear Programming Solver Chapter 14. Branching --$tChapter 14. Branching --$tChapter 15. Tour Finding --$tChapter 16. Computation --$tChapter 17. The Road Goes On --$tBibliography --$tIndex 330 $aThis book presents the latest findings on one of the most intensely investigated subjects in computational mathematics--the traveling salesman problem. It sounds simple enough: given a set of cities and the cost of travel between each pair of them, the problem challenges you to find the cheapest route by which to visit all the cities and return home to where you began. Though seemingly modest, this exercise has inspired studies by mathematicians, chemists, and physicists. Teachers use it in the classroom. It has practical applications in genetics, telecommunications, and neuroscience. The authors of this book are the same pioneers who for nearly two decades have led the investigation into the traveling salesman problem. They have derived solutions to almost eighty-six thousand cities, yet a general solution to the problem has yet to be discovered. Here they describe the method and computer code they used to solve a broad range of large-scale problems, and along the way they demonstrate the interplay of applied mathematics with increasingly powerful computing platforms. They also give the fascinating history of the problem--how it developed, and why it continues to intrigue us. 410 0$aPrinceton series in applied mathematics. 606 $aTraveling salesman problem 610 $aAT&T Labs. 610 $aAccuracy and precision. 610 $aAddition. 610 $aAlgorithm. 610 $aAnalysis of algorithms. 610 $aApplied mathematics. 610 $aApproximation algorithm. 610 $aApproximation. 610 $aBasic solution (linear programming). 610 $aBest, worst and average case. 610 $aBifurcation theory. 610 $aBig O notation. 610 $aCPLEX. 610 $aCPU time. 610 $aCalculation. 610 $aChaos theory. 610 $aColumn generation. 610 $aCombinatorial optimization. 610 $aComputation. 610 $aComputational resource. 610 $aComputer. 610 $aConnected component (graph theory). 610 $aConnectivity (graph theory). 610 $aConvex hull. 610 $aCutting-plane method. 610 $aDelaunay triangulation. 610 $aDeterminism. 610 $aDisjoint sets. 610 $aDynamic programming. 610 $aEar decomposition. 610 $aEngineering. 610 $aEnumeration. 610 $aEquation. 610 $aEstimation. 610 $aEuclidean distance. 610 $aEuclidean space. 610 $aFamily of sets. 610 $aFor loop. 610 $aGenetic algorithm. 610 $aGeorge Dantzig. 610 $aGeorgia Institute of Technology. 610 $aGreedy algorithm. 610 $aHamiltonian path. 610 $aHospitality. 610 $aHypergraph. 610 $aImplementation. 610 $aInstance (computer science). 610 $aInstitute. 610 $aInteger. 610 $aIteration. 610 $aLinear inequality. 610 $aLinear programming. 610 $aMathematical optimization. 610 $aMathematics. 610 $aModel of computation. 610 $aNeuroscience. 610 $aNotation. 610 $aOperations research. 610 $aOptimization problem. 610 $aOrder by. 610 $aPairwise. 610 $aParameter (computer programming). 610 $aParity (mathematics). 610 $aPercentage. 610 $aPolyhedron. 610 $aPolytope. 610 $aPricing. 610 $aPrinceton University. 610 $aProcessing (programming language). 610 $aProject. 610 $aQuantity. 610 $aReduced cost. 610 $aRequirement. 610 $aResult. 610 $aRice University. 610 $aRutgers University. 610 $aScientific notation. 610 $aSearch algorithm. 610 $aSearch tree. 610 $aSelf-similarity. 610 $aSimplex algorithm. 610 $aSolution set. 610 $aSolver. 610 $aSource code. 610 $aSpecial case. 610 $aStochastic. 610 $aSubroutine. 610 $aSubsequence. 610 $aSubset. 610 $aSummation. 610 $aTest set. 610 $aTheorem. 610 $aTheory. 610 $aTime complexity. 610 $aTrade-off. 610 $aTravelling salesman problem. 610 $aTree (data structure). 610 $aUpper and lower bounds. 610 $aVariable (computer science). 610 $aVariable (mathematics). 615 0$aTraveling salesman problem. 676 $a511.6 700 $aApplegate$b David L$01495294 702 $aBixby$b Robert E.$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aChvátal$b Va?ek$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910789748903321 996 $aThe traveling salesman problem$93719343 997 $aUNINA