The.Algorithm.Design.Manual.Springer-Verlag.1998

More documents

Recommendations

Info

Graph Problems: Polynomial-Time Next: Connected Components Up: A Catalog of Algorithmic Previous: Satisfiability Graph Problems: Polynomial-Time Algorithmic graph problems constitute approximately one third of all the problems in this catalog. Further, several problems from other sections can be formulated strictly in terms of graphs. Identifying the name of a graph-theoretic invariant or problem is one of the primary skills of a good algorist. Indeed, this catalog will tell you exactly how to proceed as soon as you figure out your particular problem's name. We have partitioned the bulk of the algorithmic graph problems in this book between this and the subsequent section. Here, we deal only with problems for which there exist efficient algorithms to solve them. As there is often more than one way to model a given application, it makes sense to look here before proceeding on to the harder formulations. The algorithms presented here have running times that grow slowly with the size of the graph. We adopt throughout the standard convention that n refers to the number of vertices in a graph, while m is the number of edges. Although graphs are combinatorial objects, describing a binary relation on a set of objects, graphs are usually best understood as drawings. Beyond just the problems of visualization, many interesting graph properties follow from the nature of a particular type of drawing, such as planar graphs. In this chapter, we also present a variety of different problems and algorithms associated with graph drawing. Most advanced graph algorithms are difficult to program. However, good implementations are often available if you know where to look. The best single source is LEDA, discussed in Section , although faster special-purpose codes exist for many problems. Books specializing in graph algorithms include: ● Even [Eve79a] - This is a good book on graph algorithms, fairly advanced, with a particularly thorough treatment of planarity-testing algorithms. ● Ahuja, Magnanti, and Orlin [AMO93] - While purporting to be a book on network flows, it covers the gamut of graph algorithms with emphasis on operations research. Strongly file:///E|/BOOK/BOOK4/NODE158.HTM (1 of 2) [19/1/2003 1:30:53]
Graph Problems: Polynomial-Time recommended. ● van Leeuwen [vL90a] - A 100+ page survey on research results in graph algorithms, this is the best source to determine what is known in algorithmic graph theory. ● McHugh [McH90] - A more elementary but comprehensive treatment of basic graph algorithms, including parallel algorithms. ● Connected Components ● Topological Sorting ● Minimum Spanning Tree ● Shortest Path ● Transitive Closure and Reduction ● Matching ● Eulerian Cycle / Chinese Postman ● Edge and Vertex Connectivity ● Network Flow ● Drawing Graphs Nicely ● Drawing Trees ● Planarity Detection and Embedding Next: Connected Components Up: A Catalog of Algorithmic Previous: Satisfiability Algorithms Mon Jun 2 23:33:50 EDT 1997 file:///E|/BOOK/BOOK4/NODE158.HTM (2 of 2) [19/1/2003 1:30:53]
Page 1 and 2:
The Algorithm Design Manual Next: P
Page 3 and 4:
Preface Next: Acknowledgments Up: T
Page 5 and 6:
Preface one stressing design over a
Page 7 and 8:
Acknowledgments Mon Jun 2 23:33:50
Page 9 and 10:
Contents Next: Techniques Up: The A
Page 11 and 12:
Contents ■ All-Pairs Shortest Pat
Page 13 and 14:
Contents ■ Graph Problems: Polyno
Page 15 and 16:
Contents ● About this document ..
Page 17 and 18:
The Algorithm Design Manual The Alg
Page 19 and 20:
Lecture Notes -- Analysis of Algori
Page 21 and 22:
Lecture Notes -- Analysis of Algori
Page 23 and 24:
The Stony Brook Algorithm Repositor
Page 25 and 26:
Techniques Next: Introduction to Al
Page 27 and 28:
Techniques Algorithms Mon Jun 2 23:
Page 29 and 30:
Introduction to Algorithms ● Reas
Page 31 and 32:
Data Structures and Sorting divide-
Page 33 and 34:
Breaking Problems Down ● Dynamic
Page 35 and 36:
Graph Algorithms The take-home less
Page 37 and 38:
Combinatorial Search and Heuristic
Page 39 and 40:
Intractable Problems and Approximat
Page 41 and 42:
How to Design Algorithms Next: Reso
Page 43 and 44:
How to Design Algorithms with a cou
Page 45 and 46:
How to Design Algorithms Next: Reso
Page 47 and 48:
A Catalog of Algorithmic Problems N
Page 49 and 50:
A Catalog of Algorithmic Problems
Page 51 and 52:
Algorithmic Resources Next: Softwar
Page 53 and 54:
References L. Adleman. Algorithmic
Page 55 and 56:
References AS89 Ata83 Ata84 problem
Page 57 and 58:
References BJL 91 A. Blum, T. Jiang
Page 59 and 60:
References BS81 BS86 BS93 BS96 BS97
Page 61 and 62:
References J. M. Chambers. Partial
Page 63 and 64:
References CT80 CT92 1971. G. Carpa
Page 65 and 66:
References K. Daniels and V. Milenk
Page 67 and 68:
References (FOCS), pages 60-69, 199
Page 69 and 70:
References FM71 M. Fischer and A. M
Page 71 and 72:
References History of Computing, 7:
Page 73 and 74:
References N. Gibbs, W. Poole, and
Page 75 and 76:
References Hof82 Hof89 Hol75 C. M.
