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

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The RAM Model of Computation Next: BestWorst, and Average-Case Up: Keeping Score Previous: Keeping Score The RAM Model of Computation Machine-independent algorithm design depends upon a hypothetical computer called the Random Access Machine or RAM. Under this model of computation, we are confronted with a computer where: ● Each ``simple'' operation (+, *, -, =, if, call) takes exactly 1 time step. ● Loops and subroutines are not considered simple operations. Instead, they are the composition of many single-step operations. It makes no sense for ``sort'' to be a single-step operation, since sorting 1,000,000 items will take much longer than sorting 10 items. The time it takes to run through a loop or execute a subprogram depends upon the number of loop iterations or the specific nature of the subprogram. ● Each memory access takes exactly one time step, and we have as much memory as we need. The RAM model takes no notice of whether an item is in cache or on the disk, which simplifies the analysis. Under the RAM model, we measure the run time of an algorithm by counting up the number of steps it takes on a given problem instance. By assuming that our RAM executes a given number of steps per second, the operation count converts easily to the actual run time. The RAM is a simple model of how computers perform. A common complaint is that it is too simple, that these assumptions make the conclusions and analysis too coarse to believe in practice. For example, multiplying two numbers takes more time than adding two numbers on most processors, which violates the first assumption of the model. Memory access times differ greatly depending on whether data sits in cache or on the disk, thus violating the third assumption. Despite these complaints, the RAM is an excellent model for understanding how an algorithm will perform on a real computer. It strikes a fine balance by capturing the essential behavior of computers while being simple to work with. We use the RAM model because it is useful in practice. Every model has a size range over which it is useful. Take, for example, the model that the earth is flat. You might argue that this is a bad model, since the earth is not flat. However, when laying the foundation of a house, the flat earth model is sufficiently accurate that it can be reliably used. Further, it is so much easier to manipulate a flat-earth model that it is inconceivable that you would try to think spherically when you don't have to. The same situation is true with the RAM model of computation. We make an abstraction that in general is very useful. It is quite difficult to design an algorithm such that the RAM model will give you file:///E|/BOOK/BOOK/NODE12.HTM (1 of 2) [19/1/2003 1:28:08]
The RAM Model of Computation substantially misleading results, by performing either much better or much worse in practice than the model suggests. The robustness of the RAM enables us to analyze algorithms in a machine-independent way. Next: BestWorst, and Average-Case Up: Keeping Score Previous: Keeping Score Algorithms Mon Jun 2 23:33:50 EDT 1997 file:///E|/BOOK/BOOK/NODE12.HTM (2 of 2) [19/1/2003 1:28:08]
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The Algorithm Design Manual Next: P
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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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The Algorithm Design Manual The Alg
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Lecture Notes -- Analysis of Algori
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Lecture Notes -- Analysis of Algori
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The Stony Brook Algorithm Repositor
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Techniques Next: Introduction to Al
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Techniques Algorithms Mon Jun 2 23:
Page 29 and 30:
Introduction to Algorithms ● Reas
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Data Structures and Sorting divide-
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Breaking Problems Down ● Dynamic
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Graph Algorithms The take-home less
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Combinatorial Search and Heuristic
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Intractable Problems and Approximat
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How to Design Algorithms Next: Reso
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How to Design Algorithms with a cou
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How to Design Algorithms Next: Reso
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A Catalog of Algorithmic Problems N
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A Catalog of Algorithmic Problems
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Algorithmic Resources Next: Softwar
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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
Page 61 and 62: References J. M. Chambers. Partial
Page 63 and 64: References CT80 CT92 1971. G. Carpa
Page 65 and 66: References K. Daniels and V. Milenk
Page 67 and 68: References (FOCS), pages 60-69, 199
Page 69 and 70: References FM71 M. Fischer and A. M
Page 71 and 72: References History of Computing, 7:
Page 73 and 74: References N. Gibbs, W. Poole, and
Page 75 and 76: References Hof82 Hof89 Hol75 C. M.
