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The.Algorithm.Design.Manual.Springer-Verlag.1998
The.Algorithm.Design.Manual.Springer-Verlag.1998
The.Algorithm.Design.Manual.Springer-Verlag.1998
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Caveat<br />
Next: Contents Up: <strong>The</strong> <strong>Algorithm</strong> <strong>Design</strong> <strong>Manual</strong> Previous: Acknowledgments<br />
Caveat<br />
It is traditional for the author to magnanimously accept the blame for whatever deficiencies remain. I<br />
don't. Any errors, deficiencies, or problems in this book are somebody else's fault, but I would appreciate<br />
knowing about them so as to determine who is to blame.<br />
Steven S. Skiena Department of Computer Science State University of New York Stony Brook, NY<br />
11794-4400 http://www.cs.sunysb.edu/ skiena May 1997<br />
<strong>Algorithm</strong>s<br />
Mon Jun 2 23:33:50 EDT 1997<br />
file:///E|/BOOK/BOOK/NODE3.HTM [19/1/2003 1:27:33]
Caveat Next: Contents Up: <strong>The</strong> <strong>Algorithm</strong> <strong>Design</strong> <strong>Manual</strong> Previous: Acknowledgments Caveat It is traditional for the author to magnanimously accept the blame for whatever deficiencies remain. I don't. Any errors, deficiencies, or problems in this book are somebody else's fault, but I would appreciate knowing about them so as to determine who is to blame. Steven S. Skiena Department of Computer Science State University of New York Stony Brook, NY 11794-4400 http://www.cs.sunysb.edu/ skiena May 1997 <strong>Algorithm</strong>s Mon Jun 2 23:33:50 EDT 1997 file:///E|/BOOK/BOOK/NODE3.HTM [19/1/2003 1:27:33]
Contents Next: Techniques Up: <strong>The</strong> <strong>Algorithm</strong> <strong>Design</strong> <strong>Manual</strong> Previous: Caveat Contents ● Techniques ❍ Introduction to <strong>Algorithm</strong>s ■ Correctness and Efficiency ■ Correctness ■ Efficiency ■ Expressing <strong>Algorithm</strong>s ■ Keeping Score ■ <strong>The</strong> RAM Model of Computation ■ Best, Worst, and Average-Case Complexity ■ <strong>The</strong> Big Oh Notation ■ Growth Rates ■ Logarithms ■ Modeling the Problem ■ About the War Stories ■ War Story: Psychic Modeling ■ Exercises ❍ Data Structures and Sorting ■ Fundamental Data Types ■ Containers ■ Dictionaries ■ Binary Search Trees ■ Priority Queues ■ Specialized Data Structures ■ Sorting ■ Applications of Sorting ■ Approaches to Sorting ■ Data Structures ■ Incremental Insertion ■ Divide and Conquer ■ Randomization ■ Bucketing Techniques ■ War Story: Stripping Triangulations file:///E|/BOOK/BOOK/NODE4.HTM (1 of 7) [19/1/2003 1:27:35]
- 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: Acknowledgments Mon Jun 2 23:33:50
- 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
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References J. M. Chambers. Partial
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References CT80 CT92 1971. G. Carpa
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References K. Daniels and V. Milenk
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References (FOCS), pages 60-69, 199
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References FM71 M. Fischer and A. M
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References History of Computing, 7:
- Page 73 and 74:
References N. Gibbs, W. Poole, and
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References Hof82 Hof89 Hol75 C. M.
