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1.

図書
図書
1. Hyperplane arrangements : an introduction
Alexandru Dimca
出版情報: Cham : Springer, c2017  xii, 200 p. ; 24 cm
シリーズ名: Universitext
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2.

図書
図書
2. Splitting algorithms, modern operator theory, and applications
Heinz H. Bauschke, Regina S. Burachik, D. Russell Luke, editors
出版情報: Cham : Springer, c2019  xix, 489 p. ; 25 cm
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3.

図書
図書
3. Selected works III : information theory and the theory of algorithms (: softcover)
Andrei N. Kolmogorov ; editer, Albert N. Shiryaev
出版情報: Dordrecht : Springer, c2019  xxvii, 275 p. ; 24 cm
シリーズ名: Springer Collected Works in Mathematics
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4.

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図書
4. Algorithmic puzzles (: pbk)
Anany Levitin and Maria Levitin
出版情報: New York : Oxford University Press, c2011  xxi, 257 p. ; 24 cm
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目次情報: 続きを見る
Preface
Acknowledgments
List of Puzzles
Tutorial Puzzles
Main Section Puzzles
The Epigraph Puzzle: Who said what?
Tutorials / 1：
General Strategies for Algorithm Design
Analysis Techniques
Puzzles / 2：
Easier Puzzles (#1 to #50)
Puzzles of Medium Difficulty (#51 to #110)
Harder Puzzles (#111 to #150)
Hints / 3：
Solutions / 4：
References
Design Strategy and Analysis Index
Index of Terms and Names
Preface
Acknowledgments
List of Puzzles
5.

図書
図書
5. Algorithms, graphs, and computers
Richard Bellman, Kenneth L. Cooke, Jo Ann Lockett
出版情報: New York : Academic Press, 1970  xv, 246 p. ; 24 cm
シリーズ名: Mathematics in science and engineering : a series of monographs and textbooks ; v. 62
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6.

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図書
6. The Jacobi-Perron algorithm : its theory and application (: Germany ; : U.S.)
Leon Bernstein
出版情報: Berlin ; New York : Springer-Verlag, 1971  iv, 160 p ; 26 cm
シリーズ名: Lecture notes in mathematics ; 207
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7.

図書
図書
7. An introduction to the general theory of algorithms (: pbk)
Michael Machtey, Paul Young
出版情報: New York : North-Holland, c1978  vii, 264 p. ; 24 cm
シリーズ名: Theory of computation series
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8.

図書
図書
8. Graph algorithms (: us ; : uk ; : gw)
Shimon Even
出版情報: Potomac, Md. : Computer Science Press, c1979  ix, 249 p. ; 24 cm
シリーズ名: Computer software engineering series
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9.

図書
図書
9. Combinatorial algorithms
Albert Nijenhuis and Herbert S. Wilf
出版情報: New York : Academic Press, 1975  xiv, 253 p. ; 24 cm
シリーズ名: Computer science and applied mathematics
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10.

図書
図書
10. P-functions and Boolean matrix factorization : a unified approach for wired, programmed and microprogrammed implementations of discrete algorithms (: gw ; : us)
André Thayse
出版情報: Berlin ; New York ; Tokyo : Springer-Verlag, 1984  vii, 248 p. ; 25 cm
シリーズ名: Lecture notes in computer science ; 175
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11.

図書
図書
11. Algorithms for approximation : based on the proceedings of the IMA Conference on Algorithms for the Approximation of Functions and Data held at the Royal Military College of Science, Shrivenham, July 1985
edited by J.C. Mason and M.G. Cox
出版情報: Oxford : Clarendon Press, 1987  xvi, 694 p. ; 25 cm
シリーズ名: The Institute of Mathematics and its Applications conference series ; New series ; 10
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目次情報: 続きを見る
Development of Algorithms / Part 1：
Spline Approximation and Smoothing / 1：
Spline Interpolation and Shape Preservation / 2：
Multivariate Interpolation / 3：
Least Square Methods / 4：
Rational Approximation / 5：
Complex and Nonlinear Approximation / 6：
Computer-Aided Design and Blending / 7：
Applications / Part 2：
Applications in Numerical Analysis / 8：
Applications in Partial Differential Equations / 9：
Applications in Other Disciplines / 10：
Software / Part 3：
Development of Algorithms / Part 1：
Spline Approximation and Smoothing / 1：
Spline Interpolation and Shape Preservation / 2：
12.

図書
図書
12. Graphs, networks, and algorithms
M.N.S. Swamy, K. Thulasiraman
出版情報: New York : Wiley, c1981  xviii, 592 p. ; 24 cm
シリーズ名: A Wiley-Interscience publication
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13.

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図書
13. Stable recursions : with applications to the numerical solution of stiff systems
J. R. Cash
出版情報: London ; New York : Academic Press, 1979  xii, 223 p. ; 24 cm
シリーズ名: Computational mathematics and applications
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14.

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図書
14. Algorithms for games (: us ; : gw)
G.M. Adelson-Velsky, V.L. Arlazarov, M.V. Donskoy ; [translator, Arthur Brown]
出版情報: New York ; Berlin ; Tokyo : Springer, c1988  x, 197 p. ; 25 cm
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15.

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図書
15. Computer semantics : studies of algorithms, processors, and languages
John A.N. Lee
出版情報: New York : Van Nostrand Reinhold, c1972  xvi, 397 p. ; 24 cm
シリーズ名: Computer science series
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16.

図書
図書
16. Algorithms and applications on vector and parallel computers
edited by H.J.J. te Riele, Th.J. Dekker, H.A. van der Vorst
出版情報: Amsterdam ; Tokyo : North-Holland, 1987  xi, 457 p. ; 25 cm
シリーズ名: Special topics in supercomputing ; v. 3
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17.

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図書
17. Nonlinear programming : sequential unconstrained minimization techniques
[by] Anthony V. Fiacco [and] Garth P. McCormick
出版情報: New York : Wiley, c1968  xiv, 210 p. ; 23 cm
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18.

図書
図書
18. Introduction to the design and analysis of algorithms
S.E. Goodman, S.T. Hedetniemi
出版情報: New York : McGraw-Hill, c1977  xi, 371 p. ; 21 cm
シリーズ名: McGraw-Hill computer science series
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19.

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図書
19. Algorithms+data structures=programs
Niklaus Wirth
出版情報: Englewood Cliffs, N.J. : Prentice-Hall, c1976  xvii, 366 p. ; 24 cm
シリーズ名: Prentice-Hall series in automatic computation
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20.

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図書
20. Optimization algorithms for networks and graphs
Edward Minieka
出版情報: New York : M. Dekker, c1978  viii, 356 p. ; 24 cm
シリーズ名: Industrial engineering ; v. 1
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21.

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図書
21. Combinatorial algorithms for computers and calculators. 2d ed
Albert Nijenhuis and Herbert S. Wilf
出版情報: New York : Academic Press, 1978  xv, 302 p. ; 24 cm
シリーズ名: Computer science and applied mathematics
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22.

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図書
22. Algorithms : their complexity and efficiency. 2nd ed.
Lydia Kronsjö
出版情報: Chichester ; New York : Wiley, c1987  xiii, 363 p. ; 24 cm
シリーズ名: Wiley series in computing
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23.

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図書
23. The characteristics of parallel algorithms
edited by Leah H. Jamieson, Dennis Gannon, Robert J. Douglass
出版情報: Cambridge, Mass. : MIT Press, c1987  ix, 440 p. ; 24 cm
シリーズ名: MIT Press series in scientific computation
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24.