Page 77 and 78: References IK75 Ita78 O. Ibarra and
Page 79 and 80: References Kir83 D. Kirkpatrick. Ef
Page 81 and 82: References object in 2-dimensional
Page 83 and 84: References LT79 LT80 8:99-118, 1995
Page 85 and 86: References Mil97 V. Milenkovic. Dou
Page 87 and 88: References Theoretical Computer Sci
Page 89 and 90: References Pav82 T. Pavlidis. Algor
Page 91 and 92: References Rei72 Rei91 Rei94 E. Rei
Page 93 and 94: References Prentice Hall, Englewood
Page 95 and 96: References SR95 SS71 SS90 SSS74 ST8
Page 97 and 98: References Tin90 Mikkel Thorup. On
Page 99 and 100: References Wel84 T. Welch. A techni
Page 101 and 102: References Algorithms Mon Jun 2 23:
Page 103 and 104: Correctness and Efficiency Next: Co
Page 105 and 106: Correctness NearestNeighborTSP(P) P
Page 107 and 108: Correctness Figure: A bad example f
Page 109 and 110: Efficiency Next: Expressing Algorit
Page 111: Keeping Score Next: The RAM Model o
Page 115 and 116: Best, Worst, and Average-Case Compl
Page 117 and 118: The Big Oh Notation Figure: Illustr
Page 119 and 120: Logarithms Next: Modeling the Probl
Page 121 and 122: Logarithms justified in ignoring th
Page 123 and 124: Modeling the Problem Figure: Modeli
Page 125 and 126: About the War Stories Next: War Sto
Page 127 and 128: War Story: Psychic Modeling Next: E
Page 129 and 130: War Story: Psychic Modeling have pu
Page 131 and 132: War Story: Psychic Modeling Next: E
Page 133 and 134: Exercises (b) If I prove that an al
Page 135 and 136: Fundamental Data Types Next: Contai
Page 137 and 138: Containers Next: Dictionaries Up: F
Page 139 and 140: Dictionaries Next: Binary Search Tr
Page 141 and 142: Binary Search Trees BinaryTreeQuery
Page 143 and 144: Priority Queues Next: Specialized D
Page 145 and 146: Specialized Data Structures Next: S
Page 147 and 148: Sorting Next: Applications of Sorti
Page 149 and 150: Applications of Sorting Figure: Con
Page 151 and 152: Data Structures Next: Incremental I
Page 153 and 154: Incremental Insertion Next: Divide
Page 155 and 156: Randomization Next: Bucketing Techn
Page 157 and 158: Randomization Next: Bucketing Techn
Page 159 and 160: Bucketing Techniques Algorithms Mon
Page 161 and 162: War Story: Stripping Triangulations
Page 163 and 164:
War Story: Stripping Triangulations
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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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Exercises Next: Implementation Chal
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Exercises used to select the pivot.
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Dynamic Programming Next: Fibonacci
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Fibonacci numbers Next: The Partiti
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Fibonacci numbers Next: The Partiti
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The Partition Problem . What is the
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The Partition Problem Figure: Dynam
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Approximate String Matching Next: L
Page 189 and 190:
Approximate String Matching The val
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Longest Increasing Sequence Will th
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Minimum Weight Triangulation Next:
Page 195 and 196:
Limitations of Dynamic Programming
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War Story: Evolution of the Lobster
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War Story: Evolution of the Lobster
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War Story: What's Past is Prolog Ne
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War Story: What's Past is Prolog th
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War Story: Text Compression for Bar
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War Story: Text Compression for Bar
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Divide and Conquer Next: Fast Expon
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Fast Exponentiation Next: Binary Se
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Binary Search Next: Square and Othe
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Page 217 and 218:
Exercises whose denominations are ,
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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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War Story: Getting the Graph ``Well
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Traversing a Graph Next: Breadth-Fi
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Breadth-First Search Next: Depth-Fi
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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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Minimum Spanning Trees ● Prim's A
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Prim's Algorithm inserted edge (x,y
Page 249 and 250:
Kruskal's Algorithm a tree of weigh
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Dijkstra's Algorithm Next: All-Pair
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All-Pairs Shortest Path Next: War S
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War Story: Nothing but Nets Next: W
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War Story: Nothing but Nets ``You a
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War Story: Dialing for Documents Ne
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War Story: Dialing for Documents If
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War Story: Dialing for Documents CO
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Exercises Prove the statement or gi
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Backtracking report it. kth element
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Constructing All Subsets Next: Cons
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Constructing All Paths in a Graph N
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Search Pruning Next: Bandwidth Mini
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Bandwidth Minimization immediately
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War Story: Covering Chessboards Nex
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War Story: Covering Chessboards att
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Heuristic Methods Mon Jun 2 23:33:5
Page 285 and 286:
Simulated Annealing Return S then u
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Traveling Salesman Problem Next: Ma
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Independent Set Next: Circuit Board
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Neural Networks Next: Genetic Algor
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Genetic Algorithms Next: War Story:
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War Story: Annealing Arrays Next: P
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War Story: Annealing Arrays optimal
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Parallel Algorithms Next: War Story
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War Story: Going Nowhere Fast Next:
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Page 305 and 306:
Problems and Reductions Next: Simpl
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Simple Reductions Next: Hamiltonian
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Hamiltonian Cycles Next: Independen
