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

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Exercises positions, while has been shifted k=4 positions. ❍ Suppose you know what k is. Give an O(1) algorithm to find the largest number in A. ❍ Suppose you do not know what k is. Give an algorithm to find the largest number in A. For partial credit, you may give an O(n) algorithm. 12. (*) Suppose that you are given a sorted sequence of distinct integers . Give an algorithm to determine whether there exists an index i such at . For example, in , . In , there is no such i. Next: Implementation Challenges Up: Breaking Problems Down Previous: Square and Other Roots Algorithms Mon Jun 2 23:33:50 EDT 1997 file:///E|/BOOK/BOOK2/NODE57.HTM (4 of 4) [19/1/2003 1:29:01]
The Friendship Graph Next: Data Structures for Graphs Up: Graph Algorithms Previous: Graph Algorithms The Friendship Graph Figure: A portion of the friendship graph To demonstrate the importance of proper modeling, let us consider a graph where the vertices are people, and there is an edge between two people if and only if they are friends. This graph is well-defined on any set of people, be they the people in your neighborhood, at your school or place of business, or even the entire world. There are many interesting aspects of people that are best understood as properties of this friendship graph. We use this opportunity to define important graph theory terminology. ``Talking the talk'' proves to be an important part of ``walking the walk''. ● If I am your friend, does that mean you are my friend? A graph is undirected if edge (x,y) always implies (y,x). Otherwise, the graph is said to be directed. The ``heard-of'' graph is directed, since many famous people who I have heard of have never heard of me! The ``had-sex-with'' graph is presumably undirected, since the critical operation always requires a partner. I'd like to think that the ``friendship'' graph is always an undirected graph. ● Am I my own friend? An edge of the form (x,x) is said to be a loop. If x was y's friend several times over, we can model this relationship using multiedges, multiple edges between the same pair of vertices. A graph is said to be simple if it contains no loops and no multiple edges. Simple graphs really are often simpler to work with in practice. Therefore, we might be better off if no one was their own friend. ● How close a friend are you? A graph is said to be weighted if each edge has an associated numerical attribute. We could model file:///E|/BOOK/BOOK2/NODE60.HTM (1 of 3) [19/1/2003 1:29:02]
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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
Page 167 and 168: War Story: Mystery of the Pyramids
Page 169 and 170: War Story: String 'em Up We were co
Page 171 and 172: War Story: String 'em Up Figure: Su
Page 173 and 174: Exercises Next: Implementation Chal
Page 175 and 176: Exercises used to select the pivot.
Page 177 and 178: Dynamic Programming Next: Fibonacci
Page 179 and 180: Fibonacci numbers Next: The Partiti
Page 181 and 182: Fibonacci numbers Next: The Partiti
Page 183 and 184: The Partition Problem . What is the
Page 185 and 186: The Partition Problem Figure: Dynam
Page 187 and 188: Approximate String Matching Next: L
Page 189 and 190: Approximate String Matching The val
Page 191 and 192: Longest Increasing Sequence Will th
Page 193 and 194: Minimum Weight Triangulation Next:
Page 195 and 196: Limitations of Dynamic Programming
Page 197 and 198: War Story: Evolution of the Lobster
Page 199 and 200: War Story: Evolution of the Lobster
Page 201 and 202: War Story: What's Past is Prolog Ne
Page 203 and 204: War Story: What's Past is Prolog th
Page 205 and 206: War Story: Text Compression for Bar
Page 207 and 208: War Story: Text Compression for Bar
Page 209 and 210: Divide and Conquer Next: Fast Expon
Page 211 and 212: Fast Exponentiation Next: Binary Se
Page 213 and 214: Binary Search Next: Square and Othe
Page 217: Exercises whose denominations are ,
Page 221 and 222: The Friendship Graph friendship gra
Page 223 and 224: Data Structures for Graphs linked t
Page 225 and 226: War Story: Getting the Graph ``Well
Page 227 and 228: Traversing a Graph Next: Breadth-Fi
Page 229 and 230: Breadth-First Search Next: Depth-Fi
Page 231 and 232: Depth-First Search Next: Applicatio
Page 233 and 234: Depth-First Search Next: Applicatio
Page 235 and 236: Connected Components Next: Tree and
Page 237 and 238: Two-Coloring Graphs Next: Topologic
Page 239 and 240: Topological Sorting Next: Articulat
Page 241 and 242: Modeling Graph Problems Next: Minim
Page 243 and 244: Modeling Graph Problems good line s
Page 245 and 246: Minimum Spanning Trees ● Prim's A
Page 247 and 248: Prim's Algorithm inserted edge (x,y
Page 249 and 250: Kruskal's Algorithm a tree of weigh
Page 251 and 252: Dijkstra's Algorithm Next: All-Pair
Page 253 and 254: All-Pairs Shortest Path Next: War S
Page 255 and 256: War Story: Nothing but Nets Next: W
Page 257 and 258: War Story: Nothing but Nets ``You a
Page 259 and 260: War Story: Dialing for Documents Ne
Page 261 and 262: War Story: Dialing for Documents If
Page 263 and 264: War Story: Dialing for Documents CO
Page 267 and 268: 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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Exercises Next: Implementation Chal
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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'
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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
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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
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Topological Sorting contradiction t
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Minimum Spanning Tree Next: Shortes
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Minimum Spanning Tree help you sort
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Shortest Path Next: Transitive Clos
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Shortest Path easier to program tha
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Shortest Path Related Problems: Net
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Transitive Closure and Reduction
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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
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Edge and Vertex Connectivity Severa
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Edge and Vertex Connectivity Next:
Page 499 and 500:
Network Flow programming model for
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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
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Graph Problems: Hard Problems ● C
Page 515 and 516:
Clique approximate even to within a
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Independent Set Next: Vertex Cover
Page 519 and 520:
Independent Set Related Problems: C
Page 521 and 522:
Vertex Cover Vertex cover and indep
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Traveling Salesman Problem Next: Ha