Page 77 and 78:
References IK75 Ita78 O. Ibarra and
Page 79 and 80:
References Kir83 D. Kirkpatrick. Ef
Page 81 and 82:
References object in 2-dimensional
Page 83 and 84:
References LT79 LT80 8:99-118, 1995
Page 85 and 86:
References Mil97 V. Milenkovic. Dou
Page 87 and 88:
References Theoretical Computer Sci
Page 89 and 90:
References Pav82 T. Pavlidis. Algor
Page 91 and 92:
References Rei72 Rei91 Rei94 E. Rei
Page 93 and 94:
References Prentice Hall, Englewood
Page 95 and 96:
References SR95 SS71 SS90 SSS74 ST8
Page 97 and 98:
References Tin90 Mikkel Thorup. On
Page 99 and 100:
References Wel84 T. Welch. A techni
Page 101 and 102:
References Algorithms Mon Jun 2 23:
Page 103 and 104:
Correctness and Efficiency Next: Co
Page 105 and 106:
Correctness NearestNeighborTSP(P) P
Page 107 and 108:
Correctness Figure: A bad example f
Page 109 and 110:
Efficiency Next: Expressing Algorit
Page 111 and 112:
Keeping Score Next: The RAM Model o
Page 113 and 114:
The RAM Model of Computation substa
Page 115 and 116:
Best, Worst, and Average-Case Compl
Page 117 and 118:
The Big Oh Notation Figure: Illustr
Page 119 and 120:
Logarithms Next: Modeling the Probl
Page 121 and 122:
Logarithms justified in ignoring th
Page 123 and 124:
Modeling the Problem Figure: Modeli
Page 125 and 126:
About the War Stories Next: War Sto
Page 127 and 128:
War Story: Psychic Modeling Next: E
Page 129 and 130:
War Story: Psychic Modeling have pu
Page 131 and 132:
War Story: Psychic Modeling Next: E
Page 133 and 134:
Exercises (b) If I prove that an al
Page 135 and 136:
Fundamental Data Types Next: Contai
Page 137 and 138:
Containers Next: Dictionaries Up: F
Page 139 and 140:
Dictionaries Next: Binary Search Tr
Page 141 and 142:
Binary Search Trees BinaryTreeQuery
Page 143 and 144:
Priority Queues Next: Specialized D
Page 145 and 146:
Specialized Data Structures Next: S
Page 147 and 148:
Sorting Next: Applications of Sorti
Page 149 and 150:
Applications of Sorting Figure: Con
Page 151 and 152:
Data Structures Next: Incremental I
Page 153 and 154:
Incremental Insertion Next: Divide
Page 155 and 156:
Randomization Next: Bucketing Techn
Page 157 and 158:
Randomization Next: Bucketing Techn
Page 159 and 160:
Bucketing Techniques Algorithms Mon
Page 161 and 162:
War Story: Stripping Triangulations
Page 163 and 164:
War Story: Stripping Triangulations
Page 165 and 166:
War Story: Mystery of the Pyramids
Page 167 and 168:
War Story: Mystery of the Pyramids
Page 169 and 170:
War Story: String 'em Up We were co
Page 171 and 172:
War Story: String 'em Up Figure: Su
Page 173 and 174:
Exercises Next: Implementation Chal
Page 175 and 176:
Exercises used to select the pivot.
Page 177 and 178:
Dynamic Programming Next: Fibonacci
Page 179 and 180:
Fibonacci numbers Next: The Partiti
Page 181 and 182:
Fibonacci numbers Next: The Partiti
Page 183 and 184:
The Partition Problem . What is the
Page 185 and 186:
The Partition Problem Figure: Dynam
Page 187 and 188:
Approximate String Matching Next: L
Page 189 and 190:
Approximate String Matching The val
Page 191 and 192:
Longest Increasing Sequence Will th
Page 193 and 194:
Minimum Weight Triangulation Next:
Page 195 and 196:
Limitations of Dynamic Programming
Page 197 and 198:
War Story: Evolution of the Lobster
Page 199 and 200:
War Story: Evolution of the Lobster
Page 201 and 202:
War Story: What's Past is Prolog Ne
Page 203 and 204:
War Story: What's Past is Prolog th
Page 205 and 206:
War Story: Text Compression for Bar
Page 207 and 208:
War Story: Text Compression for Bar
Page 209 and 210:
Divide and Conquer Next: Fast Expon
Page 211 and 212:
Fast Exponentiation Next: Binary Se
Page 213 and 214:
Binary Search Next: Square and Othe
Page 215 and 216:
Page 217 and 218:
Exercises whose denominations are ,
Page 219 and 220:
The Friendship Graph Next: Data Str
Page 221 and 222:
The Friendship Graph friendship gra
Page 223 and 224:
Data Structures for Graphs linked t
Page 225 and 226:
War Story: Getting the Graph ``Well
Page 227 and 228:
Traversing a Graph Next: Breadth-Fi
Page 229 and 230:
Breadth-First Search Next: Depth-Fi
Page 231 and 232:
Depth-First Search Next: Applicatio
Page 233 and 234:
Depth-First Search Next: Applicatio
Page 235 and 236:
Connected Components Next: Tree and
Page 237 and 238:
Two-Coloring Graphs Next: Topologic
Page 239 and 240:
Topological Sorting Next: Articulat
Page 241 and 242:
Modeling Graph Problems Next: Minim
Page 243 and 244:
Modeling Graph Problems good line s
Page 245 and 246:
Minimum Spanning Trees ● Prim's A
Page 247 and 248:
Prim's Algorithm inserted edge (x,y
Page 249 and 250:
Kruskal's Algorithm a tree of weigh
Page 251 and 252:
Dijkstra's Algorithm Next: All-Pair
Page 253 and 254:
All-Pairs Shortest Path Next: War S
Page 255 and 256:
War Story: Nothing but Nets Next: W
Page 257 and 258:
War Story: Nothing but Nets ``You a
Page 259 and 260:
War Story: Dialing for Documents Ne