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図書
24. Signal processing algorithms
Samuel D. Stearns, Ruth A. David
出版情報: Englewood Cliffs, N.J. : Prentice-Hall, c1988  xii, 349 p. ; 24 cm.
シリーズ名: Prentice Hall signal processing series
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25.

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図書
25. Operating systems theory
Edward G. Coffman, Jr., Peter J. Denning
出版情報: Englewood Cliffs, N.J. : Prentice-Hall, c1973  xvi, 331 p. ; 24 cm
シリーズ名: Prentice-Hall series in automatic computation
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26.

図書
図書
26. Enumerability, decidability, computability : an introduction to the theory of recursive functions. 2nd rev. ed
Hans Hermes ; translated by G.T. Hermann and O. Plassmann
出版情報: Berlin ; New York : Springer, 1969  x, 245 p. ; 24 cm
シリーズ名: Die Grundlehren der mathematischen Wissenschaften ; Bd. 127
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27.

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図書
27. Digital signal processing algorithms : number theory, convolution, fast Fourier transforms, and applications
Hari Krishna Garg
出版情報: Boca Raton, FL : CRC Press, c1998  637 p. ; 25 cm
シリーズ名: CRC Press computer engineering series
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目次情報: 続きを見る
Introduction
Computational Number Theory
Polynomial Algebra
Theoretical Aspects of Discrete Fourier Transform and Convolution
Cyclotomic Polynomial Factorization and Associated Fields
Cyclotomic Polynomial Factorization Over Finite Fields
Finite Integer Rings: Polynomial Algebra and Cyclotomic Factorization
Fast Algorithms For Acyclic Convolution of Discrete Sequences
Fast Algorithms for Cyclic Convolution
Discrete Fourier Transforms
A Coding Theory Framework for Error (NTI
Introduction
Computational Number Theory
Polynomial Algebra
28.

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28. Scientific visualization : overviews, methodologies, and techniques
Gregory M. Nielson, Hans Hagen, Heinrich Müller
出版情報: Los Alamitos, Calif. : IEEE Computer Society Press, 1997  xiii, 577 p. ; 26 cm
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29.

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図書
29. Algorithmic foundations of geographic information systems
Marc van Kreveld ... [et al.] (eds.)
出版情報: Berlin ; New York : Springer, c1997  xiv, 287 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 1340
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30.

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図書
30. Optimization : algorithms and consistent approximations
Elijah Polak
出版情報: New York : Springer-Verlag, c1997  xx, 779 p. ; 25 cm
シリーズ名: Applied mathematical sciences ; v. 124
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31.

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図書
31. Analytical techniques of celestial mechanics
Victor A. Brumberg
出版情報: Berlin ; New York : Springer-Verlag, c1995  viii, 236 p. ; 25 cm
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32.

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図書
32. Algorithmic probability : a collection of problems. 1st ed
Marcel F. Neuts
出版情報: London ; New York : Chapman & Hall, 1995  xii, 465 p. ; 24 cm
シリーズ名: Stochastic modeling
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目次情報: 続きを見る
Preface
Computational Probability: An Introduction
Solving Equations
Functions of Random Variables
Discrete-Time Markov Chains
Continuous-Time Markov Chains
Experimentation and Visualization
References
Some Topics from Matrix Analysis / Appendix 1：
Phase-Type Distibutions / Appendix 2：
The Markovian Arrival Process / Appendix 3：
Solution to Selected Problems
Index
Preface
Computational Probability: An Introduction
Solving Equations
33.

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33. Randomized algorithms
Rajeev Motwani, Prabhakar Raghavan
出版情報: Cambridge ; New York, N.Y. : Cambridge University Press, 1995  xiv, 476 p. ; 26 cm
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目次情報: 続きを見る
Tools and Techniques / Part I：
Introduction / 1：
Game-theoretic techniques / 2：
Moments and deviations / 3：
Tail inequalities / 4：
The probabilistic method / 5：
Markov chains and random walks / 6：
Algebraic techniques / 7：
Applications / Part II：
Data structures / 8：
Geometric algorithms and linear programming / 9：
Graph algorithms / 10：
Approximate counting / 11：
Parallel and distributed algorithms / 12：
Online algorithms / 13：
Number theory and algebra / 14：
Notational index / Appendix A：
Mathematical background / Appendix B：
Basic probability theory / Appendix C：
Tools and Techniques / Part I：
Introduction / 1：
Game-theoretic techniques / 2：
34.

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34. Adjoint equations and perturbation algorithms in nonlinear problems
Guri I. Marchuk, Valeri I. Agoshkov, Victor P. Shutyaev
出版情報: Boca Raton, Fla. : CRC Press, c1996  275 p. ; 25 cm
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目次情報: 続きを見る
Principles of Construction of Adjoint Operators in Non-Linear Problems
Properties of Adjoint Operators Constructed on the Basis of Various Principles
Solvability of Main and Adjoint Equations in Non-Linear Problems
Transformation Groups, Conservation Laws and Construction of the Adjoint Operators in Non-Linear Problems
Perturbation Algorithms in Non-Linear Problems
Adjoint Equations and the N-th Order Perturbation Algorithms in Non-Linear Problems of Transport Theory
Adjoint and
Principles of Construction of Adjoint Operators in Non-Linear Problems
Properties of Adjoint Operators Constructed on the Basis of Various Principles
Solvability of Main and Adjoint Equations in Non-Linear Problems
35.

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図書
35. The design and analysis of computer algorithms
Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman
出版情報: Reading, MA : Addison-Wesley, 1974  x, 470 p. ; 24 cm
シリーズ名: Addison-Wesley series in computer science and information processing
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36.

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36. Unobstructed shortest paths in polyhedral environments (: gw ; : us)
Varol Akman
出版情報: Berlin ; Tokyo : Springer-Verlag, c1987  103 p. ; 25 cm
シリーズ名: Lecture notes in computer science ; 251
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37.

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図書
37. Computer algorithms : introduction to design and analysis
Sara Baase
出版情報: Reading, Mass. : Addison-Wesley Pub. Co., c1978  xvii, 286 p. ; 25 cm
シリーズ名: Addison-Wesley series in computer science
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38.

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38. Algorithms and complexity
Herbert S. Wilf
出版情報: Englewood Cliffs, N.J. : Prentice-Hall, c1986  vi, 231 p. ; 24 cm
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目次情報: 続きを見る
Preface
Preface to the Second Edition
What this Book Is About / 0：
Background / 0.1：
Hard versus Easy Problems / 0.2：
A Preview / 0.3：
Mathematical Preliminaries / 1：
Orders of Magnitude / 1.1：
Positional Number Systems / 1.2：
Manipulations with Series / 1.3：
Recurrence Relations / 1.4：
Counting / 1.5：
Graphs / 1.6：
Recursive Algorithms / 2：
Introduction / 2.1：
Quicksort / 2.2：
Recursive Graph Algorithms / 2.3：
Fast Matrix Multiplication / 2.4：
The Discrete Fourier Transform / 2.5：
Applications of the FFT / 2.6：
A Review / 2.7：
Bibliography / 2.8：
The Network Flow Problem / 3：
Algorithms for the Network Flow Problem / 3.1：
The Algorithm of Ford and Fulkerson / 3.3：
The Max-Flow Min-Cut Theorem / 3.4：
The Complexity of the Ford-Fulkerson Algorithm / 3.5：
Layered Networks / 3.6：
The MPM Algorithm / 3.7：
Applications of Network Flow / 3.8：
Algorithms in the Theory of Numbers / 4：
Preliminaries / 4.1：
The Greatest Common Divisor / 4.2：
The Extended Euclidean Algorithm / 4.3：
Primality Testing / 4.4：
Interlude: The Ring of Integers Modulo n / 4.5：
Pseudoprimality Tests / 4.6：
Proof of Goodness of the Strong Pseudoprimality Test / 4.7：
Factoring and Cryptography / 4.8：
Factoring Large Integers / 4.9：
Proving Primality / 4.10：
NP-Completeness / 5：
Turing Machines / 5.1：
Cook's Theorem / 5.3：
Some Other NP-Complete Problems / 5.4：
Half a Loaf ... / 5.5：
Backtracking (I): Independent Sets / 5.6：
Backtracking (II): Graph Coloring / 5.7：
Approximate Algorithms for Hard Problems / 5.8：
Hints and Solutions for Selected Problems
Index
Preface
Preface to the Second Edition
What this Book Is About / 0：
39.