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Traveling Salesman Problem practice
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Traveling Salesman Problem Size is
Page 529 and 530:
Hamiltonian Cycle Eulerian cycle, p
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Graph Partition Next: Vertex Colori
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Graph Partition annealing, is almos
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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
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Feedback Edge/Vertex Set An interes
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Computational Geometry complete bib
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Robust Geometric Primitives Next: C
Page 557 and 558:
Robust Geometric Primitives ● Are
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Robust Geometric Primitives Related
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Convex Hull ● How many dimensions
Page 563 and 564:
Convex Hull http://www.cs.att.com/n
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Triangulation Next: Voronoi Diagram
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Triangulation GEOMPACK is a suite o
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Voronoi Diagrams Next: Nearest Neig
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Voronoi Diagrams McDonald's, the ti
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Nearest Neighbor Search Next: Range
Page 575 and 576:
Nearest Neighbor Search Implementat
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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
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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
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Polygon Partitioning number of piec
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Simplifying Polygons Next: Shape Si
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Simplifying Polygons vertices and o
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Shape Similarity Next: Motion Plann
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Shape Similarity or how close it is
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Motion Planning There is a wide ran
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Motion Planning often arise in the
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Maintaining Line Arrangements Think
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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
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Set Cover Next: Set Packing Up: Set
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Set Cover Figure: Hitting set is du
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Set Packing Next: String Matching U
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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
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Cryptography Next: Finite State Mac
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Cryptography ● How can I validate
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Cryptography MD5 [Riv92] is the sec
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Finite State Machine Minimization F
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Finite State Machine Minimization S
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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
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LEDA Next: Netlib Up: Software syst
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Netlib Algorithms Mon Jun 2 23:33:5
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The Stanford GraphBase Next: Combin
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Algorithm Animations with XTango Ne
Page 665 and 666:
Programs from Books Next: Discrete
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Handbook of Data Structures and Alg
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Algorithms from P to NP Next: Compu
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Algorithms in C++ Next: Data Source
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Textbooks Next: On-Line Resources U
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On-Line Resources Next: Literature
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People Next: Software Up: On-Line R
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Professional Consulting Services Ne
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Index A Up: Index - All Index: A ab
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Index A artists steal ASA ASCII asp
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Index B binary representation - sub
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Index C Up: Index - All Index: C C+
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Index C clustering , , co-NP coding
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Index C consulting services , conta
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Index D Up: Index - All Index: D DA
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Index D Dictionaries dictionaries -
Page 697 and 698:
Index D dynamic programming - appli
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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-
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Index G geometric shortest path , g
Page 707 and 708:
Index H Up: Index - All Index: H ha
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Index H Algorithms Tue Jun 3 11:59:
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Index I independent set - alternate
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Index J Up: Index - All Index: J ji
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Index K Algorithms Tue Jun 3 11:59:
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Index L linear congruential generat
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Index M Up: Index - All Index: M ma
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Index M mindset minima minimax sear
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Index N Up: Index - All Index: N na
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Index N numerical analysis numerica
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Index O overlap graph overpasses -
Page 729 and 730:
Index P , , , , , , , , , , passwor
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Index P polygons polygon triangulat
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Index Q Up: Index - All Index: Q Qh
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Index R ranking combinatorial objec
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Index S Up: Index - All Index: S s-
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Index S Shape Similarity shape simp
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Index S solar year Solving Linear E
Page 743 and 744:
Index S straight-line graph drawing
