18.04.2013
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Index (complete) unranking permutations unranking subsets unsorted array unsorted list unweighted graphs - spanning trees upper bound upper triangular matrix Utah validation Vancouver Stock Exchange Vandermonde matrices vantage point tree variable elimination variable length encodings vector quantification vector sums Vertex Coloring, , , vertex coloring - applications vertex coloring - bipartite graphs vertex coloring - related problems, , vertex cover, vertex cover - approximation algorithm vertex cover - hardness proof, vertex cover - related problems, , vertex degree, vertex disjoint paths video - algorithm animation video compression, virtual memory, , virtual memory - algorithms virtual memory - performance virtual reality applications visibility graphs, Viterbi algorithm Vizing's theorem, file:///E|/BOOK/BOOK5/INDEX_AL.HTM (54 of 56) [19/1/2003 1:33:45]
Index (complete) VLSI circuit layout, VLSI design problems volume computations, von Emde Boas queue von Neumann, J. Voronoi diagram Voronoi Diagrams Voronoi diagrams - nearest neighbor search Voronoi diagrams - related problems, , , , walk-through Waring's problem, Warshall's algorithm war story, , , , , , , , , water pipes wavelets weakly-connected graphs, web weighted graphs, applications Winograd's algorithm wire length minimization wiring layout problems word ladders, worker assignment - scheduling world's record TSP worst-case complexity WWW site X-windows Xerox machines - scheduling XRLF XTango, , , , , , , , , , , , , , , , , , , Young tableaux zero-knowledge proofs Zipf's law zone theorem, file:///E|/BOOK/BOOK5/INDEX_AL.HTM (55 of 56) [19/1/2003 1:33:45]
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Page 1 and 2:
The Algorithm Design Manual Next: P
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Page 3 and 4:
Preface Next: Acknowledgments Up: T
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Preface one stressing design over a
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Acknowledgments Mon Jun 2 23:33:50
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Contents Next: Techniques Up: The A
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Contents ■ All-Pairs Shortest Pat
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Contents ■ Graph Problems: Polyno
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Contents ● About this document ..
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Page 17 and 18:
The Algorithm Design Manual The Alg
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Page 19 and 20:
Lecture Notes -- Analysis of Algori
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Lecture Notes -- Analysis of Algori
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Page 23 and 24:
The Stony Brook Algorithm Repositor
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Page 25 and 26:
Techniques Next: Introduction to Al
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Page 27 and 28:
Techniques Algorithms Mon Jun 2 23:
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Page 29 and 30:
Introduction to Algorithms ● Reas
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Page 31 and 32:
Data Structures and Sorting divide-
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Page 33 and 34:
Breaking Problems Down ● Dynamic
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Page 35 and 36:
Graph Algorithms The take-home less
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Page 37 and 38:
Combinatorial Search and Heuristic
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Page 39 and 40:
Intractable Problems and Approximat
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Page 41 and 42:
How to Design Algorithms Next: Reso
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Page 43 and 44:
How to Design Algorithms with a cou
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Page 45 and 46:
How to Design Algorithms Next: Reso
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Page 47 and 48:
A Catalog of Algorithmic Problems N
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Page 49 and 50:
A Catalog of Algorithmic Problems
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Page 51 and 52:
Algorithmic Resources Next: Softwar
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Page 53 and 54:
References L. Adleman. Algorithmic
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References AS89 Ata83 Ata84 problem
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References BJL 91 A. Blum, T. Jiang
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References BS81 BS86 BS93 BS96 BS97
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Page 61 and 62:
References J. M. Chambers. Partial
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Page 63 and 64:
References CT80 CT92 1971. G. Carpa
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Page 65 and 66:
References K. Daniels and V. Milenk
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Page 67 and 68:
References (FOCS), pages 60-69, 199
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Page 69 and 70:
References FM71 M. Fischer and A. M
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Page 71 and 72:
References History of Computing, 7:
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Page 73 and 74:
References N. Gibbs, W. Poole, and
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Page 75 and 76:
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
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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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Page 97 and 98:
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:
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Page 103 and 104:
Correctness and Efficiency Next: Co
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Page 105 and 106:
Correctness NearestNeighborTSP(P) P
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Page 107 and 108:
Correctness Figure: A bad example f
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Page 109 and 110:
Efficiency Next: Expressing Algorit
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Page 111 and 112:
Keeping Score Next: The RAM Model o
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Page 113 and 114:
The RAM Model of Computation substa
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Page 115 and 116:
Best, Worst, and Average-Case Compl
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Page 117 and 118:
The Big Oh Notation Figure: Illustr
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Page 119 and 120:
Logarithms Next: Modeling the Probl
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Logarithms justified in ignoring th
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Page 123 and 124:
Modeling the Problem Figure: Modeli
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Page 125 and 126:
About the War Stories Next: War Sto
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Page 127 and 128:
War Story: Psychic Modeling Next: E
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Page 129 and 130:
War Story: Psychic Modeling have pu
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Page 131 and 132:
War Story: Psychic Modeling Next: E
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Page 133 and 134:
Exercises (b) If I prove that an al
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Page 135 and 136:
Fundamental Data Types Next: Contai
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Page 137 and 138:
Containers Next: Dictionaries Up: F
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Page 139 and 140:
Dictionaries Next: Binary Search Tr
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Page 141 and 142:
Binary Search Trees BinaryTreeQuery
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Page 143 and 144:
Priority Queues Next: Specialized D
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Page 145 and 146:
Specialized Data Structures Next: S
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Page 147 and 148:
Sorting Next: Applications of Sorti
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Page 149 and 150:
Applications of Sorting Figure: Con
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Page 151 and 152:
Data Structures Next: Incremental I
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Page 153 and 154:
Incremental Insertion Next: Divide
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Page 155 and 156:
Randomization Next: Bucketing Techn
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Randomization Next: Bucketing Techn
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Page 159 and 160:
Bucketing Techniques Algorithms Mon
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Page 161 and 162:
War Story: Stripping Triangulations
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War Story: Stripping Triangulations
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War Story: Mystery of the Pyramids
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War Story: Mystery of the Pyramids
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War Story: String 'em Up We were co
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War Story: String 'em Up Figure: Su
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Page 173 and 174:
Exercises Next: Implementation Chal
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Page 175 and 176:
Exercises used to select the pivot.