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39. Combinatorial algorithms
T.C. Hu
出版情報: Reading, MA : Addison-Wesley Pub. Co., c1982  292 p. ; 24 cm
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目次情報: 続きを見る
Shortest Paths / Chapter 1：
Graph terminology / 1.1：
Shortest path / 1.2：
Multiterminal shortest paths / 1.3：
Decomposition algorithm / 1.4：
Acyclic network / 1.5：
Shortest paths in a general network / 1.6：
Minimum spanning tree / 1.7：
Breadth-first-search and depth-first-search / 1.8：
Maximum flows / Chapter 2：
Maximum flow / 2.1：
Algorithms for max flows / 2.2：
Ford and Fulkerson / 2.2.1：
Karzanov's algorithm / 2.2.2：
MPM algorithms / 2.2.3：
Analysis of algorithms / 2.2.4：
Multi-terminal maximum flows / 2.3：
Realization / 2.3.1：
Analysis / 2.3.2：
Synthesis / 2.3.3：
Multi-commodity flows / 2.3.4：
Minimum cost flows / 2.4：
Applications / 2.5：
Sets of distinct representatives / 2.5.1：
PERT / 2.5.2：
Optimum communication spanning tree / 2.5.3：
Dynamic programming / Chapter 3：
Introduction / 3.1：
Knapsack problem / 3.2：
Two-dimensional knapsack problem / 3.3：
Minimum-cost alphabetic tree / 3.4：
Summary / 3.5：
Backtracking / Chapter 4：
Estimating the efficiency of backtracking / 4.1：
Branch and bound / 4.3：
Game-tree / 4.4：
Binary tree / Chapter 5：
Huffman's tree / 5.1：
Alphabetic tree / 5.3：
Hu-Tucker algorithm / 5.4：
Feasibility and optimality / 5.5：
Garsia and Wachs' algorithm / 5.6：
Regular cost function / 5.7：
T-ary tree and other results / 5.8：
Heuristic and near optimum / Chapter 6：
Greedy algorithm / 6.1：
Bin-packing / 6.2：
Job-scheduling / 6.3：
Job-scheduling (tree-constraints) / 6.4：
Matrix multiplication / Chapter 7：
Strassen's matrix multiplication / 7.1：
Optimum order of multiplying matrices / 7.2：
Partitioning a convex polygon / 7.3：
The heuristic algorithm / 7.4：
NP-complete / Chapter 8：
Polynomial algorithms / 8.1：
Nondeterministic algorithms / 8.3：
NP-complete problems / 8.4：
Facing a new problem / 8.5：
Local indexing algorithms / Chapter 9：
Mergers of algorithms / 9.1：
Maximum flows and minimum cuts / 9.2：
Maximum adjacency and minimum separation / 9.3：
Gomory-Hu tree / Chapter 10：
Tree edges and tree links / 10.1：
Contraction / 10.2：
Domination / 10.3：
Equivalent formulations / 10.4：
Optimum mergers of companies / 10.4.1：
Optimum circle partition / 10.4.2：
Extreme stars and host-feasible circles / 10.5：
The high-level approach / 10.6：
Chop-stick method / 10.7：
Relationship between phases / 10.8：
The staircase diagram / 10.9：
Complexity issues / 10.10：
Comments on Chapters 2, 5 & 6 / Appendix A：
Ancestor trees / A.1：
Minimum surface or plateau problem / A.2：
Comments on binary trees in chapter 5 / A.3：
A simple proof of the Hu-Tucker algorithm / A.3.1：
Binary search trees / A.3.2：
Binary search on a tape / A.3.3：
Comments on §6.2, bin-packing / A.4：
Network algebra / Appendix B：
Shortest Paths / Chapter 1：
Graph terminology / 1.1：
Shortest path / 1.2：
40.

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40. Algorithms
Robert Sedgewick
出版情報: Reading, Mass. : Addison-Wesley, c1983  viii, 551 p. ; 25 cm
シリーズ名: Addison-Wesley series in computer science and information processing
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41.

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41. Applications of graph theory algorithms
Vinod Chachra, Prabhakar M. Ghare, James M. Moore
出版情報: New York : North-Holland-New York, c1979  ix, 421 p. ; 24 cm
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42.

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42. Foundations of genetic algorithms ([1] - 7)
edited by Gregory J.E. Rawlins
出版情報: San Mateo, Calif. : Morgan Kaufmann Publishers, c1991-  v. ; 22-24 cm
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目次情報: 続きを見る
Introduction / Wolfgang Banzhaf ; Colin Reeves
On the Dynamics of EAs without Selection / Hans-Georg Beyer
Candidate Longpaths for the Simple Genetic Algorithm / Leila Kallel ; Bart Naudts
On the Limit of Long Strings / Adam Prugel-Bennett
Modelling the Dynamics of a Steady State Genetic Algorithm / Alex Rogers
Population Fixed-Points for Functions of Unitation / Jonathan E. Rowe
Dining with GAs: Operator Lunch Theorems / William M. Spears ; Kenneth A. De Jong
Putting the "Genetics" Back into Genetic Algorithms (Reconsidering the Role of Crossover in Hybrid Operators) / Stephen Chen ; Stephen F. Smith
Schemata as Building Blocks: Does Size Matter? / C. R. Stephens ; H. Waelbroeck ; R. Aguirre
A Formal Language for Permutation Recombination Operators / Michael Vose ; Darrell Whitley
Locality vs. Randomness--Dependence of Operator Quality on the Search State / Karsten Weicker ; Nicole Weicker
An Examination of Tunable, Random Search Landscapes / R. E. Smith ; J. E. Smith
Test Function Generators as Embedded Landscapes / Robert B. Heckendorn ; Soraya Rana
Genetic Algorithms, Fitness Sublandscapes and Subpopulations / Vanio Slavov ; Nikolay Nikolaev
Replacement Strategies in Steady State Genetic Algorithms: Static Environments / Jim Smith ; Frank Vavak
The Effect of Incest Prevention on Genetic Drift / J. David Schaffer ; Murali Mani ; Larry Eshelman ; Keith Mathias
Recombination and Error Thresholds in Finite Populations / Gabriela Ochoa ; Inman Harvey
Understanding Interactions among Genetic Algorithm Parameters / Kalyanmoy Deb ; Samir Agrawal
Toward a Control Map for Niching / Jeffrey Horn ; David E. Goldberg
Author Index
Key Word Index
Introduction / Wolfgang Banzhaf ; Colin Reeves
On the Dynamics of EAs without Selection / Hans-Georg Beyer
Candidate Longpaths for the Simple Genetic Algorithm / Leila Kallel ; Bart Naudts
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43. Axioms and hulls (: gw ; : us)
D.E. Knuth
出版情報: Berlin ; New York : Springer-Verlag, c1992  vii, 109 p. ; 25 cm
シリーズ名: Lecture notes in computer science ; 606
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44. Identification of industrial processes : the application of computers in research and production control
N. S. Rajbman and V. M. Chadeev ; translator F. W. Gerretsen ; scientific editor P. Eykhoff
出版情報: Amsterdam ; New York : North-Holland Publishing Co. : sole distributors for the U.S.A. and Canada, Elsevier North-Holland, 1980  xiv, 435 p. ; 23 cm
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45. Artificial neural networks : learning algorithms, performance evaluation, and applications
N.B. Karayiannis, A.N. Venetsanopoulos
出版情報: Boston : Kluwer Academic, c1993  xii, 440 p. ; 25 cm
シリーズ名: The Kluwer international series in engineering and computer science ; SECS 209
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46. Formal differentiation : a program synthesis technique
by Robert A. Paige
出版情報: Ann Arbor, Mich. : UMI Research Press, c1981  x, 277 p. ; 24 cm
シリーズ名: Computer science ; Artificial intelligence ; no. 6
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47. Fundamental algorithms for permutation groups (: gw ; : us)
G. Butler
出版情報: Berlin ; Tokyo : Springer-Verlag, c1991  xii, 238 p. ; 25 cm
シリーズ名: Lecture notes in computer science ; 559
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48.