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Index T Up: Index - All Index: T ta
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Index T trees - matching trees - pa
Page 749 and 750:
Index V Up: Index - All Index: V va
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Index W Up: Index - All Index: W wa
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Index Y Up: Index - All Index: Y Yo
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Index (complete) Up: Index - All In
Page 757 and 758:
Index (complete) arm, robot around
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Index (complete) binary search tree
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Index (complete) center vertex, , C
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Index (complete) compaction compari
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Index (complete) counting paths, co
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Index (complete) deletion from bina
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Index (complete) dominance ordering
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Index (complete) English to French
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Index (complete) file directory tre
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Index (complete) geom.bib, geometri
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Index (complete) Hamiltonian Cycle
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Index (complete) implementation cha
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Index (complete) job-shop schedulin
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Index (complete) linear programming
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Index (complete) , , , , , , , math
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Index (complete) modular arithmetic
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Index (complete) normal distributio
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Index (complete) parallel algorithm
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Index (complete) planar subdivision
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Index (complete) proof of correctne
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Index (complete) regular expression
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Index (complete) self-organizing tr
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Index (complete) sine functions sin
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Index (complete) spring embedding h
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Index (complete) Symbol Technologie
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Index (complete) trial division Tri
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Index (complete) VLSI circuit layou
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1.4.4 Shortest Path 1.4.4 Shortest
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1.2.5 Constrained and Unconstrained
Page 815 and 816:
1.6.4 Voronoi Diagrams 1.6.4 Vorono
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1.4.7 Eulerian Cycle / Chinese Post
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Online Bibliographies Online Biblio
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
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CD-ROM Installation To use the CD-R
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Binary Search in Action Binary Sear
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Binary Search in Action file:///E|/
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Binary Search in Action file:///E|/
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Postscript version of the lecture n
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Lecture 1 - analyzing algorithms Ne
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Lecture 1 - analyzing algorithms Yo
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Lecture 1 - analyzing algorithms Th
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Lecture 1 - analyzing algorithms Th
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Lecture 1 - analyzing algorithms is
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Lecture 2 - asymptotic notation How
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Lecture 2 - asymptotic notation Not
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Lecture 2 - asymptotic notation alg
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Lecture 2 - asymptotic notation Sup
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Lecture 2 - asymptotic notation Nex
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Lecture 3 - recurrence relations Is
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Lecture 3 - recurrence relations
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Lecture 3 - recurrence relations Li
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Lecture 3 - recurrence relations Su
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Lecture 3 - recurrence relations A
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Lecture 4 - heapsort Note iteration
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Lecture 4 - heapsort The convex hul
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Lecture 4 - heapsort 1. All leaves
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Lecture 4 - heapsort left = 2i righ
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Lecture 4 - heapsort Since this sum
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Lecture 4 - heapsort Selection sort
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Lecture 4 - heapsort Greedy Algorit
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Lecture 5 - quicksort Partitioning
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Lecture 5 - quicksort Listen To Par
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Lecture 5 - quicksort Listen To Par
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Lecture 5 - quicksort The worst cas
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Lecture 5 - quicksort Mon Jun 2 09:
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Lecture 6 - linear sorting Since 2i
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Lecture 6 - linear sorting With uni
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Lecture 6 - linear sorting Listen T
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Lecture 7 - elementary data structu
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Lecture 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
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
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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 ●
Page 1271 and 1272:
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
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
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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
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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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