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References IK75 Ita78 O. Ibarra and
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References Kir83 D. Kirkpatrick. Ef
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References object in 2-dimensional
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References LT79 LT80 8:99-118, 1995
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References Mil97 V. Milenkovic. Dou
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References Theoretical Computer Sci
- Page 89 and 90:
References Pav82 T. Pavlidis. Algor
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References Rei72 Rei91 Rei94 E. Rei
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References Prentice Hall, Englewood
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References SR95 SS71 SS90 SSS74 ST8
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References Tin90 Mikkel Thorup. On
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References Wel84 T. Welch. A techni
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References Algorithms Mon Jun 2 23:
- Page 103 and 104:
Correctness and Efficiency Next: Co
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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
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War Story: Psychic Modeling Next: E
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Exercises (b) If I prove that an al
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Fundamental Data Types Next: Contai
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Containers Next: Dictionaries Up: F
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Dictionaries Next: Binary Search Tr
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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
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Applications of Sorting Figure: Con
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Data Structures Next: Incremental I
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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
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War Story: Stripping Triangulations
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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
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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:
Exercises Next: Implementation Chal
- 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:
Exercises Next: Implementation Chal
- 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:
Exercises Next: Implementation Chal
- 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 and 464:
Satisfiability Next: Graph Problems
- Page 465 and 466:
Graph Problems: Polynomial-Time rec
- 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
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Longest Common Substring than edit
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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
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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
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On-Line Resources Next: Literature
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People Next: Software Up: On-Line R
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Professional Consulting Services Ne
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Index A Up: Index - All Index: A ab
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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
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Index M Up: Index - All Index: M ma
- Page 721 and 722:
Index M mindset minima minimax sear
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Index N Up: Index - All Index: N na
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Index N numerical analysis numerica
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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
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Index R ranking combinatorial objec
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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
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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
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Index W Up: Index - All Index: W wa
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Index Y Up: Index - All Index: Y Yo
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Index (complete) Up: Index - All In
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Index (complete) arm, robot around
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Index (complete) binary search tree
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Index (complete) center vertex, , C
- Page 763 and 764:
Index (complete) compaction compari
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Index (complete) counting paths, co
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Index (complete) deletion from bina
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Index (complete) dominance ordering
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Index (complete) English to French
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Index (complete) file directory tre
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Index (complete) geom.bib, geometri
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Index (complete) Hamiltonian Cycle
- Page 779 and 780:
Index (complete) implementation cha
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Index (complete) job-shop schedulin
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Index (complete) linear programming
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Index (complete) , , , , , , , math
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Index (complete) modular arithmetic
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Index (complete) normal distributio
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Index (complete) parallel algorithm
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Index (complete) planar subdivision
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Index (complete) proof of correctne
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Index (complete) regular expression
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Index (complete) self-organizing tr
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Index (complete) sine functions sin
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Index (complete) spring embedding h
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Index (complete) Symbol Technologie
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Index (complete) trial division Tri
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Index (complete) VLSI circuit layou
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1.4.4 Shortest Path 1.4.4 Shortest
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1.2.5 Constrained and Unconstrained
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1.6.4 Voronoi Diagrams 1.6.4 Vorono
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1.4.7 Eulerian Cycle / Chinese Post
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Online Bibliographies Online Biblio
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About the Book -- The Algorithm Des
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Copyright and Disclaimers Copyright
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CD-ROM Installation Installation an
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CD-ROM Installation install a sound