Page 261 and 262:
War Story: Dialing for Documents If
Page 263 and 264:
War Story: Dialing for Documents CO
Page 265 and 266:
Page 267 and 268:
Exercises Prove the statement or gi
Page 269 and 270:
Backtracking report it. kth element
Page 271 and 272:
Constructing All Subsets Next: Cons
Page 273 and 274:
Constructing All Paths in a Graph N
Page 275 and 276:
Search Pruning Next: Bandwidth Mini
Page 277 and 278:
Bandwidth Minimization immediately
Page 279 and 280:
War Story: Covering Chessboards Nex
Page 281 and 282:
War Story: Covering Chessboards att
Page 283 and 284:
Heuristic Methods Mon Jun 2 23:33:5
Page 285 and 286:
Simulated Annealing Return S then u
Page 287 and 288:
Traveling Salesman Problem Next: Ma
Page 289 and 290:
Independent Set Next: Circuit Board
Page 291 and 292:
Neural Networks Next: Genetic Algor
Page 293 and 294:
Genetic Algorithms Next: War Story:
Page 295 and 296:
War Story: Annealing Arrays Next: P
Page 297 and 298:
War Story: Annealing Arrays optimal
Page 299 and 300:
Parallel Algorithms Next: War Story
Page 301 and 302:
War Story: Going Nowhere Fast Next:
Page 303 and 304:
Page 305 and 306:
Problems and Reductions Next: Simpl
Page 307 and 308:
Simple Reductions Next: Hamiltonian
Page 309 and 310:
Hamiltonian Cycles Next: Independen
Page 311 and 312:
Independent Set and Vertex Cover pr
Page 313 and 314:
Clique and Independent Set These la
Page 315 and 316:
Satisfiability Mon Jun 2 23:33:50 E
Page 317 and 318:
The Theory of NP-Completeness Next:
Page 319 and 320:
3-Satisfiability where for , , , an
Page 321 and 322:
Integer Programming Next: Vertex Co
Page 323 and 324:
Integer Programming possible IP ins
Page 325 and 326:
Vertex Cover reduction for the 3-SA
Page 327 and 328:
Other NP-Complete Problems hard. Th
Page 329 and 330:
The Art of Proving Hardness easiest
Page 331 and 332:
War Story: Hard Against the Clock N
Page 333 and 334:
War Story: Hard Against the Clock I
Page 335 and 336:
Approximation Algorithms Next: Appr
Page 337 and 338:
Approximating Vertex Cover Next: Th
Page 339 and 340:
The Euclidean Traveling Salesman Ne
Page 341 and 342:
The Euclidean Traveling Salesman Ne
Page 343 and 344:
Exercises 1. Prove that the low deg
Page 345 and 346:
Data Structures Next: Dictionaries
Page 347 and 348:
Dictionaries Next: Priority Queues
Page 349 and 350:
Dictionaries use a function that ma
Page 351 and 352:
Dictionaries Implementation-oriente
Page 353 and 354:
Priority Queues ● Besides access
Page 355 and 356:
Priority Queues Fibonacci heaps [FT
Page 357 and 358:
Suffix Trees and Arrays Figure: A t
Page 359 and 360:
Suffix Trees and Arrays [GBY91]. Se
Page 361 and 362:
Graph Data Structures algorithms).
Page 363 and 364:
Graph Data Structures including the
Page 365 and 366:
Set Data Structures Next: Kd-Trees
Page 367 and 368:
Set Data Structures parent pointers
Page 369 and 370:
Kd-Trees Next: Numerical Problems U
Page 371 and 372:
Kd-Trees about p. Say we are lookin
Page 373 and 374:
Numerical Problems Next: Solving Li
Page 375 and 376:
Numerical Problems Mon Jun 2 23:33:
Page 377 and 378:
Solving Linear Equations algorithm
Page 379 and 380:
Solving Linear Equations Matrix inv
Page 381 and 382:
Bandwidth Reduction images near eac
Page 383 and 384:
Matrix Multiplication Next: Determi
Page 385 and 386:
Matrix Multiplication The linear al
Page 387 and 388:
Determinants and Permanents Next: C
Page 389 and 390:
Determinants and Permanents exposit
Page 391 and 392:
Constrained and Unconstrained Optim
Page 393 and 394:
Constrained and Unconstrained Optim
Page 395 and 396:
Linear Programming variable assignm
Page 397 and 398:
Linear Programming The book [MW93]
Page 399 and 400:
Random Number Generation Next: Fact
Page 401 and 402:
Random Number Generation is largely
Page 403 and 404:
Random Number Generation Related Pr
Page 405 and 406:
Factoring and Primality Testing The
Page 407 and 408:
Factoring and Primality Testing Alg
Page 409 and 410:
Arbitrary-Precision Arithmetic If y
Page 411 and 412:
Arbitrary-Precision Arithmetic PARI
Page 413 and 414: Knapsack Problem Next: Discrete Fou
Page 415 and 416: Knapsack Problem is a subset of S'
Page 417 and 418: Discrete Fourier Transform Next: Co
Page 419 and 420: Discrete Fourier Transform an algor
Page 421 and 422: Combinatorial Problems Next: Sortin
Page 423 and 424: Sorting Next: Searching Up: Combina
Page 425 and 426: Sorting The simplest approach to ex
Page 427 and 428: Sorting operations, implying an sor
Page 429 and 430: Searching large performance improve
Page 431 and 432: Searching Notes: Mehlhorn and Tsaka
Page 433 and 434: Median and Selection followed by fi
Page 435 and 436: Generating Permutations Next: Gener
Page 437 and 438: Generating Permutations The rank/un
Page 439 and 440: Generating Permutations generating
Page 441 and 442: Generating Subsets look right when
Page 443 and 444: Generating Subsets above for detail