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48. Genetic algorithms and robotics : a heuristic strategy for optimization
Yuval Davidor
出版情報: Singapore ; Teaneck, NJ : World Scientific, c1991  xiv, 164 p. ; 23 cm
シリーズ名: World Scientific series in robotics and automated systems ; 1
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49.

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図書
49. Handbook of genetic algorithms
edited by Lawrence Davis
出版情報: New York : Van Nostrand Reinhold, c1991  xii, 385 p. ; 24 cm
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50.

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50. Combinatorial algorithms
Luděk Kučera
出版情報: Bristol ; Philadelphia : Adam Hilger, c1990  xi, 270 p. ; 25 cm
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51.

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図書
51. Theory of algorithms
A.A. Markov ; [translated by Jacques J. Schorr-Kon and PST staff]
出版情報: Jerusalem : Israel Program for Scientific Translations, 1962  444 p. ; 25 cm
シリーズ名: Works of the Mathematical Institute im. V.A. Steklov ; vol. 42
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52.

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52. Iterative methods for the solution of equations
[by] J.F. Traub
出版情報: Englewood Cliffs, N.J. : Prentice-Hall, c1964  xviii, 310 p. ; 24 cm
シリーズ名: Prentice-Hall series in automatic computation
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53.

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53. LYaPAS : a programming language for logic and coding algorithms
edited by M.A. Gavrilov and A.D. Zakrevskii ; translated by Morton Nadler
出版情報: New York : Academic Press, 1969  xix, 475 p. ; 24 cm
シリーズ名: ACM monograph series
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54.

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図書
54. Combinatorial complexes : a mathematical theory of algorithms
Peter H. Sellers
出版情報: Dordrecht ; Boston : D. Reidel Pub. Co., c1979  xv, 184 p. ; 23 cm
シリーズ名: Mathematics and its applications ; v. 2
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55.

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図書
55. TREAT : a new and efficient match algorithm for AI production systems (: uk ; : us)
Daniel P. Miranker
出版情報: London : Pitman , San Mateo, Calif. : Morgan Kaufmann Publishers, 1990  xiii, 144 p. ; 24 cm
シリーズ名: Research notes in artificial intelligence
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56.

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図書
56. A collection of test problems for constrained global optimization algorithms (: gw ; : us)
C.A. Floudas, P.M. Pardalos
出版情報: Berlin ; New York ; Tokyo : Springer-Verlag, c1990  xiv, 180 p. ; 25 cm
シリーズ名: Lecture notes in computer science ; 455
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57.

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図書
57. Fundamentals of the computing sciences
Kurt Maly, Allen R. Hanson
出版情報: Englewood Cliffs, N.J. : Prentice-Hall, c1978  xxi, 488 p. ; 24 cm
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58.

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図書
58. Adaptive algorithms and stochastic approximations (: gw ; : us)
Albert Benveniste, Michel Métivier, Pierre Priouret ; translated from the French by Stephen S. Wilson
出版情報: Berlin ; Tokyo : Springer-Verlag, c1990  xi, 365 p. ; 25 cm
シリーズ名: Applications of mathematics ; 22
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59.

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図書
59. More C tools for scientists and engineers
Louis Baker
出版情報: New York : McGraw-Hill, c1991  xi, 308 p. ; 23 cm
シリーズ名: Computing that works
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60.

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図書
60. Algorithms, their complexity and efficiency
Lydia I. Kronsjö
出版情報: Chichester ; New York : John Wiley, c1979  xv, 361 p. ; 26 cm
シリーズ名: Wiley series in computing
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61.

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図書
61. The metrical theory of Jacobi-Perron algorithm (: Germany ; : U.S.)
Fritz Schweiger
出版情報: Berlin ; New York : Springer-Verlag, 1973  111 p. ; 25 cm
シリーズ名: Lecture notes in mathematics ; 334
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62.

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図書
62. The design and analysis of parallel algorithms (: pbk)
Selim G. Akl
出版情報: Englewood Cliffs, N.J. : Prentice Hall, c1989  xiii, 401 p. ; 25 cm
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63.

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図書
63. Fast algorithms and their implementation on specialized parallel computers (: us)
J. Mikloško ... [et al.] ; edited by J. Mikloško
出版情報: Bratislava : VEDA, Pub. House of the Slovak Academy of Sciences , Amsterdam ; Tokyo : North-Holland, 1989  xv, 261 p. ; 25 cm
シリーズ名: Special topics in supercomputing ; v. 5
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64.

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図書
64. Efficient parallel algorithms
Alan Gibbons, Wojciech Rytter
出版情報: Cambridge [Cambridgeshire] : Cambridge University Press, c1988  viii, 259 p. ; 26 cm
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目次情報: 続きを見る
Preface
Introduction / 1：
The model of parallel computation / 1.1：
Some general algorithmic techniques / 1.2：
Reducing the number of processors / 1.3：
Examples of fast parallel computations on vectors and lists / 1.4：
Bibliography
Graph algorithms / 2：
Parallel computations on trees / 2.1：
Paths, spanning trees, connected components and blocks / 2.2：
Eulerian circuits and maximal matchings / 2.3：
Colouring of graphs / 2.4：
Bibliographic notes
Expression evaluation / 3：
Constructing the expression tree / 3.1：
A parallel pebble game with applications to expression evaluation / 3.2：
An optimal parallel algorithm for expression evaluation / 3.3：
The optimal parallel transformation of regular expressions to non-deterministic finite automata / 3.4：
Evaluation of generalised expressions: straight-line programs / 3.5：
More efficient algorithms for dynamic programming / 3.6：
A more algebraic point of view: a method of simultaneous substitutions / 3.7：
Parallel recognition and parsing of context-free languages / 4：
Parallel recognition of general context-free languages / 4.1：
Parallel recognition of unambiguous context-free languages / 4.2：
Parallel parsing of general context-free languages / 4.3：
Optimal parallel recognition and parsing of bracket languages / 4.4：
Optimal parallel recognition of input-driven languages / 4.5：
Fast parallel sorting / 5：
Batcher's sorting networks / 5.1：
Cole's optimal parallel merge sort / 5.2：
A theoretical optimal sorting network: Paterson's version of the algorithm of Ajtai, Komlos and Szemeredi / 5.3：
Parallel string matching / 6：
Analysis of the text / 6.1：
Preprocessing the pattern / 6.2：
Complexity of the whole pattern-matching algorithm / 6.3：
P-completeness: hardly parallelisable problems / 7：
A first P-complete problem / 7.1：
A selection of P-complete problems / 7.2：
Index of definitions, techniques and algorithms
Preface
Introduction / 1：
The model of parallel computation / 1.1：
65.