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Page 177 and 178:
Dynamic Programming Next: Fibonacci
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Page 179 and 180:
Fibonacci numbers Next: The Partiti
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Page 181 and 182:
Fibonacci numbers Next: The Partiti
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Page 183 and 184:
The Partition Problem . What is the
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Page 185 and 186:
The Partition Problem Figure: Dynam
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Page 187 and 188:
Approximate String Matching Next: L
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Page 189 and 190:
Approximate String Matching The val
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Page 191 and 192:
Longest Increasing Sequence Will th
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Page 193 and 194:
Minimum Weight Triangulation Next:
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Page 195 and 196:
Limitations of Dynamic Programming
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Page 197 and 198:
War Story: Evolution of the Lobster
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Page 199 and 200:
War Story: Evolution of the Lobster
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Page 201 and 202:
War Story: What's Past is Prolog Ne
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War Story: What's Past is Prolog th
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Page 205 and 206:
War Story: Text Compression for Bar
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Page 207 and 208:
War Story: Text Compression for Bar
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Page 209 and 210:
Divide and Conquer Next: Fast Expon
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Page 211 and 212:
Fast Exponentiation Next: Binary Se
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Page 213 and 214:
Binary Search Next: Square and Othe
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Page 215 and 216:
Exercises Next: Implementation Chal
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Page 217 and 218:
Exercises whose denominations are ,
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Page 219 and 220:
The Friendship Graph Next: Data Str
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The Friendship Graph friendship gra
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Data Structures for Graphs linked t
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Page 225 and 226:
War Story: Getting the Graph ``Well
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Page 227 and 228:
Traversing a Graph Next: Breadth-Fi
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Page 229 and 230:
Breadth-First Search Next: Depth-Fi
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Page 231 and 232:
Depth-First Search Next: Applicatio
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Depth-First Search Next: Applicatio
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Connected Components Next: Tree and
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Two-Coloring Graphs Next: Topologic
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Topological Sorting Next: Articulat
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Modeling Graph Problems Next: Minim
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Modeling Graph Problems good line s
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Page 245 and 246:
Minimum Spanning Trees ● Prim's A
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Page 247 and 248:
Prim's Algorithm inserted edge (x,y
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Page 249 and 250:
Kruskal's Algorithm a tree of weigh
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Page 251 and 252:
Dijkstra's Algorithm Next: All-Pair
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Page 253 and 254:
All-Pairs Shortest Path Next: War S
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Page 255 and 256:
War Story: Nothing but Nets Next: W
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Page 257 and 258:
War Story: Nothing but Nets ``You a
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Page 259 and 260:
War Story: Dialing for Documents Ne
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Page 261 and 262:
War Story: Dialing for Documents If
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Page 263 and 264:
War Story: Dialing for Documents CO
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Page 265 and 266:
Exercises Next: Implementation Chal
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Page 267 and 268:
Exercises Prove the statement or gi
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Page 269 and 270:
Backtracking report it. kth element
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Page 271 and 272:
Constructing All Subsets Next: Cons
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Page 273 and 274:
Constructing All Paths in a Graph N
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Page 275 and 276:
Search Pruning Next: Bandwidth Mini
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Bandwidth Minimization immediately
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Page 279 and 280:
War Story: Covering Chessboards Nex
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Page 281 and 282:
War Story: Covering Chessboards att
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Page 283 and 284:
Heuristic Methods Mon Jun 2 23:33:5
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Page 285 and 286:
Simulated Annealing Return S then u
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Page 287 and 288:
Traveling Salesman Problem Next: Ma
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Page 289 and 290:
Independent Set Next: Circuit Board
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Page 291 and 292:
Neural Networks Next: Genetic Algor
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Page 293 and 294:
Genetic Algorithms Next: War Story:
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War Story: Annealing Arrays Next: P
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Page 297 and 298:
War Story: Annealing Arrays optimal
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Page 299 and 300:
Parallel Algorithms Next: War Story
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Page 301 and 302:
War Story: Going Nowhere Fast Next:
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Page 303 and 304:
Exercises Next: Implementation Chal
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Page 305 and 306:
Problems and Reductions Next: Simpl
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Page 307 and 308:
Simple Reductions Next: Hamiltonian
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Page 309 and 310:
Hamiltonian Cycles Next: Independen
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Page 311 and 312:
Independent Set and Vertex Cover pr
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Page 313 and 314:
Clique and Independent Set These la
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Page 315 and 316:
Satisfiability Mon Jun 2 23:33:50 E
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Page 317 and 318:
The Theory of NP-Completeness Next:
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Page 319 and 320:
3-Satisfiability where for , , , an
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Page 321 and 322:
Integer Programming Next: Vertex Co
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Page 323 and 324:
Integer Programming possible IP ins
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Page 325 and 326:
Vertex Cover reduction for the 3-SA
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Page 327 and 328:
Other NP-Complete Problems hard. Th
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Page 329 and 330:
The Art of Proving Hardness easiest
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Page 331 and 332:
War Story: Hard Against the Clock N
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Page 333 and 334:
War Story: Hard Against the Clock I
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Page 335 and 336:
Approximation Algorithms Next: Appr
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Page 337 and 338:
Approximating Vertex Cover Next: Th
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Page 339 and 340:
The Euclidean Traveling Salesman Ne
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Page 341 and 342:
The Euclidean Traveling Salesman Ne
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Page 343 and 344:
Exercises 1. Prove that the low deg
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Page 345 and 346:
Data Structures Next: Dictionaries
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Page 347 and 348:
Dictionaries Next: Priority Queues
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Page 349 and 350:
Dictionaries use a function that ma
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Page 351 and 352:
Dictionaries Implementation-oriente
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Priority Queues ● Besides access
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Page 355 and 356:
Priority Queues Fibonacci heaps [FT
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Page 357 and 358:
Suffix Trees and Arrays Figure: A t
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Page 359 and 360:
Suffix Trees and Arrays [GBY91]. Se
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Page 361 and 362:
Graph Data Structures algorithms).
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Page 363 and 364:
Graph Data Structures including the
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Page 365 and 366:
Set Data Structures Next: Kd-Trees
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Page 367 and 368:
Set Data Structures parent pointers
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Page 369 and 370:
Kd-Trees Next: Numerical Problems U
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Page 371 and 372:
Kd-Trees about p. Say we are lookin
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Page 373 and 374:
Numerical Problems Next: Solving Li
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Page 375 and 376:
Numerical Problems Mon Jun 2 23:33:
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Page 377 and 378:
Solving Linear Equations algorithm
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Page 379 and 380:
Solving Linear Equations Matrix inv
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Page 381 and 382:
Bandwidth Reduction images near eac
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Page 383 and 384:
Matrix Multiplication Next: Determi
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Page 385 and 386:
Matrix Multiplication The linear al
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Page 387 and 388:
Determinants and Permanents Next: C
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Page 389 and 390:
Determinants and Permanents exposit
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Page 391 and 392:
Constrained and Unconstrained Optim
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Page 393 and 394:
Constrained and Unconstrained Optim
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Page 395 and 396:
Linear Programming variable assignm
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Page 397 and 398:
Linear Programming The book [MW93]
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Page 399 and 400:
Random Number Generation Next: Fact
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Page 401 and 402:
Random Number Generation is largely
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Page 403 and 404:
Random Number Generation Related Pr
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Page 405 and 406:
Factoring and Primality Testing The
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Page 407 and 408:
Factoring and Primality Testing Alg
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Page 409 and 410:
Arbitrary-Precision Arithmetic If y
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Page 411 and 412:
Arbitrary-Precision Arithmetic PARI
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Page 413 and 414:
Knapsack Problem Next: Discrete Fou
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Page 415 and 416:
Knapsack Problem is a subset of S'
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Page 417 and 418:
Discrete Fourier Transform Next: Co
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Page 419 and 420:
Discrete Fourier Transform an algor
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Page 421 and 422:
Combinatorial Problems Next: Sortin
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Page 423 and 424:
Sorting Next: Searching Up: Combina
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Page 425 and 426:
Sorting The simplest approach to ex
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Page 427 and 428:
Sorting operations, implying an sor
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Page 429 and 430:
Searching large performance improve
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Page 431 and 432:
Searching Notes: Mehlhorn and Tsaka
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Page 433 and 434:
Median and Selection followed by fi
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Page 435 and 436:
Generating Permutations Next: Gener
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Page 437 and 438:
Generating Permutations The rank/un
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Page 439 and 440:
Generating Permutations generating
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Page 441 and 442:
Generating Subsets look right when
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Page 443 and 444:
Generating Subsets above for detail
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Generating Partitions Although the
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Page 447 and 448:
Generating Partitions Two related c
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Page 449 and 450:
Generating Graphs generate: ● Do
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Generating Graphs Combinatorica [Sk
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Page 453 and 454:
Calendrical Calculations Next: Job
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Page 455 and 456:
Calendrical Calculations Gregorian,
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Page 457 and 458:
Job Scheduling ● To assign a set
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Page 459 and 460:
Job Scheduling shop scheduling incl
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Page 461 and 462:
Satisfiability logic, and automatic
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Page 463 and 464:
Satisfiability Next: Graph Problems
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Page 465 and 466:
Graph Problems: Polynomial-Time rec
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Page 467 and 468:
Connected Components Testing the co
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Page 469 and 470:
Connected Components discussing gra
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Page 471 and 472:
Topological Sorting contradiction t
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Page 473 and 474:
Minimum Spanning Tree Next: Shortes
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Page 475 and 476:
Minimum Spanning Tree help you sort
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Page 477 and 478:
Shortest Path Next: Transitive Clos
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Page 479 and 480:
Shortest Path easier to program tha
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Page 481 and 482:
Shortest Path Related Problems: Net
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Page 483 and 484:
Transitive Closure and Reduction
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Page 485 and 486:
Transitive Closure and Reduction Al
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Page 487 and 488:
Matching augmenting paths and stopp
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Page 489 and 490:
Matching Combinatorica [Ski90] prov
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Page 491 and 492:
Eulerian Cycle / Chinese Postman ar
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Page 493 and 494:
Eulerian Cycle / Chinese Postman Ne
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Page 495 and 496:
Edge and Vertex Connectivity Severa
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Page 497 and 498:
Edge and Vertex Connectivity Next:
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Page 499 and 500:
Network Flow programming model for
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Page 501 and 502:
Network Flow Combinatorica [Ski90]
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Page 503 and 504:
Drawing Graphs Nicely vertices are
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Page 505 and 506:
Drawing Graphs Nicely labs.com/orgs
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Page 507 and 508:
Drawing Trees such as the map of th
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Page 509 and 510:
Planarity Detection and Embedding N
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Page 511 and 512:
Planarity Detection and Embedding p
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Page 513 and 514:
Graph Problems: Hard Problems ● C
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Page 515 and 516:
Clique approximate even to within a
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Page 517 and 518:
Independent Set Next: Vertex Cover
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Page 519 and 520:
Independent Set Related Problems: C
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Page 521 and 522:
Vertex Cover Vertex cover and indep
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Page 523 and 524:
Traveling Salesman Problem Next: Ha
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Page 525 and 526:
Traveling Salesman Problem practice
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Page 527 and 528:
Traveling Salesman Problem Size is
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Page 529 and 530:
Hamiltonian Cycle Eulerian cycle, p
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Page 531 and 532:
Graph Partition Next: Vertex Colori
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Page 533 and 534:
Graph Partition annealing, is almos
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Page 535 and 536:
Vertex Coloring Several special cas
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Page 537 and 538:
Vertex Coloring Notes: An excellent
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Page 539 and 540:
Edge Coloring The minimum number of
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Page 541 and 542:
Graph Isomorphism Next: Steiner Tre
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Page 543 and 544:
Graph Isomorphism vertices into equ
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Page 545 and 546:
Steiner Tree Next: Feedback Edge/Ve
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Page 547 and 548:
Steiner Tree The worst case for a m
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Page 549 and 550:
Feedback Edge/Vertex Set Next: Comp
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Page 551 and 552:
Feedback Edge/Vertex Set An interes
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Page 553 and 554:
Computational Geometry complete bib
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Page 555 and 556:
Robust Geometric Primitives Next: C
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Page 557 and 558:
Robust Geometric Primitives ● Are
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Page 559 and 560:
Robust Geometric Primitives Related
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Page 561 and 562:
Convex Hull ● How many dimensions
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Page 563 and 564:
Convex Hull http://www.cs.att.com/n
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Page 565 and 566:
Triangulation Next: Voronoi Diagram
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Page 567 and 568:
Triangulation GEOMPACK is a suite o
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Page 569 and 570:
Voronoi Diagrams Next: Nearest Neig
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Page 571 and 572:
Voronoi Diagrams McDonald's, the ti
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Page 573 and 574:
Nearest Neighbor Search Next: Range
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Page 575 and 576:
Nearest Neighbor Search Implementat
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Page 577 and 578:
Range Search Next: Point Location U
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Page 579 and 580:
Range Search subdivisions in C++. I
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Page 581 and 582:
Point Location the n edges for inte
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Page 583 and 584:
Point Location More recently, there
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Page 585 and 586:
Intersection Detection ● Do you w
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Page 587 and 588:
Intersection Detection Implementati
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Page 589 and 590:
Bin Packing Next: Medial-Axis Trans
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Page 591 and 592:
Bin Packing approach for general sh
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Page 593 and 594:
Medial-Axis Transformation Next: Po
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Page 595 and 596:
Medial-Axis Transformation Implemen
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Page 597 and 598:
Polygon Partitioning number of piec
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Page 599 and 600:
Simplifying Polygons Next: Shape Si
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Page 601 and 602:
Simplifying Polygons vertices and o
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Page 603 and 604:
Shape Similarity Next: Motion Plann
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Page 605 and 606:
Shape Similarity or how close it is
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Page 607 and 608:
Motion Planning There is a wide ran
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Page 609 and 610:
Motion Planning often arise in the
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Page 611 and 612:
Maintaining Line Arrangements Think
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Page 613 and 614:
Maintaining Line Arrangements Next:
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Page 615 and 616:
Minkowski Sum where x+y is the vect
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Page 617 and 618:
Set and String Problems Next: Set C
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Page 619 and 620:
Set Cover Next: Set Packing Up: Set
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Page 621 and 622:
Set Cover Figure: Hitting set is du
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Page 623 and 624:
Set Packing Next: String Matching U
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Page 625 and 626:
Set Packing Notes: An excellent exp
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Page 627 and 628:
String Matching shouldn't try. Furt
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Page 629 and 630:
String Matching and texts, I recomm
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Page 631 and 632:
Approximate String Matching This sa
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Page 633 and 634:
Approximate String Matching http://
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Page 635 and 636:
Text Compression Next: Cryptography
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Page 637 and 638:
Text Compression code string. ASCII
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Page 639 and 640:
Cryptography Next: Finite State Mac
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Page 641 and 642:
Cryptography ● How can I validate
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Page 643 and 644:
Cryptography MD5 [Riv92] is the sec
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Page 645 and 646:
Finite State Machine Minimization F
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Page 647 and 648:
Finite State Machine Minimization S
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Page 649 and 650:
Longest Common Substring than edit
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Page 651 and 652:
Longest Common Substring include [A
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Page 653 and 654:
Shortest Common Superstring Finding
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Page 655 and 656:
Software systems Next: LEDA Up: Alg
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Page 657 and 658:
LEDA Next: Netlib Up: Software syst
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Page 659 and 660:
Netlib Algorithms Mon Jun 2 23:33:5
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Page 661 and 662:
The Stanford GraphBase Next: Combin
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Page 663 and 664:
Algorithm Animations with XTango Ne
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Page 665 and 666:
Programs from Books Next: Discrete
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Page 667 and 668:
Handbook of Data Structures and Alg
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Page 669 and 670:
Algorithms from P to NP Next: Compu
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Page 671 and 672:
Algorithms in C++ Next: Data Source
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Page 673 and 674:
Textbooks Next: On-Line Resources U
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Page 675 and 676:
On-Line Resources Next: Literature
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Page 677 and 678:
People Next: Software Up: On-Line R
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Page 679 and 680:
Professional Consulting Services Ne
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Page 681 and 682:
Index A Up: Index - All Index: A ab
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Page 683 and 684:
Index A artists steal ASA ASCII asp
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Page 685 and 686:
Index B binary representation - sub
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Page 687 and 688:
Index C Up: Index - All Index: C C+
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Page 689 and 690:
Index C clustering , , co-NP coding
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Page 691 and 692:
Index C consulting services , conta
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Page 693 and 694:
Index D Up: Index - All Index: D DA
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Page 695 and 696:
Index D Dictionaries dictionaries -
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Page 697 and 698:
Index D dynamic programming - appli
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Page 699 and 700:
Index E empirical results - heurist
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Page 701 and 702:
Index F Up: Index - All Index: F fa
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Page 703 and 704:
Index F frequency domain friend-or-
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Page 705 and 706:
Index G geometric shortest path , g
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Page 707 and 708:
Index H Up: Index - All Index: H ha
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Page 709 and 710:
Index H Algorithms Tue Jun 3 11:59:
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Page 711 and 712:
Index I independent set - alternate
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Page 713 and 714:
Index J Up: Index - All Index: J ji
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Page 715 and 716:
Index K Algorithms Tue Jun 3 11:59:
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Page 717 and 718:
Index L linear congruential generat
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Page 719 and 720:
Index M Up: Index - All Index: M ma
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Page 721 and 722:
Index M mindset minima minimax sear
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Page 723 and 724:
Index N Up: Index - All Index: N na
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Page 725 and 726:
Index N numerical analysis numerica
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Page 727 and 728:
Index O overlap graph overpasses -
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Page 729 and 730:
Index P , , , , , , , , , , passwor
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Index P polygons polygon triangulat
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Page 733 and 734:
Index Q Up: Index - All Index: Q Qh
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Page 735 and 736:
Index R ranking combinatorial objec
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Page 737 and 738:
Index S Up: Index - All Index: S s-
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Page 739 and 740:
Index S Shape Similarity shape simp
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Page 741 and 742:
Index S solar year Solving Linear E
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Page 743 and 744:
Index S straight-line graph drawing
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Page 745 and 746:
Index T Up: Index - All Index: T ta
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Page 747 and 748:
Index T trees - matching trees - pa
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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
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Page 755 and 756:
Index (complete) Up: Index - All In
-
Page 757 and 758:
Index (complete) arm, robot around
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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
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Page 765 and 766:
Index (complete) counting paths, co
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Page 767 and 768:
Index (complete) deletion from bina
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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
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Page 775 and 776:
Index (complete) geom.bib, geometri
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Page 777 and 778:
Index (complete) Hamiltonian Cycle
-
Page 779 and 780:
Index (complete) implementation cha
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Page 781 and 782:
Index (complete) job-shop schedulin
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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
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Page 791 and 792:
Index (complete) parallel algorithm
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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:
Index (complete) trial division Tri
-
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
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Page 817 and 818:
1.4.7 Eulerian Cycle / Chinese Post
-
Page 819 and 820:
Online Bibliographies Online Biblio
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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
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Page 827 and 828:
CD-ROM Installation install a sound
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Page 829 and 830:
Thanks! Frank Ruscica file:///E|/WE
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Page 831 and 832:
Thanks! Zhong Li Thanks also to Fil
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Page 833 and 834:
CD-ROM Installation To use the CD-R
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Page 835 and 836:
Binary Search in Action Binary Sear
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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
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Page 843 and 844:
Lecture 1 - analyzing algorithms Ne
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Page 845 and 846:
Lecture 1 - analyzing algorithms Yo
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Page 847 and 848:
Lecture 1 - analyzing algorithms Th
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Page 849 and 850:
Lecture 1 - analyzing algorithms Th
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Page 851 and 852:
Lecture 1 - analyzing algorithms is
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Page 853 and 854:
Lecture 2 - asymptotic notation How
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Page 855 and 856:
Lecture 2 - asymptotic notation Not
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Page 857 and 858:
Lecture 2 - asymptotic notation alg
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Page 859 and 860:
Lecture 2 - asymptotic notation Sup
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Page 861 and 862:
Lecture 2 - asymptotic notation Nex
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Page 863 and 864:
Lecture 3 - recurrence relations Is
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Page 865 and 866:
Lecture 3 - recurrence relations
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Page 867 and 868:
Lecture 3 - recurrence relations Li
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Page 869 and 870:
Lecture 3 - recurrence relations Su
-
Page 871 and 872:
Lecture 3 - recurrence relations A
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Page 873 and 874:
Lecture 4 - heapsort Note iteration
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Page 875 and 876:
Lecture 4 - heapsort The convex hul
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Page 877 and 878:
Lecture 4 - heapsort 1. All leaves
-
Page 879 and 880:
Lecture 4 - heapsort left = 2i righ
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Page 881 and 882:
Lecture 4 - heapsort Since this sum
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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
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Page 893 and 894:
Lecture 5 - quicksort The worst cas
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Page 895 and 896:
Lecture 5 - quicksort Mon Jun 2 09:
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Page 897 and 898:
Lecture 6 - linear sorting Since 2i
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Page 899 and 900:
Lecture 6 - linear sorting With uni
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Page 901 and 902:
Lecture 6 - linear sorting Listen T
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Page 903 and 904:
Lecture 7 - elementary data structu
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Page 905 and 906:
Lecture 7 - elementary data structu
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Page 907 and 908:
Lecture 7 - elementary data structu
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Page 909 and 910:
Lecture 7 - elementary data structu
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Page 911 and 912:
Lecture 7 - elementary data structu
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Page 913 and 914:
Lecture 7 - elementary data structu
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Page 915 and 916:
Lecture 7 - elementary data structu
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Page 917 and 918:
Lecture 8 - binary trees - Joyce Ki
-
Page 919 and 920:
Lecture 8 - binary trees TREE-MINIM
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Page 921 and 922:
Lecture 8 - binary trees Inorder-Tr
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Page 923 and 924:
Lecture 8 - binary trees Listen To
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Page 925 and 926:
Lecture 8 - binary trees insert(b)
-
Page 927 and 928:
Lecture 8 - binary trees Not (1) -
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Page 929 and 930:
Lecture 9 - catch up Next: Lecture
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Page 931 and 932:
Lecture 10 - tree restructuring 1.