Page 445 and 446: Generating Partitions Although the
Page 447 and 448: Generating Partitions Two related c
Page 449 and 450: Generating Graphs generate: ● Do
Page 451 and 452: Generating Graphs Combinatorica [Sk
Page 453 and 454: Calendrical Calculations Next: Job
Page 455 and 456: Calendrical Calculations Gregorian,
Page 457 and 458: Job Scheduling ● To assign a set
Page 459 and 460: Job Scheduling shop scheduling incl
Page 461 and 462: Satisfiability logic, and automatic
Page 463: Satisfiability Next: Graph Problems
Page 467 and 468: Connected Components Testing the co
Page 469 and 470: Connected Components discussing gra
Page 471 and 472: Topological Sorting contradiction t
Page 473 and 474: Minimum Spanning Tree Next: Shortes
Page 475 and 476: Minimum Spanning Tree help you sort
Page 477 and 478: Shortest Path Next: Transitive Clos
Page 479 and 480: Shortest Path easier to program tha
Page 481 and 482: Shortest Path Related Problems: Net
Page 483 and 484: Transitive Closure and Reduction
Page 485 and 486: Transitive Closure and Reduction Al
Page 487 and 488: Matching augmenting paths and stopp
Page 489 and 490: Matching Combinatorica [Ski90] prov
Page 491 and 492: Eulerian Cycle / Chinese Postman ar
Page 493 and 494: Eulerian Cycle / Chinese Postman Ne
Page 495 and 496: Edge and Vertex Connectivity Severa
Page 497 and 498: Edge and Vertex Connectivity Next:
Page 499 and 500: Network Flow programming model for
Page 501 and 502: Network Flow Combinatorica [Ski90]
Page 503 and 504: Drawing Graphs Nicely vertices are
Page 505 and 506: Drawing Graphs Nicely labs.com/orgs
Page 507 and 508: Drawing Trees such as the map of th
Page 509 and 510: Planarity Detection and Embedding N
Page 511 and 512: Planarity Detection and Embedding p
Page 513 and 514: Graph Problems: Hard Problems ● C
Page 515 and 516:
Clique approximate even to within a
Page 517 and 518:
Independent Set Next: Vertex Cover
Page 519 and 520:
Independent Set Related Problems: C
Page 521 and 522:
Vertex Cover Vertex cover and indep
Page 523 and 524:
Traveling Salesman Problem Next: Ha
Page 525 and 526:
Traveling Salesman Problem practice
Page 527 and 528:
Traveling Salesman Problem Size is
Page 529 and 530:
Hamiltonian Cycle Eulerian cycle, p
Page 531 and 532:
Graph Partition Next: Vertex Colori
Page 533 and 534:
Graph Partition annealing, is almos
Page 535 and 536:
Vertex Coloring Several special cas
Page 537 and 538:
Vertex Coloring Notes: An excellent
Page 539 and 540:
Edge Coloring The minimum number of
Page 541 and 542:
Graph Isomorphism Next: Steiner Tre
Page 543 and 544:
Graph Isomorphism vertices into equ
Page 545 and 546:
Steiner Tree Next: Feedback Edge/Ve
Page 547 and 548:
Steiner Tree The worst case for a m
Page 549 and 550:
Feedback Edge/Vertex Set Next: Comp
Page 551 and 552:
Feedback Edge/Vertex Set An interes
Page 553 and 554:
Computational Geometry complete bib
Page 555 and 556:
Robust Geometric Primitives Next: C
Page 557 and 558:
Robust Geometric Primitives ● Are
Page 559 and 560:
Robust Geometric Primitives Related
Page 561 and 562:
Convex Hull ● How many dimensions
Page 563 and 564:
Convex Hull http://www.cs.att.com/n
Page 565 and 566:
Triangulation Next: Voronoi Diagram
Page 567 and 568:
Triangulation GEOMPACK is a suite o
Page 569 and 570:
Voronoi Diagrams Next: Nearest Neig
Page 571 and 572:
Voronoi Diagrams McDonald's, the ti
Page 573 and 574:
Nearest Neighbor Search Next: Range
Page 575 and 576:
Nearest Neighbor Search Implementat
Page 577 and 578:
Range Search Next: Point Location U
Page 579 and 580:
Range Search subdivisions in C++. I
Page 581 and 582:
Point Location the n edges for inte
Page 583 and 584:
Point Location More recently, there
Page 585 and 586:
Intersection Detection ● Do you w
Page 587 and 588:
Intersection Detection Implementati
Page 589 and 590:
Bin Packing Next: Medial-Axis Trans
Page 591 and 592:
Bin Packing approach for general sh
Page 593 and 594:
Medial-Axis Transformation Next: Po
Page 595 and 596:
Medial-Axis Transformation Implemen
Page 597 and 598:
Polygon Partitioning number of piec
Page 599 and 600:
Simplifying Polygons Next: Shape Si
Page 601 and 602:
Simplifying Polygons vertices and o
Page 603 and 604:
Shape Similarity Next: Motion Plann
Page 605 and 606:
Shape Similarity or how close it is
Page 607 and 608:
Motion Planning There is a wide ran
Page 609 and 610:
Motion Planning often arise in the
Page 611 and 612:
Maintaining Line Arrangements Think
Page 613 and 614:
Maintaining Line Arrangements Next:
Page 615 and 616:
Minkowski Sum where x+y is the vect
Page 617 and 618:
Set and String Problems Next: Set C
Page 619 and 620:
Set Cover Next: Set Packing Up: Set
Page 621 and 622:
Set Cover Figure: Hitting set is du
Page 623 and 624:
Set Packing Next: String Matching U
Page 625 and 626:
Set Packing Notes: An excellent exp
Page 627 and 628:
String Matching shouldn't try. Furt
Page 629 and 630:
String Matching and texts, I recomm