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65. Algorithms for clustering data
Anil K. Jain, Richard C. Dubes
出版情報: Englewood Cliffs, N.J. : Prentice Hall, c1988  xiv, 320 p. ; 24 cm
シリーズ名: Prentice Hall advanced reference series ; . Computer science
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66.

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図書
66. Algorithms for graphics and image processing
Theo Pavlidis
出版情報: Rockville, MD : Computer Science Press, c1982  xv, 416 p., [29] p. of plates ; 24 cm
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67.

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図書
67. Interactive computer graphics : data structures, algorithms, languages
Wolfgang K. Giloi
出版情報: Englewood Cliffs, N.J. : Prentice-Hall, c1978  xiii, 354 p. ; 24 cm
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68.

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図書
68. The theory of algorithms
by A.A. Markov and N.M. Nagorny ; translated by M. Greendlinger
出版情報: Dordrecht ; Boston : Kluwer Academic, c1988  xxiv, 369 p. ; 25 cm
シリーズ名: Mathematics and its applications ; Soviet series ; 23
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69.

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図書
69. Parallel algorithms and matrix computation (: pbk)
Jagdish J. Modi
出版情報: Oxford [Oxfordshire] : Clarendon Press , New York ; Tokyo : Oxford University Press, 1988  xi, 260 p. ; 22 cm
シリーズ名: Oxford applied mathematics and computing science series
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70.

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図書
70. Genetic algorithms in search, optimization, and machine learning
David E. Goldberg
出版情報: Reading, Mass. ; Tokyo : Addison-Wesley, c1989  xiii, 412 p. ; 25 cm
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目次情報: 続きを見る
Genetic Algorithms Revisited: Mathematical Foundations
Computer Implementation of a Genetic Algorithm
Some Applications of Genetic Algorithms
Advanced Operators and Techniques in Genetic Search
Introduction to Genetics-Based Machine Learning
Applications of Genetics-Based Machine Learning
A Look Back, A Glance Ahead
Appendixes
Genetic Algorithms Revisited: Mathematical Foundations
Computer Implementation of a Genetic Algorithm
Some Applications of Genetic Algorithms
71.

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図書
71. Learning algorithms theory and applications (: us ; : gw)
S. Lakshmivarahan
出版情報: New York : Springer-Verlag, c1981  x, 279 p. ; 24 cm
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72.

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図書
72. Algorithms for parallel polygon rendering (: gw ; : us)
T. Theoharis
出版情報: Berlin ; Tokyo : Springer-Verlag, c1989  vii, 147 p. ; 25 cm
シリーズ名: Lecture notes in computer science ; 373
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73.

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図書
73. Logical and combinatorial algorithms for drug design (: Research Studies Press ; : Wiley)
V.E. Golender and A.B. Rozenblit
出版情報: Letchworth, Hertfordshire, Eng. : Research Studies Press , New York : Wiley, c1983  xiii, 289 p. ; 24 cm
シリーズ名: Chemometrics series ; 6
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74.

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74. Residue arithmetic and its applications to computer technology
Nicholas S. Szabó, Richard I. Tanaka
出版情報: New York : McGraw-Hill, c1967  xvi, 236 p. ; 23 cm
シリーズ名: McGraw-Hill series in information processing and computers
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75.

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図書
75. Design and analysis of algorithms
Jeffrey D. Smith
出版情報: Boston : PWS-KENT Pub. Co., c1989  xiii, 447 p. ; 25 cm
シリーズ名: Computer science series
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76.

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図書
76. Computing: fundamentals and applications
[by] Taylor L. Booth [and] Yi-tzuu Chien
出版情報: Santa Barbara, Calif. : Hamilton, c1974  xix, 497 p. ; 24 cm
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77.

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図書
77. Algorithmic combinatorics
Even, Shimon, 1935-
出版情報: New York : Macmillan, [1973]  xii, 260 p ; 24 cm
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78.

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図書
78. Mathematics for the analysis of algorithms
Daniel H. Greene, Donald E, Knuth
出版情報: Boston : Birkhäuser, c1981  107 p. ; 24 cm
シリーズ名: Progress in computer science ; no. 1
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79.

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図書
79. C tools for scientists and engineers (: pbk)
Louis Baker
出版情報: New York : McGraw-Hill, c1989  xii, 324 p. ; 23 cm
シリーズ名: Computing that works
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80.

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図書
80. Practical numerical algorithms for chaotic systems (: us ; : gw)
Thomas S. Parker, Leon O. Chua
出版情報: New York ; Berlin : Springer-Verlag, c1989  xiv, 348 p. ; 25 cm
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目次情報: 続きを見る
Steady-State Solutions
PoincarF Maps
Stability
Integration
Locating Limit Sets
Manifolds
Dimension
Bifurcation Diagrams
Programming
Phase Portraits
The Newton-Raphson Algorithm
The Variational Equation
Differential Topology
The PoincarF Map
One Lyapunov Exponent Vanishes
Cantor Sets
List ot Symbols
Bibliography
Index
Steady-State Solutions
PoincarF Maps
Stability
81.

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図書
81. Algorithms and data structures
Niklaus Wirth
出版情報: Englewood Cliffs, N.J. : Prentice-Hall, c1986  288 p. ; 25 cm
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82.

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図書
82. Algorithms : the construction, proof, and analysis of programs (: pbk)
Pierre Berlioux and Philippe Bizard ; translated by Annwyl Williams
出版情報: Chichester [West Sussex] ; New York : J. Wiley, c1986  ix, 145 p. ; 23 cm
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83.

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図書
83. Systems of reductions (: gw ; : us)
B. Benninghofen, S. Kemmerich, M.M. Richter
出版情報: Berlin ; Tokyo : Springer-Verlag, c1987  x, 263 p. ; 25 cm
シリーズ名: Lecture notes in computer science ; 277
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目次情報: 続きを見る
Finite Sets of Reductions
Infinite Sets of Reductions
Automata and Reductions
Deciding Algebraic Properties of Finitely Presented Monoids by Friedrich Otto
References
Subject Index
List of Symbols and Abbreviations
Finite Sets of Reductions
Infinite Sets of Reductions
Automata and Reductions
84.

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84. Algorithmics : the spirit of computing (: pbk)
David Harel
出版情報: Wokingham, England ; Reading, Mass. : Addison-Wesley, c1987  xiv, 425 p. ; 24 cm
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85.