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Thanks! Frank Ruscica file:///E|/WE
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Thanks! Zhong Li Thanks also to Fil
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CD-ROM Installation To use the CD-R
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Binary Search in Action Binary Sear
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Binary Search in Action file:///E|/
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Binary Search in Action file:///E|/
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Postscript version of the lecture n
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Lecture 1 - analyzing algorithms Ne
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Lecture 1 - analyzing algorithms Yo
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Lecture 1 - analyzing algorithms Th
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Lecture 1 - analyzing algorithms Th
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Lecture 1 - analyzing algorithms is
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Lecture 2 - asymptotic notation How
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Lecture 2 - asymptotic notation Not
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Lecture 2 - asymptotic notation alg
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Lecture 2 - asymptotic notation Sup
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Lecture 2 - asymptotic notation Nex
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Lecture 3 - recurrence relations Is
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Lecture 3 - recurrence relations
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Lecture 3 - recurrence relations Li
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Lecture 3 - recurrence relations Su
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Lecture 3 - recurrence relations A
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Lecture 4 - heapsort Note iteration
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Lecture 4 - heapsort The convex hul
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Lecture 4 - heapsort 1. All leaves
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Lecture 4 - heapsort left = 2i righ
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Lecture 4 - heapsort Since this sum
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Lecture 4 - heapsort Selection sort
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Lecture 4 - heapsort Greedy Algorit
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Lecture 5 - quicksort Partitioning
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Lecture 5 - quicksort Listen To Par
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Lecture 5 - quicksort Listen To Par
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Lecture 5 - quicksort The worst cas
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Lecture 5 - quicksort Mon Jun 2 09:
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Lecture 6 - linear sorting Since 2i
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Lecture 6 - linear sorting With uni
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Lecture 6 - linear sorting Listen T
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Lecture 7 - elementary data structu
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Lecture 7 - elementary data structu
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Lecture 7 - elementary data structu
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Lecture 7 - elementary data structu
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Lecture 7 - elementary data structu
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Lecture 7 - elementary data structu
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Lecture 8 - binary trees - Joyce Ki
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Lecture 8 - binary trees TREE-MINIM
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Lecture 8 - binary trees Inorder-Tr
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Lecture 8 - binary trees Listen To
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Lecture 8 - binary trees insert(b)
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Lecture 8 - binary trees Not (1) -
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Lecture 9 - catch up Next: Lecture
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Lecture 10 - tree restructuring 1.
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Lecture 10 - tree restructuring Lis
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Lecture 10 - tree restructuring Not
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Lecture 10 - tree restructuring Cas
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Lecture 11 - backtracking Next: Lec
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Lecture 11 - backtracking Only 63 s
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Lecture 11 - backtracking Recursion
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Lecture 11 - backtracking Specifica
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Lecture 12 - introduction to dynami
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Lecture 12 - introduction to dynami
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Lecture 12 - introduction to dynami
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Lecture 12 - introduction to dynami
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Lecture 12 - introduction to dynami
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Lecture 13 - dynamic programming ap
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Lecture 13 - dynamic programming ap
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Lecture 13 - dynamic programming ap
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Lecture 14 - data structures for gr
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Lecture 14 - data structures for gr
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Lecture 14 - data structures for gr
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Lecture 14 - data structures for gr
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Lecture 14 - data structures for gr
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Lecture 14 - data structures for gr
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Lecture 15 - DFS and BFS Next: Lect
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Lecture 15 - DFS and BFS In a DFS o
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Lecture 15 - DFS and BFS It could u
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Lecture 15 - DFS and BFS Algorithm
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Lecture 16 - applications of DFS an
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Lecture 16 - applications of DFS an
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Lecture 16 - applications of DFS an
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Lecture 17 - minimum spanning trees
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Lecture 17 - minimum spanning trees
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Lecture 17 - minimum spanning trees
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Lecture 17 - minimum spanning trees
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Lecture 17 - minimum spanning trees
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Lecture 18 - shortest path algorthm
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Lecture 18 - shortest path algorthm
