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

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1.6.14 Motion Planning Go to the corresponding chapter in the book About the Book Send us Mail Go to Main Page This page last modified on Tue Jun 03, 1997 . file:///E|/WEBSITE/FILES2/MOTINING.HTM (2 of 2) [19/1/2003 1:36:45]
1.4.9 Network Flow 1.4.9 Network Flow INPUT OUTPUT Input Description: A graph G , where each edge (i,j) has a capacity c_{i,j} . A source node and sink node t . Problem: What is the maximum flow you can route from to t while respecting the capacity of each edge. Implementations ● Goldberg's Network Optimization Codes (C) (rating 10) ● DIMACS Implementation Challenges (FORTRAN) (rating 8) ● LEDA - A Library of Efficient Data Types and Algorithms (C++) (rating 5) ● Moret and Shapiro's Algorithms P to NP (Pascal) (rating 4) ● Discrete Optimization Methods (Pascal) (rating 4) ● Combinatorica (Mathematica) (rating 3) ● GraphEd -- Graph Editor and Layout Program (C) (rating 3) ● Nijenhuis and Wilf: Combinatorial Algorithms (FORTRAN) (rating 3) file:///E|/WEBSITE/FILES/NETWFLOW.HTM (1 of 2) [19/1/2003 1:36:45]
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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:
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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
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References J. M. Chambers. Partial
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References CT80 CT92 1971. G. Carpa
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References K. Daniels and V. Milenk
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References (FOCS), pages 60-69, 199
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References FM71 M. Fischer and A. M
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References History of Computing, 7:
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References N. Gibbs, W. Poole, and
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References Hof82 Hof89 Hol75 C. M.
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References IK75 Ita78 O. Ibarra and
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References Kir83 D. Kirkpatrick. Ef
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References object in 2-dimensional
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References LT79 LT80 8:99-118, 1995
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References Mil97 V. Milenkovic. Dou
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References Theoretical Computer Sci
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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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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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Correctness and Efficiency Next: Co
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Correctness NearestNeighborTSP(P) P
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Correctness Figure: A bad example f
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Efficiency Next: Expressing Algorit
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Keeping Score Next: The RAM Model o
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The RAM Model of Computation substa
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Best, Worst, and Average-Case Compl
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The Big Oh Notation Figure: Illustr
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Logarithms Next: Modeling the Probl
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Logarithms justified in ignoring th
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Modeling the Problem Figure: Modeli
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About the War Stories Next: War Sto
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War Story: Psychic Modeling Next: E
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War Story: Psychic Modeling have pu
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War Story: Psychic Modeling Next: E
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Exercises (b) If I prove that an al
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Fundamental Data Types Next: Contai
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Containers Next: Dictionaries Up: F
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Dictionaries Next: Binary Search Tr
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Binary Search Trees BinaryTreeQuery
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Priority Queues Next: Specialized D
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Specialized Data Structures Next: S