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Independent Set and Vertex Cover pr
Page 313 and 314:
Clique and Independent Set These la
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Satisfiability Mon Jun 2 23:33:50 E
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The Theory of NP-Completeness Next:
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3-Satisfiability where for , , , an
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Integer Programming Next: Vertex Co
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Integer Programming possible IP ins
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Vertex Cover reduction for the 3-SA
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Other NP-Complete Problems hard. Th
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The Art of Proving Hardness easiest
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War Story: Hard Against the Clock N
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War Story: Hard Against the Clock I
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Approximation Algorithms Next: Appr
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Approximating Vertex Cover Next: Th
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The Euclidean Traveling Salesman Ne
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The Euclidean Traveling Salesman Ne
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Exercises 1. Prove that the low deg
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Data Structures Next: Dictionaries
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Dictionaries Next: Priority Queues
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Dictionaries use a function that ma
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Dictionaries Implementation-oriente
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Priority Queues ● Besides access
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Priority Queues Fibonacci heaps [FT
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Suffix Trees and Arrays Figure: A t
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Suffix Trees and Arrays [GBY91]. Se
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Graph Data Structures algorithms).
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Graph Data Structures including the
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Set Data Structures Next: Kd-Trees
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Set Data Structures parent pointers
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Kd-Trees Next: Numerical Problems U
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Kd-Trees about p. Say we are lookin
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Numerical Problems Next: Solving Li
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Numerical Problems Mon Jun 2 23:33:
Page 377 and 378:
Solving Linear Equations algorithm
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Solving Linear Equations Matrix inv
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Bandwidth Reduction images near eac
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Matrix Multiplication Next: Determi
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Matrix Multiplication The linear al
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Determinants and Permanents Next: C
Page 389 and 390:
Determinants and Permanents exposit
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Constrained and Unconstrained Optim
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Constrained and Unconstrained Optim
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Linear Programming variable assignm
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Linear Programming The book [MW93]
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Random Number Generation Next: Fact
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Random Number Generation is largely
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Random Number Generation Related Pr
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Factoring and Primality Testing The
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Factoring and Primality Testing Alg
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Arbitrary-Precision Arithmetic If y
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Arbitrary-Precision Arithmetic PARI
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Knapsack Problem Next: Discrete Fou
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Knapsack Problem is a subset of S'
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Discrete Fourier Transform Next: Co
Page 419 and 420:
Discrete Fourier Transform an algor
Page 421 and 422:
Combinatorial Problems Next: Sortin
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Sorting Next: Searching Up: Combina
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Sorting The simplest approach to ex
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Sorting operations, implying an sor
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Searching large performance improve
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Searching Notes: Mehlhorn and Tsaka
Page 433 and 434:
Median and Selection followed by fi
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Generating Permutations Next: Gener
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Generating Permutations The rank/un
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Generating Permutations generating
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Generating Subsets look right when
Page 443 and 444:
Generating Subsets above for detail
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Generating Partitions Although the
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Generating Partitions Two related c
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Generating Graphs generate: ● Do
Page 451 and 452:
Generating Graphs Combinatorica [Sk
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Calendrical Calculations Next: Job
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Calendrical Calculations Gregorian,
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Job Scheduling ● To assign a set
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Job Scheduling shop scheduling incl
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Satisfiability logic, and automatic
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Satisfiability Next: Graph Problems
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Graph Problems: Polynomial-Time rec
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Connected Components Testing the co
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Connected Components discussing gra
Page 471 and 472:
Topological Sorting contradiction t
Page 473 and 474:
Minimum Spanning Tree Next: Shortes
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Minimum Spanning Tree help you sort
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Shortest Path Next: Transitive Clos
Page 479 and 480:
Shortest Path easier to program tha
Page 481 and 482:
Shortest Path Related Problems: Net
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Transitive Closure and Reduction
Page 485 and 486:
Transitive Closure and Reduction Al
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Matching augmenting paths and stopp