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Lecture 18 - shortest path algorthm
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Lecture 18 - shortest path algorthm
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Lecture 19 - satisfiability Next: L
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Lecture 19 - satisfiability Why? Th
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Lecture 19 - satisfiability between
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Lecture 19 - satisfiability Note th
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Lecture 20 - integer programming Ex
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Lecture 20 - integer programming Ne
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Lecture 21 - vertex cover Proof: VC
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Lecture 21 - vertex cover Question:
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Lecture 21 - vertex cover seen to d
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Lecture 22 - techniques for proving
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Lecture 22 - techniques for proving
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Lecture 22 - techniques for proving
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Lecture 22 - techniques for proving
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Lecture 23 - approximation algorith
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Lecture 23 - approximation algorith
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Lecture 23 - approximation algorith
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Lecture 23 - approximation algorith
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Lecture 23 - approximation algorith
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About this document ... Up: No Titl
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1.1.6 Kd-Trees ● Point Location
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1.1.3 Suffix Trees and Arrays Send
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1.5.1 Clique ● Vertex Cover Go to
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1.6.2 Convex Hull Related Problems
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Visual Links Index file:///E|/WEBSI
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Visual Links Index file:///E|/WEBSI
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Visual Links Index file:///E|/WEBSI
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Visual Links Index file:///E|/WEBSI
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Visual Links Index file:///E|/WEBSI
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Visual Links Index file:///E|/WEBSI
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Visual Links Index file:///E|/WEBSI
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Visual Links Index file:///E|/WEBSI
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Visual Links Index file:///E|/WEBSI
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Visual Links Index file:///E|/WEBSI
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Visual Links Index file:///E|/WEBSI
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About the Ratings About the Ratings
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1.2 Numerical Problems 1.2 Numerica
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1.4 Graph Problems -- polynomial-ti
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1.6 Computational Geometry 1.6 Comp
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C++ Language Implementations Algori
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C Language Implementations ● POSI
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FORTRAN Language Implementations Al
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Lisp Language Implementations Algor
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Algorithm Repository -- Citations C
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Practical Algorithm Design -- User
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LEDA - A Library of Efficient Data
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Discrete Optimization Methods Discr
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Netlib / TOMS -- Collected Algorith
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Netlib / TOMS -- Collected Algorith
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Xtango and Polka Algorithm Animatio
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Combinatorica Combinatorica Combina
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file:///E|/WEBSITE/IMPLEMEN/GRAPHBA
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1.4.1 Connected Components 1.4.1 Co
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1.5.9 Graph Isomorphism 1.5.9 Graph
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1.2.3 Matrix Multiplication 1.2.3 M
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1.6.14 Motion Planning 1.6.14 Motio
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1.4.9 Network Flow 1.4.9 Network Fl
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1.1.2 Priority Queues 1.1.2 Priorit
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1.5.10 Steiner Tree 1.5.10 Steiner
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1.4.5 Transitive Closure and Reduct
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About the Book -- The Algorithm Des
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About the Book -- The Algorithm Des
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Genocop -- Optimization via Genetic
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1.2.6 Linear Programming ● Knapsa
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1.2.7 Random Number Generation ●
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1.3.10 Satisfiability ● Constrain
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Qhull - higher dimensional convex h
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1.6.5 Nearest Neighbor Search 1.6.5
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1.6.7 Point Location 1.6.7 Point Lo
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1.6.10 Medial-Axis Transformation 1
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1.6.3 Triangulation 1.6.3 Triangula
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Nijenhuis and Wilf: Combinatorial A
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1.5.5 Hamiltonian Cycle 1.5.5 Hamil
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1.4.6 Matching 1.4.6 Matching INPUT
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A compendium of NP optimization pro
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1.6.9 Bin Packing 1.6.9 Bin Packing
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1.6.12 Simplifying Polygons 1.6.12
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1.6.13 Shape Similarity 1.6.13 Shap
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1.6.11 Polygon Partitioning 1.6.11
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1.4.3 Minimum Spanning Tree 1.4.3 M
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1.6.15 Maintaining Line Arrangement
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1.3.7 Generating Graphs 1.3.7 Gener
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1.2.11 Discrete Fourier Transform 1
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1.5.8 Edge Coloring 1.5.8 Edge Colo
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1.3.3 Median and Selection 1.3.3 Me
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1.3.1 Sorting 1.3.1 Sorting INPUT O