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85. Algorithmics : theory and practice (: pbk)
Gilles Brassard and Paul Bratley
出版情報: Englewood Cliffs, N.J. : Prentice Hall, c1988  xvi, 361 p. ; 25 cm
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86.

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図書
86. Towards dynamic randomized algorithms in computational geometry (: us ; : gw)
Monique Teillaud
出版情報: Berlin ; Tokyo : Springer-Verlag, c1993  ix, 156 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 758
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87.

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図書
87. Ray shooting, depth orders and hidden surface removal (: gw ; : us)
Mark de Berg
出版情報: Berlin ; New York : Springer-Verlag, c1993  x, 201 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 703
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88.

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図書
88. Algorithmic and combinatorial algebra (: acid-free)
by L.A. Bokutʹ and G.P. Kukin
出版情報: Dordrecht : Kluwer Academic, c1994  xvi, 384 p. ; 25 cm
シリーズ名: Mathematics and its applications ; v. 255
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89.

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図書
89. The traveling salesman : computational solutions for TSP applications (: gw ; : us)
Gerhard Reinelt
出版情報: Berlin ; New York : Springer-Verlag, c1994  viii, 223 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 840
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目次情報: 続きを見る
Introduction / 1：
Basic Concepts / 2：
Graph Theory / 2.1：
Complexity Theory / 2.2：
Linear and Integer Programming / 2.3：
Data Structures / 2.4：
Some Fundamental Algorithms / 2.5：
Related Problems and Applications / 3：
Some Related Problems / 3.1：
Practical Applications of the TSP / 3.2：
The Tes t Problem Ins tances / 3.3：
Geometric Concepts / 4：
Voronoi Diagrams / 4.1：
Delaunay Triangulations / 4.2：
Convex Hulls / 4.3：
Candidate Sets / 5：
Nearest Neighbors / 5.1：
Candidates Bas ed on the Delaunay Graph / 5.2：
Other Candidate Sets / 5.3：
Construction Heuristics / 6：
Neares t Neighbor Heuris tics / 6.1：
Ins ertion Heuris tics / 6.2：
Heuris tics Us ing Spanning Trees / 6.3：
Savings Methods and Greedy Algorithm / 6.4：
Comparis on of Cons truction Heuris tics / 6.5：
Improving Solutions / 7：
Node and Edge Ins ertion / 7.1：
2-Opt Exchange / 7.2：
Cros s ing Elimination / 7.3：
The 3-Opt Heuris tic and Variants / 7.4：
Lin-Kernighan Type Heuris tics / 7.5：
Comparis on of Improvement Heuris tics / 7.6：
Fast Heuristics for Large Geometric Problems / 8：
Space Filling Curves / 8.1：
Strip Heuris tics / 8.2：
Partial Repres entation / 8.3：
Decompos ition Approaches / 8.4：
Further Heuristic Approaches / 9：
Simulated Annealing / 9.1：
Evolutionary Strategiesand Genetic Algorithms / 9.2：
Tabu Search / 9.3：
Neural Networks / 9.4：
Lower Bounds / 10：
Boundsfrom Linear Programming / 10.1：
Simple Lower Bounds / 10.2：
Lagrangean Relaxation / 10.3：
Comparison of Lower Bounds / 10.4：
A Case Study: TSPs in PCB Production / 11：
Drilling of Printed Circuit Boards / 11.1：
Plotting of PCB Production Mas ks / 11.2：
Practical TSP Solving / 12：
Determining Optimal Solutions / 12.1：
An Implementation Concept / 12.2：
Interdependence of Algorithms / 12.3：
Appendix: TSPLIB
References
Index
Introduction / 1：
Basic Concepts / 2：
Graph Theory / 2.1：
90.

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図書
90. Algorithms for random generation and counting : a Markov chain approach (: us ; : gw)
Alistair Sinclair
出版情報: Boston : Birkhäuser, c1993  146 p. ; 25 cm
シリーズ名: Progress in theoretical computer science
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91.

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図書
91. Rewriting techniques and applications : 14th International Conference, RTA 2003, Valencia, Spain, June 9-11, 2003 : proceedings
Robert Nieuwenhuis (ed.)
出版情報: Berlin ; Tokyo : Springer, c2003  xi, 514 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 2706
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92.

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92. Speech coding algorithms : foundation and evolution of standardized coders
Wai C. Chu
出版情報: New York : John Wiley & Sons, Inc., c2003  xxiv, 558 p. ; 25 cm
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目次情報: 続きを見る
Preface
Acronyms
Notation
Introduction / 1：
Overview of Speech Coding / 1.1：
Classification of Speech Coders / 1.2：
Speech Production and Modeling / 1.3：
Some Properties of the Human Auditory System / 1.4：
Speech Coding Standards / 1.5：
About Algorithms / 1.6：
Summary and References / 1.7：
Signal Processing Techniques / 2：
Pitch Period Estimation / 2.1：
All-Pole and All-Zero Filters / 2.2：
Convolution / 2.3：
Exercises / 2.4：
Stochastic Processes and Models / 3：
Power Spectral Density / 3.1：
Periodogram / 3.2：
Autoregressive Model / 3.3：
Autocorrelation Estimation / 3.4：
Other Signal Models / 3.5：
Linear Prediction / 3.6：
The Problem of Linear Prediction / 4.1：
Linear Prediction Analysis of Nonstationary Signals / 4.2：
Examples of Linear Prediction Analysis of Speech / 4.3：
The Levinson-Durbin Algorithm / 4.4：
The Leroux-Gueguen Algorithm / 4.5：
Long-Term Linear Prediction / 4.6：
Synthesis Filters / 4.7：
Practical Implementation / 4.8：
Moving Average Prediction / 4.9：
Scalar Quantization / 4.10：
Uniform Quantizer / 5.1：
Optimal Quantizer / 5.3：
Quantizer Design Algorithms / 5.4：
Algorithmic Implementation / 5.5：
Pulse Code Modulation and its Variants / 5.6：
Uniform Quantization / 6.1：
Nonuniform Quantization / 6.2：
Differential Pulse Code Modulation / 6.3：
Adaptive Schemes / 6.4：
Vector Quantization / 6.5：
Multistage VQ / 7.1：
Predictive VQ / 7.5：
Other Structured Schemes / 7.6：
Scalar Quantization of Linear Prediction Coefficient / 7.7：
Spectral Distortion / 8.1：
Quantization Based on Reflection Coefficient and Log Area Ratio / 8.2：
Line Spectral Frequency / 8.3：
Quantization Based on Line Spectral Frequency / 8.4：
Interpolation of LPC / 8.5：
Linear Prediction Coding / 8.6：
Speech Production Model / 9.1：
Structure of the Algorithm / 9.2：
Voicing Detector / 9.3：
The FS1015 LPC Coder / 9.4：
Limitations of the LPC Model / 9.5：
Regular-Pulse Excitation Coders / 9.6：
Multipulse Excitation Model / 10.1：
Regular-Pulse-Excited-Long-Term Prediction / 10.2：
Code-Excited Linear Prediction / 10.3：
The CELP Speech Production Model / 11.1：
The Principle of Analysis-by-Synthesis / 11.2：
Encoding and Decoding / 11.3：
Excitation Codebook Search / 11.4：
Postfilter / 11.5：
The Federal Standard Version of Celp / 11.6：
Improving the Long-Term Predictor / 12.1：
The Concept of the Adaptive Codebook / 12.2：
Incorporation of the Adaptive Codebook to the CELP Framework / 12.3：
Stochastic Codebook Structure / 12.4：
Adaptive Codebook Search / 12.5：
Stochastic Codebook Search / 12.6：
Encoder and Decoder / 12.7：
Vector Sum Excited Linear Prediction / 12.8：
The Core Encoding Structure / 13.1：
Search Strategies for Excitation Codebooks / 13.2：
Excitation Codebook Searches / 13.3：
Gain Related Procedures / 13.4：
Low-Delay Celp / 13.5：
Strategies to Achieve Low Delay / 14.1：
Basic Operational Principles / 14.2：
Linear Prediction Analysis / 14.3：
Backward Gain Adaptation / 14.4：
Codebook Training / 14.6：
Vector Quantization of Linear Prediction Coefficient / 14.8：
Correlation Among the LSFs / 15.1：
Split VQ / 15.2：
Algebraic Celp / 15.3：
Algebraic Codebook Structure / 16.1：
Adaptive Codebook / 16.2：
Algebraic Codebook Search / 16.3：
Gain Quantization Using Conjugate VQ / 16.5：
Other ACELP Standards / 16.6：
Mixed Excitation Linear Prediction / 16.7：
The MELP Speech Production Model / 17.1：
Fourier Magnitudes / 17.2：
Shaping Filters / 17.3：
Pitch Period and Voicing Strength Estimation / 17.4：
Encoder Operations / 17.5：
Decoder Operations / 17.6：
Source-Controlled Variable Bit-Rate Celp / 17.7：
Adaptive Rate Decision / 18.1：
LP Analysis and LSF-Related Operations / 18.2：
Decoding and Encoding / 18.3：
Speech Quality Assessment / 18.4：
The Scope of Quality and Measuring Conditions / 19.1：
Objective Quality Measurements for Waveform Coders / 19.2：
Subjective Quality Measures / 19.3：
Improvements on Objective Quality Measures / 19.4：
Minimum-Phase Property of the Forward Prediction-Error Filter / Appendix A：
Some Properties of Line Spectral Frequency / Appendix B：
Research Directions in Speech Coding / Appendix C：
Linear Combiner for Pattern Classification / Appendix D：
Celp: Optimal Long-Term Predictor to Minimize the Weighted Difference / Appendix E：
Review of Linear Algebra: Orthogonality, Basis, Linear Independence, and the Gram-Schmidt Algorithm / Appendix F：
Bibliography
Index
Preface
Acronyms
Notation
93.