Page 631 and 632:
Approximate String Matching This sa
Page 633 and 634:
Approximate String Matching http://
Page 635 and 636:
Text Compression Next: Cryptography
Page 637 and 638:
Text Compression code string. ASCII
Page 639 and 640:
Cryptography Next: Finite State Mac
Page 641 and 642:
Cryptography ● How can I validate
Page 643 and 644:
Cryptography MD5 [Riv92] is the sec
Page 645 and 646:
Finite State Machine Minimization F
Page 647 and 648:
Finite State Machine Minimization S
Page 649 and 650:
Longest Common Substring than edit
Page 651 and 652:
Longest Common Substring include [A
Page 653 and 654:
Shortest Common Superstring Finding
Page 655 and 656:
Software systems Next: LEDA Up: Alg
Page 657 and 658:
LEDA Next: Netlib Up: Software syst
Page 659 and 660:
Netlib Algorithms Mon Jun 2 23:33:5
Page 661 and 662:
The Stanford GraphBase Next: Combin
Page 663 and 664:
Algorithm Animations with XTango Ne
Page 665 and 666:
Programs from Books Next: Discrete
Page 667 and 668:
Handbook of Data Structures and Alg
Page 669 and 670:
Algorithms from P to NP Next: Compu
Page 671 and 672:
Algorithms in C++ Next: Data Source
Page 673 and 674:
Textbooks Next: On-Line Resources U
Page 675 and 676:
On-Line Resources Next: Literature
Page 677 and 678:
People Next: Software Up: On-Line R
Page 679 and 680:
Professional Consulting Services Ne
Page 681 and 682:
Index A Up: Index - All Index: A ab
Page 683 and 684:
Index A artists steal ASA ASCII asp
Page 685 and 686:
Index B binary representation - sub
Page 687 and 688:
Index C Up: Index - All Index: C C+
Page 689 and 690:
Index C clustering , , co-NP coding
Page 691 and 692:
Index C consulting services , conta
Page 693 and 694:
Index D Up: Index - All Index: D DA
Page 695 and 696:
Index D Dictionaries dictionaries -
Page 697 and 698:
Index D dynamic programming - appli
Page 699 and 700:
Index E empirical results - heurist
Page 701 and 702:
Index F Up: Index - All Index: F fa
Page 703 and 704:
Index F frequency domain friend-or-
Page 705 and 706:
Index G geometric shortest path , g
Page 707 and 708:
Index H Up: Index - All Index: H ha
Page 709 and 710:
Index H Algorithms Tue Jun 3 11:59:
Page 711 and 712:
Index I independent set - alternate
Page 713 and 714:
Index J Up: Index - All Index: J ji
Page 715 and 716:
Index K Algorithms Tue Jun 3 11:59:
Page 717 and 718:
Index L linear congruential generat
Page 719 and 720:
Index M Up: Index - All Index: M ma
Page 721 and 722:
Index M mindset minima minimax sear
Page 723 and 724:
Index N Up: Index - All Index: N na
Page 725 and 726:
Index N numerical analysis numerica
Page 727 and 728:
Index O overlap graph overpasses -
Page 729 and 730:
Index P , , , , , , , , , , passwor
Page 731 and 732:
Index P polygons polygon triangulat
Page 733 and 734:
Index Q Up: Index - All Index: Q Qh
Page 735 and 736:
Index R ranking combinatorial objec
Page 737 and 738:
Index S Up: Index - All Index: S s-
Page 739 and 740:
Index S Shape Similarity shape simp
Page 741 and 742:
Index S solar year Solving Linear E
Page 743 and 744:
Index S straight-line graph drawing
Page 745 and 746:
Index T Up: Index - All Index: T ta
Page 747 and 748:
Index T trees - matching trees - pa
Page 749 and 750:
Index V Up: Index - All Index: V va
Page 751 and 752:
Index W Up: Index - All Index: W wa
Page 753 and 754:
Index Y Up: Index - All Index: Y Yo
Page 755 and 756:
Index (complete) Up: Index - All In
Page 757 and 758:
Index (complete) arm, robot around
Page 759 and 760:
Index (complete) binary search tree
Page 761 and 762:
Index (complete) center vertex, , C
Page 763 and 764:
Index (complete) compaction compari
Page 765 and 766:
Index (complete) counting paths, co
Page 767 and 768:
Index (complete) deletion from bina
Page 769 and 770:
Index (complete) dominance ordering
Page 771 and 772:
Index (complete) English to French
Page 773 and 774:
Index (complete) file directory tre
Page 775 and 776:
Index (complete) geom.bib, geometri
Page 777 and 778:
Index (complete) Hamiltonian Cycle
Page 779 and 780:
Index (complete) implementation cha
Page 781 and 782:
Index (complete) job-shop schedulin
Page 783 and 784:
Index (complete) linear programming
Page 785 and 786:
Index (complete) , , , , , , , math
Page 787 and 788:
Index (complete) modular arithmetic
Page 789 and 790:
Index (complete) normal distributio
Page 791 and 792:
Index (complete) parallel algorithm
Page 793 and 794:
Index (complete) planar subdivision
Page 795 and 796:
Index (complete) proof of correctne
Page 797 and 798:
Index (complete) regular expression
Page 799 and 800:
Index (complete) self-organizing tr
Page 801 and 802:
Index (complete) sine functions sin
Page 803 and 804:
Index (complete) spring embedding h
Page 805 and 806:
Index (complete) Symbol Technologie
Page 807 and 808:
Index (complete) trial division Tri
Page 809 and 810:
Index (complete) VLSI circuit layou
Page 811 and 812:
1.4.4 Shortest Path 1.4.4 Shortest