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Plugins for use with the CDROM Plug
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Implementation Challenges Next: Dat
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Implementation Challenges Next: Gra
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Implementation Challenges Next: Int
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Caveats Next: Data Structures Up: A
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1.1.1 Dictionaries ● Priority Que
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1.1.4 Graph Data Structures ● Gra
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1.1.5 Set Data Structures ● Gener
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1.2.1 Solving Linear Equations ●
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1.2.2 Bandwidth Reduction ● Topol
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1.2.4 Determinants and Permanents
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1.2.8 Factoring and Primality Testi
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1.2.9 Arbitrary Precision Arithmeti
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1.2.10 Knapsack Problem ● Linear
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1.3.2 Searching ● Sorting Go to t
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1.3.4 Generating Permutations ● C
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1.3.5 Generating Subsets ● Genera
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1.3.6 Generating Partitions ● Gen
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1.3.8 Calendrical Calculations Go t
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1.3.9 Job Scheduling ● Feedback E
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1.4.2 Topological Sorting Related P
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1.4.8 Edge and Vertex Connectivity
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1.4.10 Drawing Graphs Nicely ● Pl
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1.4.11 Drawing Trees ● Planarity
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1.4.12 Planarity Detection and Embe
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1.5.2 Independent Set ● Vertex Co
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1.5.3 Vertex Cover ● Independent
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1.5.4 Traveling Salesman Problem
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1.5.6 Graph Partition ● Network F
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1.5.7 Vertex Coloring Related Probl
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1.5.11 Feedback Edge/Vertex Set Go
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1.6.1 Robust Geometric Primitives G
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1.6.6 Range Search ● Kd-Trees ●
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1.6.8 Intersection Detection ● Ro
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1.6.16 Minkowski Sum Go to the corr
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1.7.1 Set Cover ● Set Packing ●
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1.7.2 Set Packing Go to the corresp
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1.7.3 String Matching ● Approxima
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1.7.4 Approximate String Matching
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1.7.5 Text Compression Go to the co
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1.7.6 Cryptography ● Text Compres
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1.7.7 Finite State Machine Minimiza
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1.7.8 Longest Common Substring ●
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1.7.9 Shortest Common Superstring G
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Handbook of Algorithms and Data Str
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Moret and Shapiro's Algorithms P to
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Index B Up: Index - All Index: B ba
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Index C Up: Index - All Index: C ch
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Index E Up: Index - All Index: E ed
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Index G Up: Index - All Index: G ga
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Index I Up: Index - All Index: I in
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Index L Up: Index - All Index: L li
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Index N Up: Index - All Index: N ne
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Index P Up: Index - All Index: P pa
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Index R Up: Index - All Index: R RA
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Index S successor supercomputer , s
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Index U Up: Index - All Index: U un
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Index W Up: Index - All Index: W wo
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Index (complete) bin packing bipart
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Index (complete) Fourier transform
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Index (complete) parenthesization p
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Index (complete) symmetry, exploiti
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Ranger - Nearest Neighbor Search in
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DIMACS Implementation Challenges Pe
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Stony Brook Project Implementations
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Clarkson's higher dimensional conve
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file:///E|/WEBSITE/BIBLIO/TESTDATA/
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SimPack/Sim++ Simulation Toolkit Si
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Fire-Engine and Spare-Parts String
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Geolab -- Computational Geometry Sy
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Calendrical Calculations Calendrica
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David Eppstein's Knuth-Morris-Pratt
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Mike Trick's Graph Coloring Resourc
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Frank Ruskey's Combinatorial Genera
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Arrange - maintainance of arrangeme
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LP_SOLVE: Linear Programming Code L
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PARI - Package for Number Theory Se
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TSP solvers TSP solvers tsp-solve i
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PHYLIP -- inferring phylogenic tree
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Skeletonization Software (2-D) Skel
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agrep - Approximate General Regular
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CAP -- Contig Assembly Program CAP
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NAUTY -- Graph Isomorphism NAUTY --
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BIPM -- Bipartite Matching Codes BI
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LAPACK and LINPACK -- Linear Algebr
- Page 1483 and 1484:
User Comments User Comments Archive
- Page 1485 and 1486:
The Algorithm Design Manual Most pr
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The Algorithm Design Manual 5.6 War
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