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93. Automatic sequences
by Friedrich von Haeseler
出版情報: Berlin : Walter de Gruyter, 2003  191 p. ; 25 cm
シリーズ名: De Gruyter expositions in mathematics ; v. 36
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94.

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図書
94. Graph theory for programmers : algorithms for processing trees
by Victor N. Kasyanov and Vladimir A. Evstigneev ; [translated by P. Malyshev]
出版情報: Dordrecht ; Boston : Kluwer Academic Publishers, c2000  x, 432 p. ; 25 cm
シリーズ名: Mathematics and its applications ; v. 515
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目次情報: 続きを見る
Preface
Basic Concepts and Algorithms / Part 1.：
Trees and Their Properties / Chapter 1.：
Introduction and Basic Definitions / 1.1.：
Representations of Trees / 1.2.：
Numbering and Calculation of Trees / 1.3.：
Bibliographical Notes / 1.4.：
References
Computational Models. Complexity and Fundamental Algorithms / Chapter 2.：
Introduction: Algorithm Representation Language / 2.1.：
Depth-First and Breadth-First Traversals of Graphs and Trees / 2.2.：
Generation of Trees / 2.3.：
Spanning Trees / 2.4.：
The Problem of Finding the Optimal Spanning Tree / 3.1.：
Algorithms of Numbering of All Spanning Trees / 3.2.：
Search of Spanning Trees with Given Properties / 3.3.：
Translation and Transformation of Programs / 3.4.：
Structural Trees / Chapter 4.：
Introduction and Principal Definitions / 4.1.：
Hierarchical Representations of Regularizable CF-Graphs / 4.2.：
Hammock Representations of CF-Graphs / 4.3.：
Exposure of the Dominance Relation / 4.4.：
Isomorphism, Unification, and Term-Rewriting Systems / 4.5.：
Isomorphisms of Trees / 5.1.：
Problem of Unification / 5.2.：
Term-Rewriting Systems / 5.3.：
Syntax Trees / 5.4.：
Language Syntax and the Problem of Syntax Analysis / 6.1.：
Generative Grammars / 6.2.：
Syntax Analysis / 6.3.：
Translation and Constructors of Analyzers / 6.4.：
Search and Storage of Information / 6.5.：
Information Trees / Chapter 7.：
Balanced Trees / 7.1.：
Multidimensional Trees (k-d-Trees) / 7.2.：
Trees for Multilevel Memory / 7.3.：
B-Trees / 8.1.：
Generalizations of B-Trees / 8.2.：
Multidimensional B-Trees / 8.3.：
Multiattribute Trees / 8.4.：
Additional List of Literature / 8.5.：
Subject Index
Preface
Basic Concepts and Algorithms / Part 1.：
Trees and Their Properties / Chapter 1.：
95.