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Sorting Next: Applications of Sorti
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Applications of Sorting Figure: Con
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Data Structures Next: Incremental I
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Incremental Insertion Next: Divide
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Randomization Next: Bucketing Techn
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Randomization Next: Bucketing Techn
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Bucketing Techniques Algorithms Mon
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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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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
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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
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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
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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:
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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
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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'
Page 417 and 418:
Discrete Fourier Transform Next: Co
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Discrete Fourier Transform an algor
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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
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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
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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
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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
Page 465 and 466:
Graph Problems: Polynomial-Time rec
Page 467 and 468:
Connected Components Testing the co
Page 469 and 470:
Connected Components discussing gra
Page 471 and 472:
Topological Sorting contradiction t
Page 473 and 474:
Minimum Spanning Tree Next: Shortes
Page 475 and 476:
Minimum Spanning Tree help you sort
Page 477 and 478:
Shortest Path Next: Transitive Clos
Page 479 and 480:
Shortest Path easier to program tha
Page 481 and 482:
Shortest Path Related Problems: Net
Page 483 and 484:
Transitive Closure and Reduction
Page 485 and 486:
Transitive Closure and Reduction Al
Page 487 and 488:
Matching augmenting paths and stopp
Page 489 and 490:
Matching Combinatorica [Ski90] prov
Page 491 and 492:
Eulerian Cycle / Chinese Postman ar
Page 493 and 494:
Eulerian Cycle / Chinese Postman Ne
Page 495 and 496:
Edge and Vertex Connectivity Severa
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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
Page 505 and 506:
Drawing Graphs Nicely labs.com/orgs
Page 507 and 508:
Drawing Trees such as the map of th
Page 509 and 510:
Planarity Detection and Embedding N
Page 511 and 512:
Planarity Detection and Embedding p
Page 513 and 514:
Graph Problems: Hard Problems ● C
Page 515 and 516:
Clique approximate even to within a
Page 517 and 518:
Independent Set Next: Vertex Cover
Page 519 and 520:
Independent Set Related Problems: C
Page 521 and 522:
Vertex Cover Vertex cover and indep
Page 523 and 524:
Traveling Salesman Problem Next: Ha
Page 525 and 526:
Traveling Salesman Problem practice
Page 527 and 528:
Traveling Salesman Problem Size is
Page 529 and 530:
Hamiltonian Cycle Eulerian cycle, p
Page 531 and 532:
Graph Partition Next: Vertex Colori
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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
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Edge Coloring The minimum number of
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Graph Isomorphism Next: Steiner Tre
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Graph Isomorphism vertices into equ
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Steiner Tree Next: Feedback Edge/Ve
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Steiner Tree The worst case for a m
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Feedback Edge/Vertex Set Next: Comp
Page 551 and 552:
Feedback Edge/Vertex Set An interes
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Computational Geometry complete bib
Page 555 and 556:
Robust Geometric Primitives Next: C
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Robust Geometric Primitives ● Are
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Robust Geometric Primitives Related
Page 561 and 562:
Convex Hull ● How many dimensions
Page 563 and 564:
Convex Hull http://www.cs.att.com/n
Page 565 and 566:
Triangulation Next: Voronoi Diagram
Page 567 and 568:
Triangulation GEOMPACK is a suite o
Page 569 and 570:
Voronoi Diagrams Next: Nearest Neig
Page 571 and 572:
Voronoi Diagrams McDonald's, the ti
Page 573 and 574:
Nearest Neighbor Search Next: Range
Page 575 and 576:
Nearest Neighbor Search Implementat
Page 577 and 578:
Range Search Next: Point Location U
Page 579 and 580:
Range Search subdivisions in C++. I
Page 581 and 582:
Point Location the n edges for inte
Page 583 and 584:
Point Location More recently, there
Page 585 and 586:
Intersection Detection ● Do you w
Page 587 and 588:
Intersection Detection Implementati
Page 589 and 590:
Bin Packing Next: Medial-Axis Trans
Page 591 and 592:
Bin Packing approach for general sh
Page 593 and 594:
Medial-Axis Transformation Next: Po
Page 595 and 596:
Medial-Axis Transformation Implemen
Page 597 and 598:
Polygon Partitioning number of piec
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Simplifying Polygons Next: Shape Si
Page 601 and 602:
Simplifying Polygons vertices and o
Page 603 and 604:
Shape Similarity Next: Motion Plann
Page 605 and 606:
Shape Similarity or how close it is
Page 607 and 608:
Motion Planning There is a wide ran
Page 609 and 610:
Motion Planning often arise in the
Page 611 and 612:
Maintaining Line Arrangements Think
Page 613 and 614:
Maintaining Line Arrangements Next:
Page 615 and 616:
Minkowski Sum where x+y is the vect
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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
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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
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String Matching and texts, I recomm
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Approximate String Matching This sa
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Approximate String Matching http://
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Text Compression Next: Cryptography
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Text Compression code string. ASCII
Page 639 and 640:
Cryptography Next: Finite State Mac
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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
Page 655 and 656:
Software systems Next: LEDA Up: Alg
Page 657 and 658:
LEDA Next: Netlib Up: Software syst
Page 659 and 660:
Netlib Algorithms Mon Jun 2 23:33:5
Page 661 and 662:
The Stanford GraphBase Next: Combin
Page 663 and 664:
Algorithm Animations with XTango Ne
Page 665 and 666:
Programs from Books Next: Discrete
Page 667 and 668:
Handbook of Data Structures and Alg
Page 669 and 670:
Algorithms from P to NP Next: Compu
Page 671 and 672:
Algorithms in C++ Next: Data Source
Page 673 and 674:
Textbooks Next: On-Line Resources U
Page 675 and 676:
On-Line Resources Next: Literature
Page 677 and 678:
People Next: Software Up: On-Line R
Page 679 and 680:
Professional Consulting Services Ne
Page 681 and 682:
Index A Up: Index - All Index: A ab
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Index A artists steal ASA ASCII asp
Page 685 and 686:
Index B binary representation - sub
Page 687 and 688:
Index C Up: Index - All Index: C C+
Page 689 and 690:
Index C clustering , , co-NP coding
Page 691 and 692:
Index C consulting services , conta
Page 693 and 694:
Index D Up: Index - All Index: D DA
Page 695 and 696:
Index D Dictionaries dictionaries -
Page 697 and 698:
Index D dynamic programming - appli
Page 699 and 700:
Index E empirical results - heurist
Page 701 and 702:
Index F Up: Index - All Index: F fa
Page 703 and 704:
Index F frequency domain friend-or-
Page 705 and 706:
Index G geometric shortest path , g
Page 707 and 708:
Index H Up: Index - All Index: H ha
Page 709 and 710:
Index H Algorithms Tue Jun 3 11:59:
Page 711 and 712:
Index I independent set - alternate
Page 713 and 714:
Index J Up: Index - All Index: J ji
Page 715 and 716:
Index K Algorithms Tue Jun 3 11:59:
Page 717 and 718:
Index L linear congruential generat
Page 719 and 720:
Index M Up: Index - All Index: M ma
Page 721 and 722:
Index M mindset minima minimax sear
Page 723 and 724:
Index N Up: Index - All Index: N na
Page 725 and 726:
Index N numerical analysis numerica
Page 727 and 728:
Index O overlap graph overpasses -
Page 729 and 730:
Index P , , , , , , , , , , passwor
Page 731 and 732:
Index P polygons polygon triangulat
Page 733 and 734:
Index Q Up: Index - All Index: Q Qh
Page 735 and 736:
Index R ranking combinatorial objec
Page 737 and 738:
Index S Up: Index - All Index: S s-
Page 739 and 740:
Index S Shape Similarity shape simp
Page 741 and 742:
Index S solar year Solving Linear E
Page 743 and 744:
Index S straight-line graph drawing
Page 745 and 746:
Index T Up: Index - All Index: T ta
Page 747 and 748:
Index T trees - matching trees - pa
Page 749 and 750:
Index V Up: Index - All Index: V va
Page 751 and 752:
Index W Up: Index - All Index: W wa
Page 753 and 754:
Index Y Up: Index - All Index: Y Yo
Page 755 and 756:
Index (complete) Up: Index - All In
Page 757 and 758:
Index (complete) arm, robot around
Page 759 and 760:
Index (complete) binary search tree
Page 761 and 762:
Index (complete) center vertex, , C
Page 763 and 764:
Index (complete) compaction compari
Page 765 and 766:
Index (complete) counting paths, co
Page 767 and 768:
Index (complete) deletion from bina
Page 769 and 770:
Index (complete) dominance ordering
Page 771 and 772:
Index (complete) English to French
Page 773 and 774:
Index (complete) file directory tre
Page 775 and 776:
Index (complete) geom.bib, geometri
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Index (complete) Hamiltonian Cycle
Page 779 and 780:
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
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
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Index (complete) spring embedding h
Page 805 and 806:
Index (complete) Symbol Technologie
Page 807 and 808:
Index (complete) trial division Tri
Page 809 and 810:
Index (complete) VLSI circuit layou
Page 811 and 812:
1.4.4 Shortest Path 1.4.4 Shortest
Page 813 and 814:
1.2.5 Constrained and Unconstrained
Page 815 and 816:
1.6.4 Voronoi Diagrams 1.6.4 Vorono
Page 817 and 818:
1.4.7 Eulerian Cycle / Chinese Post
Page 819 and 820:
Online Bibliographies Online Biblio
Page 821 and 822:
About the Book -- The Algorithm Des
Page 823 and 824:
Copyright and Disclaimers Copyright
Page 825 and 826:
CD-ROM Installation Installation an
Page 827 and 828:
CD-ROM Installation install a sound
Page 829 and 830:
Thanks! Frank Ruscica file:///E|/WE
Page 831 and 832:
Thanks! Zhong Li Thanks also to Fil
Page 833 and 834:
CD-ROM Installation To use the CD-R
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Binary Search in Action Binary Sear
Page 837 and 838:
Binary Search in Action file:///E|/
Page 839 and 840:
Binary Search in Action file:///E|/
Page 841 and 842:
Postscript version of the lecture n
Page 843 and 844:
Lecture 1 - analyzing algorithms Ne
Page 845 and 846:
Lecture 1 - analyzing algorithms Yo
Page 847 and 848:
Lecture 1 - analyzing algorithms Th
Page 849 and 850:
Lecture 1 - analyzing algorithms Th
Page 851 and 852:
Lecture 1 - analyzing algorithms is
Page 853 and 854:
Lecture 2 - asymptotic notation How
Page 855 and 856:
Lecture 2 - asymptotic notation Not
Page 857 and 858:
Lecture 2 - asymptotic notation alg
Page 859 and 860:
Lecture 2 - asymptotic notation Sup
Page 861 and 862:
Lecture 2 - asymptotic notation Nex
Page 863 and 864:
Lecture 3 - recurrence relations Is
Page 865 and 866:
Lecture 3 - recurrence relations
Page 867 and 868:
Lecture 3 - recurrence relations Li
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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 12 - introduction to dynami
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Lecture 13 - dynamic programming ap
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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
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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1.6.2 Convex Hull Related Problems
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Visual Links Index file:///E|/WEBSI
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Page 1063 and 1064:
Page 1065 and 1066:
Page 1067 and 1068:
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Page 1073 and 1074: 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
Page 1091 and 1092: C++ Language Implementations Algori
Page 1093 and 1094: C Language Implementations ● POSI
Page 1095 and 1096: FORTRAN Language Implementations Al
Page 1097 and 1098: Lisp Language Implementations Algor
Page 1099 and 1100: Algorithm Repository -- Citations C
Page 1101 and 1102: Practical Algorithm Design -- User
Page 1103 and 1104: LEDA - A Library of Efficient Data
Page 1105 and 1106: Discrete Optimization Methods Discr
Page 1107 and 1108: Netlib / TOMS -- Collected Algorith
Page 1109 and 1110: Netlib / TOMS -- Collected Algorith
Page 1111 and 1112: Xtango and Polka Algorithm Animatio
Page 1113 and 1114: Combinatorica Combinatorica Combina
Page 1115 and 1116: file:///E|/WEBSITE/IMPLEMEN/GRAPHBA
Page 1117 and 1118: 1.4.1 Connected Components 1.4.1 Co
Page 1119 and 1120: 1.5.9 Graph Isomorphism 1.5.9 Graph
Page 1121 and 1122: 1.2.3 Matrix Multiplication 1.2.3 M
Page 1123: 1.6.14 Motion Planning 1.6.14 Motio
Page 1127 and 1128: 1.1.2 Priority Queues 1.1.2 Priorit
Page 1129 and 1130: 1.5.10 Steiner Tree 1.5.10 Steiner
Page 1131 and 1132: 1.4.5 Transitive Closure and Reduct
Page 1133 and 1134: About the Book -- The Algorithm Des
Page 1135 and 1136: About the Book -- The Algorithm Des
Page 1137 and 1138: Genocop -- Optimization via Genetic