Page 813 and 814:
1.2.5 Constrained and Unconstrained
Page 815 and 816:
1.6.4 Voronoi Diagrams 1.6.4 Vorono
Page 817 and 818:
1.4.7 Eulerian Cycle / Chinese Post
Page 819 and 820:
Online Bibliographies Online Biblio
Page 821 and 822:
About the Book -- The Algorithm Des
Page 823 and 824:
Copyright and Disclaimers Copyright
Page 825 and 826:
CD-ROM Installation Installation an
Page 827 and 828:
CD-ROM Installation install a sound
Page 829 and 830:
Thanks! Frank Ruscica file:///E|/WE
Page 831 and 832:
Thanks! Zhong Li Thanks also to Fil
Page 833 and 834:
CD-ROM Installation To use the CD-R
Page 835 and 836:
Binary Search in Action Binary Sear
Page 837 and 838:
Binary Search in Action file:///E|/
Page 839 and 840:
Binary Search in Action file:///E|/
Page 841 and 842:
Postscript version of the lecture n
Page 843 and 844:
Lecture 1 - analyzing algorithms Ne
Page 845 and 846:
Lecture 1 - analyzing algorithms Yo
Page 847 and 848:
Lecture 1 - analyzing algorithms Th
Page 849 and 850:
Lecture 1 - analyzing algorithms Th
Page 851 and 852:
Lecture 1 - analyzing algorithms is
Page 853 and 854:
Lecture 2 - asymptotic notation How
Page 855 and 856:
Lecture 2 - asymptotic notation Not
Page 857 and 858:
Lecture 2 - asymptotic notation alg
Page 859 and 860:
Lecture 2 - asymptotic notation Sup
Page 861 and 862:
Lecture 2 - asymptotic notation Nex
Page 863 and 864:
Lecture 3 - recurrence relations Is
Page 865 and 866:
Lecture 3 - recurrence relations
Page 867 and 868:
Lecture 3 - recurrence relations Li
Page 869 and 870:
Lecture 3 - recurrence relations Su
Page 871 and 872:
Lecture 3 - recurrence relations A
Page 873 and 874:
Lecture 4 - heapsort Note iteration
Page 875 and 876:
Lecture 4 - heapsort The convex hul
Page 877 and 878:
Lecture 4 - heapsort 1. All leaves
Page 879 and 880:
Lecture 4 - heapsort left = 2i righ
Page 881 and 882:
Lecture 4 - heapsort Since this sum
Page 883 and 884:
Lecture 4 - heapsort Selection sort
Page 885 and 886:
Lecture 4 - heapsort Greedy Algorit
Page 887 and 888:
Lecture 5 - quicksort Partitioning
Page 889 and 890:
Lecture 5 - quicksort Listen To Par
Page 891 and 892:
Lecture 5 - quicksort Listen To Par
Page 893 and 894:
Lecture 5 - quicksort The worst cas
Page 895 and 896:
Lecture 5 - quicksort Mon Jun 2 09:
Page 897 and 898:
Lecture 6 - linear sorting Since 2i
Page 899 and 900:
Lecture 6 - linear sorting With uni
Page 901 and 902:
Lecture 6 - linear sorting Listen T
Page 903 and 904:
Lecture 7 - elementary data structu
Page 905 and 906:
Page 907 and 908:
Page 909 and 910:
Page 911 and 912:
Page 913 and 914:
Page 915 and 916:
Page 917 and 918:
Lecture 8 - binary trees - Joyce Ki
Page 919 and 920:
Lecture 8 - binary trees TREE-MINIM
Page 921 and 922:
Lecture 8 - binary trees Inorder-Tr
Page 923 and 924:
Lecture 8 - binary trees Listen To
Page 925 and 926:
Lecture 8 - binary trees insert(b)
Page 927 and 928:
Lecture 8 - binary trees Not (1) -
Page 929 and 930:
Lecture 9 - catch up Next: Lecture
Page 931 and 932:
Lecture 10 - tree restructuring 1.
Page 933 and 934:
Lecture 10 - tree restructuring Lis
Page 935 and 936:
Lecture 10 - tree restructuring Not
Page 937 and 938:
Lecture 10 - tree restructuring Cas
Page 939 and 940:
Lecture 11 - backtracking Next: Lec
Page 941 and 942:
Lecture 11 - backtracking Only 63 s
Page 943 and 944:
Lecture 11 - backtracking Recursion
Page 945 and 946:
Lecture 11 - backtracking Specifica
Page 947 and 948:
Lecture 12 - introduction to dynami
Page 949 and 950:
Page 951 and 952:
Page 953 and 954:
Page 955 and 956:
Page 957 and 958:
Lecture 13 - dynamic programming ap
Page 959 and 960:
Page 961 and 962:
Page 963 and 964:
Lecture 14 - data structures for gr
Page 965 and 966:
Page 967 and 968:
Page 969 and 970:
Page 971 and 972:
Page 973 and 974:
Page 975 and 976:
Lecture 15 - DFS and BFS Next: Lect
Page 977 and 978:
Lecture 15 - DFS and BFS In a DFS o
Page 979 and 980:
Lecture 15 - DFS and BFS It could u
Page 981 and 982:
Lecture 15 - DFS and BFS Algorithm
Page 983 and 984:
Lecture 16 - applications of DFS an
Page 985 and 986:
Page 987 and 988:
Page 989 and 990:
Lecture 17 - minimum spanning trees
Page 991 and 992:
Page 993 and 994:
Page 995 and 996:
Page 997 and 998:
Page 999 and 1000:
Lecture 18 - shortest path algorthm
Page 1001 and 1002:
Page 1003 and 1004:
Page 1005 and 1006:
Page 1007 and 1008:
Page 1009 and 1010:
Lecture 19 - satisfiability Next: L
Page 1011 and 1012:
Lecture 19 - satisfiability Why? Th
Page 1013 and 1014:
Lecture 19 - satisfiability between
Page 1015 and 1016:
Lecture 19 - satisfiability Note th
Page 1017 and 1018:
Lecture 20 - integer programming Ex
Page 1019 and 1020:
Lecture 20 - integer programming Ne
Page 1021 and 1022:
Lecture 21 - vertex cover Proof: VC
Page 1023 and 1024:
Lecture 21 - vertex cover Question:
Page 1025 and 1026:
Lecture 21 - vertex cover seen to d
Page 1027 and 1028:
Lecture 22 - techniques for proving