-
Page 933 and 934:
Lecture 10 - tree restructuring Lis
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Page 935 and 936:
Lecture 10 - tree restructuring Not
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Page 937 and 938:
Lecture 10 - tree restructuring Cas
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Page 939 and 940:
Lecture 11 - backtracking Next: Lec
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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
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Page 947 and 948:
Lecture 12 - introduction to dynami
-
Page 949 and 950:
Lecture 12 - introduction to dynami
-
Page 951 and 952:
Lecture 12 - introduction to dynami
-
Page 953 and 954:
Lecture 12 - introduction to dynami
-
Page 955 and 956:
Lecture 12 - introduction to dynami
-
Page 957 and 958:
Lecture 13 - dynamic programming ap
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Page 959 and 960:
Lecture 13 - dynamic programming ap
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Page 961 and 962:
Lecture 13 - dynamic programming ap
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Page 963 and 964:
Lecture 14 - data structures for gr
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Page 965 and 966:
Lecture 14 - data structures for gr
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Page 967 and 968:
Lecture 14 - data structures for gr
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Page 969 and 970:
Lecture 14 - data structures for gr
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Page 971 and 972:
Lecture 14 - data structures for gr
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Page 973 and 974:
Lecture 14 - data structures for gr
-
Page 975 and 976:
Lecture 15 - DFS and BFS Next: Lect
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Page 977 and 978:
Lecture 15 - DFS and BFS In a DFS o
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Page 979 and 980:
Lecture 15 - DFS and BFS It could u
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Page 981 and 982:
Lecture 15 - DFS and BFS Algorithm
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Page 983 and 984:
Lecture 16 - applications of DFS an
-
Page 985 and 986:
Lecture 16 - applications of DFS an
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Page 987 and 988:
Lecture 16 - applications of DFS an
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Page 989 and 990:
Lecture 17 - minimum spanning trees
-
Page 991 and 992:
Lecture 17 - minimum spanning trees
-
Page 993 and 994:
Lecture 17 - minimum spanning trees
-
Page 995 and 996:
Lecture 17 - minimum spanning trees
-
Page 997 and 998:
Lecture 17 - minimum spanning trees
-
Page 999 and 1000:
Lecture 18 - shortest path algorthm
-
Page 1001 and 1002:
Lecture 18 - shortest path algorthm
-
Page 1003 and 1004:
Lecture 18 - shortest path algorthm
-
Page 1005 and 1006:
Lecture 18 - shortest path algorthm
-
Page 1007 and 1008:
Lecture 18 - shortest path algorthm
-
Page 1009 and 1010:
Lecture 19 - satisfiability Next: L
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Page 1011 and 1012:
Lecture 19 - satisfiability Why? Th
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Page 1013 and 1014:
Lecture 19 - satisfiability between
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Page 1015 and 1016:
Lecture 19 - satisfiability Note th
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Page 1017 and 1018:
Lecture 20 - integer programming Ex
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Page 1019 and 1020:
Lecture 20 - integer programming Ne
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Page 1021 and 1022:
Lecture 21 - vertex cover Proof: VC
-
Page 1023 and 1024:
Lecture 21 - vertex cover Question:
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Page 1025 and 1026:
Lecture 21 - vertex cover seen to d
-
Page 1027 and 1028:
Lecture 22 - techniques for proving
-
Page 1029 and 1030:
Lecture 22 - techniques for proving
-
Page 1031 and 1032:
Lecture 22 - techniques for proving
-
Page 1033 and 1034:
Lecture 22 - techniques for proving
-
Page 1035 and 1036:
Lecture 23 - approximation algorith
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Page 1037 and 1038:
Lecture 23 - approximation algorith
-
Page 1039 and 1040:
Lecture 23 - approximation algorith
-
Page 1041 and 1042:
Lecture 23 - approximation algorith
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Page 1043 and 1044:
Lecture 23 - approximation algorith
-
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
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Page 1053 and 1054:
1.6.2 Convex Hull Related Problems
-
Page 1055 and 1056:
Visual Links Index file:///E|/WEBSI
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Page 1057 and 1058:
Visual Links Index file:///E|/WEBSI
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Page 1059 and 1060:
Visual Links Index file:///E|/WEBSI
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Page 1061 and 1062:
Visual Links Index file:///E|/WEBSI
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Page 1063 and 1064:
Visual Links Index file:///E|/WEBSI
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Page 1065 and 1066:
Visual Links Index file:///E|/WEBSI
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Page 1067 and 1068:
Visual Links Index file:///E|/WEBSI
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Page 1069 and 1070:
Visual Links Index file:///E|/WEBSI
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Page 1071 and 1072:
Visual Links Index file:///E|/WEBSI
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Page 1073 and 1074:
Visual Links Index file:///E|/WEBSI
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Page 1075 and 1076:
Visual Links Index file:///E|/WEBSI
-
Page 1077 and 1078:
Visual Links Index file:///E|/WEBSI
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Page 1079 and 1080:
Visual Links Index file:///E|/WEBSI
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Page 1081 and 1082:
Visual Links Index file:///E|/WEBSI
-
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
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Page 1091 and 1092:
C++ Language Implementations Algori
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Page 1093 and 1094:
C Language Implementations ● POSI
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Page 1095 and 1096:
FORTRAN Language Implementations Al
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Page 1097 and 1098:
Lisp Language Implementations Algor
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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
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Page 1107 and 1108:
Netlib / TOMS -- Collected Algorith
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Page 1109 and 1110:
Netlib / TOMS -- Collected Algorith
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Page 1111 and 1112:
Xtango and Polka Algorithm Animatio
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Page 1113 and 1114:
Combinatorica Combinatorica Combina
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Page 1115 and 1116:
file:///E|/WEBSITE/IMPLEMEN/GRAPHBA
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Page 1117 and 1118:
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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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
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Page 1125 and 1126:
1.4.9 Network Flow 1.4.9 Network Fl
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Page 1127 and 1128:
1.1.2 Priority Queues 1.1.2 Priorit
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Page 1129 and 1130:
1.5.10 Steiner Tree 1.5.10 Steiner
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Page 1131 and 1132:
1.4.5 Transitive Closure and Reduct
-
Page 1133 and 1134:
About the Book -- The Algorithm Des
-
Page 1135 and 1136:
About the Book -- The Algorithm Des
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Page 1137 and 1138:
Genocop -- Optimization via Genetic
-
Page 1139 and 1140:
1.2.6 Linear Programming ● Knapsa
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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
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Page 1153 and 1154:
1.6.3 Triangulation 1.6.3 Triangula
-
Page 1155 and 1156:
Nijenhuis and Wilf: Combinatorial A
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Page 1157 and 1158:
1.5.5 Hamiltonian Cycle 1.5.5 Hamil
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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
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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
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Page 1177 and 1178:
1.2.11 Discrete Fourier Transform 1
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Page 1179 and 1180:
1.5.8 Edge Coloring 1.5.8 Edge Colo
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Page 1181 and 1182:
1.3.3 Median and Selection 1.3.3 Me
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Page 1183 and 1184:
1.3.1 Sorting 1.3.1 Sorting INPUT O
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Page 1185 and 1186:
Plugins for use with the CDROM Plug
-
Page 1187 and 1188:
Implementation Challenges Next: Dat
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Page 1189 and 1190:
Implementation Challenges Next: Gra
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Page 1191 and 1192:
Implementation Challenges Next: Int
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Page 1193 and 1194:
Caveats Next: Data Structures Up: A
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Page 1195 and 1196:
1.1.1 Dictionaries ● Priority Que
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Page 1197 and 1198:
1.1.4 Graph Data Structures ● Gra
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Page 1199 and 1200:
1.1.5 Set Data Structures ● Gener
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Page 1201 and 1202:
1.2.1 Solving Linear Equations ●
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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
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Page 1209 and 1210:
1.2.9 Arbitrary Precision Arithmeti
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Page 1211 and 1212:
1.2.10 Knapsack Problem ● Linear
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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
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Page 1219 and 1220:
1.3.6 Generating Partitions ● Gen
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Page 1221 and 1222:
1.3.8 Calendrical Calculations Go t
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Page 1223 and 1224:
1.3.9 Job Scheduling ● Feedback E
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Page 1225 and 1226:
1.4.2 Topological Sorting Related P
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Page 1227 and 1228:
1.4.8 Edge and Vertex Connectivity
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Page 1229 and 1230:
1.4.10 Drawing Graphs Nicely ● Pl
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Page 1231 and 1232:
1.4.11 Drawing Trees ● Planarity
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Page 1233 and 1234:
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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Page 1239 and 1240:
1.5.4 Traveling Salesman Problem
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Page 1241 and 1242:
1.5.6 Graph Partition ● Network F
-
Page 1243 and 1244:
1.5.7 Vertex Coloring Related Probl
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Page 1245 and 1246:
1.5.11 Feedback Edge/Vertex Set Go
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1.6.1 Robust Geometric Primitives G
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Page 1249 and 1250:
1.6.6 Range Search ● Kd-Trees ●
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1.6.8 Intersection Detection ● Ro
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Page 1253 and 1254:
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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Page 1259 and 1260:
1.7.3 String Matching ● Approxima
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1.7.4 Approximate String Matching
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Page 1263 and 1264:
1.7.5 Text Compression Go to the co
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Page 1265 and 1266:
1.7.6 Cryptography ● Text Compres
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Page 1267 and 1268:
1.7.7 Finite State Machine Minimiza
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Page 1269 and 1270:
1.7.8 Longest Common Substring ●
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Page 1271 and 1272:
1.7.9 Shortest Common Superstring G
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Page 1273 and 1274:
Handbook of Algorithms and Data Str
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Page 1275 and 1276:
Moret and Shapiro's Algorithms P to
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Page 1277 and 1278:
Index B Up: Index - All Index: B ba
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Page 1279 and 1280:
Index C Up: Index - All Index: C ch
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Page 1281 and 1282:
Index E Up: Index - All Index: E ed
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Page 1283 and 1284:
Index G Up: Index - All Index: G ga
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Page 1285 and 1286:
Index I Up: Index - All Index: I in
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Page 1287 and 1288:
Index L Up: Index - All Index: L li
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Page 1289 and 1290:
Index N Up: Index - All Index: N ne
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Page 1291 and 1292:
Index P Up: Index - All Index: P pa
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Page 1293 and 1294:
Index R Up: Index - All Index: R RA
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Page 1295 and 1296:
Index S successor supercomputer , s
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Page 1297 and 1298:
Index U Up: Index - All Index: U un
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Page 1299 and 1300:
Index W Up: Index - All Index: W wo
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Page 1301 and 1302:
Index (complete) bin packing bipart
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Page 1303 and 1304:
Index (complete) Fourier transform
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Page 1305 and 1306:
Index (complete) parenthesization p
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Page 1307 and 1308:
Index (complete) symmetry, exploiti
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Page 1309 and 1310:
Ranger - Nearest Neighbor Search in
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Page 1311 and 1312:
DIMACS Implementation Challenges Pe
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Page 1313 and 1314:
Stony Brook Project Implementations
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Page 1315 and 1316:
Clarkson's higher dimensional conve
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file:///E|/WEBSITE/BIBLIO/TESTDATA/
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file:///E|/WEBSITE/BIBLIO/TESTDATA/
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The Algorithm Design Manual 5.6 War
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