Page 1139 and 1140: 1.2.6 Linear Programming ● Knapsa
Page 1141 and 1142: 1.2.7 Random Number Generation ●
Page 1143 and 1144: 1.3.10 Satisfiability ● Constrain
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Page 1147 and 1148: 1.6.5 Nearest Neighbor Search 1.6.5
Page 1149 and 1150: 1.6.7 Point Location 1.6.7 Point Lo
Page 1151 and 1152: 1.6.10 Medial-Axis Transformation 1
Page 1153 and 1154: 1.6.3 Triangulation 1.6.3 Triangula
Page 1155 and 1156: Nijenhuis and Wilf: Combinatorial A
Page 1157 and 1158: 1.5.5 Hamiltonian Cycle 1.5.5 Hamil
Page 1159 and 1160: 1.4.6 Matching 1.4.6 Matching INPUT
Page 1161 and 1162: A compendium of NP optimization pro
Page 1163 and 1164: 1.6.9 Bin Packing 1.6.9 Bin Packing
Page 1165 and 1166: 1.6.12 Simplifying Polygons 1.6.12
Page 1167 and 1168: 1.6.13 Shape Similarity 1.6.13 Shap
Page 1169 and 1170: 1.6.11 Polygon Partitioning 1.6.11
Page 1171 and 1172: 1.4.3 Minimum Spanning Tree 1.4.3 M
Page 1173 and 1174: 1.6.15 Maintaining Line Arrangement
Page 1175 and 1176:
1.3.7 Generating Graphs 1.3.7 Gener
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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
Page 1185 and 1186:
Plugins for use with the CDROM Plug
Page 1187 and 1188:
Implementation Challenges Next: Dat
Page 1189 and 1190:
Implementation Challenges Next: Gra
Page 1191 and 1192:
Implementation Challenges Next: Int
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Caveats Next: Data Structures Up: A
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1.1.1 Dictionaries ● Priority Que
Page 1197 and 1198:
1.1.4 Graph Data Structures ● Gra
Page 1199 and 1200:
1.1.5 Set Data Structures ● Gener
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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
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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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
Page 1229 and 1230:
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
Page 1237 and 1238:
1.5.3 Vertex Cover ● Independent
Page 1239 and 1240:
1.5.4 Traveling Salesman Problem
Page 1241 and 1242:
1.5.6 Graph Partition ● Network F
Page 1243 and 1244:
1.5.7 Vertex Coloring Related Probl
Page 1245 and 1246:
1.5.11 Feedback Edge/Vertex Set Go
Page 1247 and 1248:
1.6.1 Robust Geometric Primitives G
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1.6.6 Range Search ● Kd-Trees ●
Page 1251 and 1252:
1.6.8 Intersection Detection ● Ro
Page 1253 and 1254:
1.6.16 Minkowski Sum Go to the corr
Page 1255 and 1256:
1.7.1 Set Cover ● Set Packing ●
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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
Page 1267 and 1268:
1.7.7 Finite State Machine Minimiza
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1.7.8 Longest Common Substring ●
Page 1271 and 1272:
1.7.9 Shortest Common Superstring G
Page 1273 and 1274:
Handbook of Algorithms and Data Str
Page 1275 and 1276:
Moret and Shapiro's Algorithms P to
Page 1277 and 1278:
Index B Up: Index - All Index: B ba
Page 1279 and 1280:
Index C Up: Index - All Index: C ch
Page 1281 and 1282:
Index E Up: Index - All Index: E ed
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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
Page 1305 and 1306:
Index (complete) parenthesization p
Page 1307 and 1308:
Index (complete) symmetry, exploiti
Page 1309 and 1310:
Ranger - Nearest Neighbor Search in
Page 1311 and 1312:
DIMACS Implementation Challenges Pe
Page 1313 and 1314:
Stony Brook Project Implementations
Page 1315 and 1316:
Clarkson's higher dimensional conve
Page 1317 and 1318:
file:///E|/WEBSITE/BIBLIO/TESTDATA/
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SimPack/Sim++ Simulation Toolkit Si
Page 1449 and 1450:
Fire-Engine and Spare-Parts String
Page 1451 and 1452:
Geolab -- Computational Geometry Sy
Page 1453 and 1454:
Calendrical Calculations Calendrica
Page 1455 and 1456:
David Eppstein's Knuth-Morris-Pratt
Page 1457 and 1458:
Mike Trick's Graph Coloring Resourc
Page 1459 and 1460:
Frank Ruskey's Combinatorial Genera
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Arrange - maintainance of arrangeme
Page 1463 and 1464:
LP_SOLVE: Linear Programming Code L
Page 1465 and 1466:
PARI - Package for Number Theory Se
Page 1467 and 1468:
TSP solvers TSP solvers tsp-solve i
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PHYLIP -- inferring phylogenic tree
Page 1471 and 1472:
Skeletonization Software (2-D) Skel
Page 1473 and 1474:
agrep - Approximate General Regular
Page 1475 and 1476:
CAP -- Contig Assembly Program CAP
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NAUTY -- Graph Isomorphism NAUTY --
Page 1479 and 1480:
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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