Page 1029 and 1030:
Page 1031 and 1032:
Page 1033 and 1034:
Page 1035 and 1036:
Lecture 23 - approximation algorith
Page 1037 and 1038:
Page 1039 and 1040:
Page 1041 and 1042:
Page 1043 and 1044:
Page 1045 and 1046:
About this document ... Up: No Titl
Page 1047 and 1048:
1.1.6 Kd-Trees ● Point Location
Page 1049 and 1050:
1.1.3 Suffix Trees and Arrays Send
Page 1051 and 1052:
1.5.1 Clique ● Vertex Cover Go to
Page 1053 and 1054:
1.6.2 Convex Hull Related Problems
Page 1055 and 1056:
Visual Links Index file:///E|/WEBSI
Page 1057 and 1058:
Page 1059 and 1060:
Page 1061 and 1062:
Page 1063 and 1064:
Page 1065 and 1066:
Page 1067 and 1068:
Page 1069 and 1070:
Page 1071 and 1072:
Page 1073 and 1074:
Page 1075 and 1076:
Page 1077 and 1078:
Page 1079 and 1080:
Page 1081 and 1082:
Page 1083 and 1084:
About the Ratings About the Ratings
Page 1085 and 1086:
1.2 Numerical Problems 1.2 Numerica
Page 1087 and 1088:
1.4 Graph Problems -- polynomial-ti
Page 1089 and 1090:
1.6 Computational Geometry 1.6 Comp
Page 1091 and 1092:
C++ Language Implementations Algori
Page 1093 and 1094:
C Language Implementations ● POSI
Page 1095 and 1096:
FORTRAN Language Implementations Al
Page 1097 and 1098:
Lisp Language Implementations Algor
Page 1099 and 1100:
Algorithm Repository -- Citations C
Page 1101 and 1102:
Practical Algorithm Design -- User
Page 1103 and 1104:
LEDA - A Library of Efficient Data
Page 1105 and 1106:
Discrete Optimization Methods Discr
Page 1107 and 1108:
Netlib / TOMS -- Collected Algorith
Page 1109 and 1110:
Netlib / TOMS -- Collected Algorith
Page 1111 and 1112:
Xtango and Polka Algorithm Animatio
Page 1113 and 1114:
Combinatorica Combinatorica Combina
Page 1115 and 1116:
file:///E|/WEBSITE/IMPLEMEN/GRAPHBA
Page 1117 and 1118:
1.4.1 Connected Components 1.4.1 Co
Page 1119 and 1120:
1.5.9 Graph Isomorphism 1.5.9 Graph
Page 1121 and 1122:
1.2.3 Matrix Multiplication 1.2.3 M
Page 1123 and 1124:
1.6.14 Motion Planning 1.6.14 Motio
Page 1125 and 1126:
1.4.9 Network Flow 1.4.9 Network Fl
Page 1127 and 1128:
1.1.2 Priority Queues 1.1.2 Priorit
Page 1129 and 1130:
1.5.10 Steiner Tree 1.5.10 Steiner
Page 1131 and 1132:
1.4.5 Transitive Closure and Reduct
Page 1133 and 1134:
Page 1135 and 1136:
Page 1137 and 1138:
Genocop -- Optimization via Genetic
Page 1139 and 1140:
1.2.6 Linear Programming ● Knapsa
Page 1141 and 1142:
1.2.7 Random Number Generation ●
Page 1143 and 1144:
1.3.10 Satisfiability ● Constrain
Page 1145 and 1146:
Qhull - higher dimensional convex h
Page 1147 and 1148:
1.6.5 Nearest Neighbor Search 1.6.5
Page 1149 and 1150:
1.6.7 Point Location 1.6.7 Point Lo
Page 1151 and 1152:
1.6.10 Medial-Axis Transformation 1
Page 1153 and 1154:
1.6.3 Triangulation 1.6.3 Triangula
Page 1155 and 1156:
Nijenhuis and Wilf: Combinatorial A
Page 1157 and 1158:
1.5.5 Hamiltonian Cycle 1.5.5 Hamil
Page 1159 and 1160:
1.4.6 Matching 1.4.6 Matching INPUT
Page 1161 and 1162:
A compendium of NP optimization pro
Page 1163 and 1164:
1.6.9 Bin Packing 1.6.9 Bin Packing
Page 1165 and 1166:
1.6.12 Simplifying Polygons 1.6.12
Page 1167 and 1168:
1.6.13 Shape Similarity 1.6.13 Shap
Page 1169 and 1170:
1.6.11 Polygon Partitioning 1.6.11
Page 1171 and 1172:
1.4.3 Minimum Spanning Tree 1.4.3 M
Page 1173 and 1174:
1.6.15 Maintaining Line Arrangement
Page 1175 and 1176:
1.3.7 Generating Graphs 1.3.7 Gener
Page 1177 and 1178:
1.2.11 Discrete Fourier Transform 1
Page 1179 and 1180:
1.5.8 Edge Coloring 1.5.8 Edge Colo
Page 1181 and 1182:
1.3.3 Median and Selection 1.3.3 Me
Page 1183 and 1184:
1.3.1 Sorting 1.3.1 Sorting INPUT O
Page 1185 and 1186:
Plugins for use with the CDROM Plug
Page 1187 and 1188:
Implementation Challenges Next: Dat
Page 1189 and 1190:
Implementation Challenges Next: Gra
Page 1191 and 1192:
Implementation Challenges Next: Int
Page 1193 and 1194:
Caveats Next: Data Structures Up: A
Page 1195 and 1196:
1.1.1 Dictionaries ● Priority Que
Page 1197 and 1198:
1.1.4 Graph Data Structures ● Gra
Page 1199 and 1200:
1.1.5 Set Data Structures ● Gener
Page 1201 and 1202:
1.2.1 Solving Linear Equations ●
Page 1203 and 1204:
1.2.2 Bandwidth Reduction ● Topol
Page 1205 and 1206:
1.2.4 Determinants and Permanents
Page 1207 and 1208:
1.2.8 Factoring and Primality Testi
Page 1209 and 1210:
1.2.9 Arbitrary Precision Arithmeti
Page 1211 and 1212:
1.2.10 Knapsack Problem ● Linear
Page 1213 and 1214:
1.3.2 Searching ● Sorting Go to t
Page 1215 and 1216:
1.3.4 Generating Permutations ● C
Page 1217 and 1218:
1.3.5 Generating Subsets ● Genera
Page 1219 and 1220:
1.3.6 Generating Partitions ● Gen
Page 1221 and 1222:
1.3.8 Calendrical Calculations Go t
Page 1223 and 1224:
1.3.9 Job Scheduling ● Feedback E
Page 1225 and 1226:
1.4.2 Topological Sorting Related P
Page 1227 and 1228:
1.4.8 Edge and Vertex Connectivity
Page 1229 and 1230:
1.4.10 Drawing Graphs Nicely ● Pl
Page 1231 and 1232:
1.4.11 Drawing Trees ● Planarity
Page 1233 and 1234:
1.4.12 Planarity Detection and Embe