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Matching Combinatorica [Ski90] prov
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Eulerian Cycle / Chinese Postman ar
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Eulerian Cycle / Chinese Postman Ne
Page 495 and 496:
Edge and Vertex Connectivity Severa
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Edge and Vertex Connectivity Next:
Page 499 and 500:
Network Flow programming model for
Page 501 and 502:
Network Flow Combinatorica [Ski90]
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Drawing Graphs Nicely vertices are
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Drawing Graphs Nicely labs.com/orgs
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Drawing Trees such as the map of th
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Planarity Detection and Embedding N
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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
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Graph Partition annealing, is almos
Page 535 and 536:
Vertex Coloring Several special cas
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Vertex Coloring Notes: An excellent
Page 539 and 540:
Edge Coloring The minimum number of
Page 541 and 542:
Graph Isomorphism Next: Steiner Tre
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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
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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
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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
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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
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Approximate String Matching http://
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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
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Finite State Machine Minimization F
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Finite State Machine Minimization S
Page 649 and 650:
Longest Common Substring than edit
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Longest Common Substring include [A
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Shortest Common Superstring Finding
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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
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Handbook of Data Structures and Alg
Page 669 and 670:
Algorithms from P to NP Next: Compu
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Algorithms in C++ Next: Data Source
Page 673 and 674:
Textbooks Next: On-Line Resources U
Page 675 and 676:
On-Line Resources Next: Literature
Page 677 and 678:
People Next: Software Up: On-Line R
Page 679 and 680:
Professional Consulting Services Ne
Page 681 and 682:
Index A Up: Index - All Index: A ab
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Index A artists steal ASA ASCII asp
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Index B binary representation - sub
Page 687 and 688:
Index C Up: Index - All Index: C C+
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Index C clustering , , co-NP coding
Page 691 and 692:
Index C consulting services , conta
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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
Page 723 and 724:
Index N Up: Index - All Index: N na
Page 725 and 726:
Index N numerical analysis numerica
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Index O overlap graph overpasses -
Page 729 and 730:
Index P , , , , , , , , , , passwor
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Index P polygons polygon triangulat
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Index Q Up: Index - All Index: Q Qh
Page 735 and 736:
Index R ranking combinatorial objec
Page 737 and 738:
Index S Up: Index - All Index: S s-
Page 739 and 740:
Index S Shape Similarity shape simp
Page 741 and 742:
Index S solar year Solving Linear E
Page 743 and 744:
Index S straight-line graph drawing
Page 745 and 746:
Index T Up: Index - All Index: T ta
Page 747 and 748:
Index T trees - matching trees - pa
Page 749 and 750:
Index V Up: Index - All Index: V va
Page 751 and 752:
Index W Up: Index - All Index: W wa
Page 753 and 754:
Index Y Up: Index - All Index: Y Yo
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Index (complete) Up: Index - All In
Page 757 and 758:
Index (complete) arm, robot around
Page 759 and 760:
Index (complete) binary search tree
Page 761 and 762:
Index (complete) center vertex, , C
Page 763 and 764:
Index (complete) compaction compari
Page 765 and 766:
Index (complete) counting paths, co
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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
Page 775 and 776:
Index (complete) geom.bib, geometri
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Index (complete) Hamiltonian Cycle
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Index (complete) implementation cha
Page 781 and 782:
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
Page 793 and 794:
Index (complete) planar subdivision
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Index (complete) proof of correctne
Page 797 and 798:
Index (complete) regular expression
Page 799 and 800:
Index (complete) self-organizing tr
Page 801 and 802:
Index (complete) sine functions sin
Page 803 and 804:
Index (complete) spring embedding h
Page 805 and 806:
Index (complete) Symbol Technologie
Page 807 and 808:
Index (complete) trial division Tri
Page 809 and 810:
Index (complete) VLSI circuit layou
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1.4.4 Shortest Path 1.4.4 Shortest
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1.2.5 Constrained and Unconstrained
Page 815 and 816:
1.6.4 Voronoi Diagrams 1.6.4 Vorono
Page 817 and 818:
1.4.7 Eulerian Cycle / Chinese Post
Page 819 and 820:
Online Bibliographies Online Biblio
Page 821 and 822:
About the Book -- The Algorithm Des
Page 823 and 824:
Copyright and Disclaimers Copyright
Page 825 and 826:
CD-ROM Installation Installation an
Page 827 and 828:
CD-ROM Installation install a sound
Page 829 and 830:
Thanks! Frank Ruscica file:///E|/WE
Page 831 and 832:
Thanks! Zhong Li Thanks also to Fil
Page 833 and 834:
CD-ROM Installation To use the CD-R
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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 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 13 - dynamic programming ap
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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 17 - minimum spanning trees
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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 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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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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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
Page 1455 and 1456:
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
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User Comments User Comments Archive
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The Algorithm Design Manual Most pr
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The Algorithm Design Manual 5.6 War
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