Page 1235 and 1236:
1.5.2 Independent Set ● Vertex Co
Page 1237 and 1238:
1.5.3 Vertex Cover ● Independent
Page 1239 and 1240:
1.5.4 Traveling Salesman Problem
Page 1241 and 1242:
1.5.6 Graph Partition ● Network F
Page 1243 and 1244:
1.5.7 Vertex Coloring Related Probl
Page 1245 and 1246:
1.5.11 Feedback Edge/Vertex Set Go
Page 1247 and 1248:
1.6.1 Robust Geometric Primitives G
Page 1249 and 1250:
1.6.6 Range Search ● Kd-Trees ●
Page 1251 and 1252:
1.6.8 Intersection Detection ● Ro
Page 1253 and 1254:
1.6.16 Minkowski Sum Go to the corr
Page 1255 and 1256:
1.7.1 Set Cover ● Set Packing ●
Page 1257 and 1258:
1.7.2 Set Packing Go to the corresp
Page 1259 and 1260:
1.7.3 String Matching ● Approxima
Page 1261 and 1262:
1.7.4 Approximate String Matching
Page 1263 and 1264:
1.7.5 Text Compression Go to the co
Page 1265 and 1266:
1.7.6 Cryptography ● Text Compres
Page 1267 and 1268:
1.7.7 Finite State Machine Minimiza
Page 1269 and 1270:
1.7.8 Longest Common Substring ●
Page 1271 and 1272:
1.7.9 Shortest Common Superstring G
Page 1273 and 1274:
Handbook of Algorithms and Data Str
Page 1275 and 1276:
Moret and Shapiro's Algorithms P to
Page 1277 and 1278:
Index B Up: Index - All Index: B ba
Page 1279 and 1280:
Index C Up: Index - All Index: C ch
Page 1281 and 1282:
Index E Up: Index - All Index: E ed
Page 1283 and 1284:
Index G Up: Index - All Index: G ga
Page 1285 and 1286:
Index I Up: Index - All Index: I in
Page 1287 and 1288:
Index L Up: Index - All Index: L li
Page 1289 and 1290:
Index N Up: Index - All Index: N ne
Page 1291 and 1292:
Index P Up: Index - All Index: P pa
Page 1293 and 1294:
Index R Up: Index - All Index: R RA
Page 1295 and 1296:
Index S successor supercomputer , s
Page 1297 and 1298:
Index U Up: Index - All Index: U un
Page 1299 and 1300:
Index W Up: Index - All Index: W wo
Page 1301 and 1302:
Index (complete) bin packing bipart
Page 1303 and 1304:
Index (complete) Fourier transform
Page 1305 and 1306:
Index (complete) parenthesization p
Page 1307 and 1308:
Index (complete) symmetry, exploiti
Page 1309 and 1310:
Ranger - Nearest Neighbor Search in
Page 1311 and 1312:
DIMACS Implementation Challenges Pe
Page 1313 and 1314:
Stony Brook Project Implementations
Page 1315 and 1316:
Clarkson's higher dimensional conve
Page 1317 and 1318:
file:///E|/WEBSITE/BIBLIO/TESTDATA/
Page 1319 and 1320:
Page 1321 and 1322:
Page 1323 and 1324:
Page 1325 and 1326:
Page 1327 and 1328:
Page 1329 and 1330:
Page 1331 and 1332:
Page 1333 and 1334:
Page 1335 and 1336:
Page 1337 and 1338:
Page 1339 and 1340:
Page 1341 and 1342:
Page 1343 and 1344:
Page 1345 and 1346:
Page 1347 and 1348:
Page 1349 and 1350:
Page 1351 and 1352:
Page 1353 and 1354:
Page 1355 and 1356:
Page 1357 and 1358:
Page 1359 and 1360:
Page 1361 and 1362:
Page 1363 and 1364:
Page 1365 and 1366:
Page 1367 and 1368:
Page 1369 and 1370:
Page 1371 and 1372:
Page 1373 and 1374:
Page 1375 and 1376:
Page 1377 and 1378:
Page 1379 and 1380:
Page 1381 and 1382:
Page 1383 and 1384:
Page 1385 and 1386:
Page 1387 and 1388:
Page 1389 and 1390:
Page 1391 and 1392:
Page 1393 and 1394:
Page 1395 and 1396:
Page 1397 and 1398:
Page 1399 and 1400:
Page 1401 and 1402:
Page 1403 and 1404:
Page 1405 and 1406:
Page 1407 and 1408:
Page 1409 and 1410:
Page 1411 and 1412:
Page 1413 and 1414:
Page 1415 and 1416:
Page 1417 and 1418:
Page 1419 and 1420:
Page 1421 and 1422:
Page 1423 and 1424:
Page 1425 and 1426:
Page 1427 and 1428:
Page 1429 and 1430:
Page 1431 and 1432:
Page 1433 and 1434:
Page 1435 and 1436:
Page 1437 and 1438:
Page 1439 and 1440:
Page 1441 and 1442:
Page 1443 and 1444:
Page 1445 and 1446:
Page 1447 and 1448:
SimPack/Sim++ Simulation Toolkit Si
Page 1449 and 1450:
Fire-Engine and Spare-Parts String
Page 1451 and 1452:
Geolab -- Computational Geometry Sy
Page 1453 and 1454:
Calendrical Calculations Calendrica
Page 1455 and 1456:
David Eppstein's Knuth-Morris-Pratt
Page 1457 and 1458:
Mike Trick's Graph Coloring Resourc
Page 1459 and 1460:
Frank Ruskey's Combinatorial Genera
Page 1461 and 1462:
Arrange - maintainance of arrangeme
Page 1463 and 1464:
LP_SOLVE: Linear Programming Code L
Page 1465 and 1466:
PARI - Package for Number Theory Se
Page 1467 and 1468:
TSP solvers TSP solvers tsp-solve i
Page 1469 and 1470:
PHYLIP -- inferring phylogenic tree
Page 1471 and 1472:
Skeletonization Software (2-D) Skel
Page 1473 and 1474:
agrep - Approximate General Regular
Page 1475 and 1476:
CAP -- Contig Assembly Program CAP
Page 1477 and 1478:
NAUTY -- Graph Isomorphism NAUTY --
Page 1479 and 1480:
BIPM -- Bipartite Matching Codes BI
Page 1481 and 1482:
LAPACK and LINPACK -- Linear Algebr
Page 1483 and 1484:
User Comments User Comments Archive
Page 1485 and 1486:
The Algorithm Design Manual Most pr
Page 1487 and 1488:
The Algorithm Design Manual 5.6 War
show all

The.Algorithm.Design.Manual.Springer-Verlag.1998

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?