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図書
95. Optimization algorithms in physics
Alexander K. Hartmann, Heiko Rieger
出版情報: Berlin : Wiley-VCH, c2002  x, 372 p. ; 25 cm
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目次情報: 続きを見る
Introduction to Optimization / 1：
Bibliography
Complexity Theory / 2：
Algorithms / 2.1：
Time Complexity / 2.2：
NP Completeness / 2.3：
Programming Techniques / 2.4：
Graphs / 3：
Trees and Lists / 3.1：
Networks / 3.3：
Graph Representations / 3.4：
Basic Graph Algorithms / 3.5：
NP-Complete Graph Problems / 3.6：
Simple Graph Algorithms / 4：
The Connectivity-percolation Problem / 4.1：
Hoshen-Kopelman Algorithm / 4.1.1：
Other Algorithms for Connectivity Percolation / 4.1.2：
General Search Algorithms / 4.1.3：
Shortest-path Algorithms / 4.2：
The Directed Polymer in a Random Medium / 4.2.1：
Dijkstra's Algorithm / 4.2.2：
Label-correcting Algorithm / 4.2.3：
Minimum Spanning Tree / 4.3：
Introduction to Statistical Physics / 5：
Basics of Statistical Physics / 5.1：
Phase Transitions / 5.2：
Percolation and Finite-size Scaling / 5.3：
Magnetic Transition / 5.4：
Disordered Systems / 5.5：
Maximum-flow Methods / 6：
Random-field Systems and Diluted Antiferromagnets / 6.1：
Transformation to a Graph / 6.2：
Simple Maximum Flow Algorithms / 6.3：
Dinic's Method and the Wave Algorithm / 6.4：
Calculating all Ground States / 6.5：
Results for the RFIM and the DAFF / 6.6：
Minimum-cost Flows / 7：
Motivation / 7.1：
The Solution of the N-Line Problem / 7.2：
Convex Mincost-flow Problems in Physics / 7.3：
General Minimum-cost-flow Algorithms / 7.4：
Miscellaneous Results for Different Models / 7.5：
Genetic Algorithms / 8：
The Basic Scheme / 8.1：
Finding the Minimum of a Function / 8.2：
Ground States of One-dimensional Quantum Systems / 8.3：
Orbital Parameters of Interacting Galaxies / 8.4：
Approximation Methods for Spin Glasses / 9：
Spin Glasses / 9.1：
Experimental Results / 9.1.1：
Theoretical Approaches / 9.1.2：
Genetic Cluster-exact Approximation / 9.2：
Energy and Ground-state Statistics / 9.3：
Ballistic Search / 9.4：
Results / 9.5：
Matchings / 10：
Matching and Spin Glasses / 10.1：
Definition of the General Matching Problem / 10.2：
Augmenting Paths / 10.3：
Matching Algorithms / 10.4：
Maximum-cardinality Matching on Bipartite Graphs / 10.4.1：
Minimum-weight Perfect Bipartite Matching / 10.4.2：
Cardinality Matching on General Graphs / 10.4.3：
Minimum-weight Perfect Matching for General Graphs / 10.4.4：
Ground-state Calculations in 2d / 10.5：
Monte Carlo Methods / 11：
Stochastic Optimization: Simple Concepts / 11.1：
Simulated Annealing / 11.2：
Parallel Tempering / 11.3：
Prune-enriched Rosenbluth Method (PERM) / 11.4：
Protein Folding / 11.5：
Branch-and-bound Methods / 12：
Vertex Covers / 12.1：
Numerical Methods / 12.2：
Practical Issues / 12.3：
Software Engineering / 13.1：
Object-oriented Software Development / 13.2：
Programming Style / 13.3：
Programming Tools / 13.4：
Using Macros / 13.4.1：
Make Files / 13.4.2：
Scripts / 13.4.3：
Libraries / 13.5：
Numerical Recipes / 13.5.1：
LEDA / 13.5.2：
Creating your own Libraries / 13.5.3：
Random Numbers / 13.6：
Generating Random Numbers / 13.6.1：
Inversion Method / 13.6.2：
Rejection Method / 13.6.3：
The Gaussian Distribution / 13.6.4：
Tools for Testing / 13.7：
gdb / 13.7.1：
ddd / 13.7.2：
checkergcc / 13.7.3：
Evaluating Data / 13.8：
Data Plotting / 13.8.1：
Curve Fitting / 13.8.2：
Finite-size Scaling / 13.8.3：
Information Retrieval and Publishing / 13.9：
Searching for Literature / 13.9.1：
Preparing Publications / 13.9.2：
Index
Introduction to Optimization / 1：
Bibliography
Complexity Theory / 2：
96.

図書
図書
96. Sensor and data fusion concepts and applications
Lawrence A. Klein
出版情報: Bellingham, Wash., USA : SPIE Optical Engineering Press, c1993  xviii, 131 p. ; 26 cm
シリーズ名: Tutorial texts in optical engineering ; v. TT 14
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97.

図書
図書
97. Rewriting techniques and applications : 15th International Conference, RTA 2004, Aachen, Germany, June 3-5, 2004 : proceedings
Vincent van Oostrom (ed.)
出版情報: Berlin ; Tokyo : Springer, c2004  x, 311 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 3091
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98.

図書
図書
98. Concentration of measure for the analysis of randomized algorithms (: hard)
Devdatt P. Dubhashi, Alessandro Panconesi
出版情報: Cambridge ; New York : Cambridge University Press, 2009  xiv, 196 p. ; 24 cm
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Preface
Chernoff-Hoeffding Bounds / 1：
What Is "Concentration of Measure"? / 1.1：
The Binomial Distribution / 1.2：
The Chernoff Bound / 1.3：
Heterogeneous Variables / 1.4：
The Hoeffding Extension / 1.5：
Useful Forms of the Bound / 1.6：
A Variance Bound / 1.7：
Pointers to the Literature / 1.8：
Problems / 1.9：
Applications of the Chernoff-Hoeffding Bounds / 2：
Probabilistic Amplification / 2.1：
Load Balancing / 2.2：
Skip Lists / 2.3：
Quicksort / 2.4：
Low-Distortion Embeddings / 2.5：
Chernoff-Hoeffding Bounds in Dependent Settings / 2.6：
Negative Dependence / 3.1：
Local Dependence / 3.2：
Janson's Inequality / 3.3：
Limited Independence / 3.4：
Markov Dependence / 3.5：
Interlude: Probabilistic Recurrences / 3.6：
Martingales and the Method of Bounded Differences / 4.1：
Review of Conditional Probabilities and Expectations / 5.1：
Martingales and Azuma's Inequality / 5.2：
Generalising Martingales and Azuma's Inequality / 5.3：
The Method of Bounded Differences / 5.4：
The Simple Method of Bounded Differences in Action / 5.5：
Chernoff-Hoeffding Revisited / 6.1：
Stochastic Optimisation: Bin Packing / 6.2：
Balls and Bins / 6.3：
Distributed Edge Colouring: Take 1 / 6.4：
Models for the Web Graph / 6.5：
Game Theory and Blackwell's Approachability Theorem / 6.6：
The Method of Averaged Bounded Differences / 6.7：
Hypergeometric Distribution / 7.1：
Occupancy in Balls and Bins / 7.2：
Stochastic Optimisation: Travelling Salesman Problem / 7.3：
Coupling / 7.4：
Handling Rare Bad Events / 7.5：
The Method of Bounded Variances / 7.6：
A Variance Bound for Martingale Sequences / 8.1：
Applications / 8.2：
Interlude: The Infamous Upper Tail / 8.3：
Motivation: Non-Lipschitz Functions / 9.1：
Concentration of Multivariate Polynomials / 9.2：
The Deletion Method / 9.3：
Isoperimetric Inequalities and Concentration / 9.4：
Isoperimetric Inequalities / 10.1：
Isoperimetry and Concentration / 10.2：
The Hamming Cube / 10.3：
Martingales and Isoperimetric Inequalities / 10.4：
Talagrand's Isoperimetric Inequality / 10.5：
Statement of the Inequality / 11.1：
The Method of Non-Uniformly Bounded Differences / 11.2：
Certifiable Functions / 11.3：
Isoperimetric Inequalities and Concentration via Transportation Cost Inequalities / 11.4：
Distance between Probability Distributions / 12.1：
Transportation Cost Inequalities Imply Isoperimetric Inequalities and Concentration / 12.2：
Transportation Cost Inequality in Product Spaces with the Hamming Distance / 12.3：
An Extension to Non-Product Measures / 12.4：
Quadratic Transportation Cost and Talagrand's Inequality / 12.5：
Introduction / 13.1：
Review and Road Map / 13.2：
Quadratic Transportation Cost / 13.3：
Talagrand's Inequality via Quadratic Transportation Cost / 13.5：
Extension to Dependent Processes / 13.6：
Log-Sobolev Inequalities and Concentration / 13.7：
A Discrete Log-Sobolev Inequality on the Hamming Cube / 14.1：
Tensorisation / 14.3：
Modified Log-Sobolev Inequalities in Product Spaces / 14.4：
The Method of Bounded Differences Revisited / 14.5：
Self-Bounding Functions / 14.6：
Talagrand's Inequality Revisited / 14.7：
Summary of the Most Useful Bounds / 14.8：
Bounds for Well-Behaved Functions / A.1：
Bibliography
Index
Preface
Chernoff-Hoeffding Bounds / 1：
What Is "Concentration of Measure"? / 1.1：
99.

図書
図書
99. Machine learning : an algorithmic perspective
Stephen Marsland
出版情報: Boca Raton : Chapman & Hall/CRC, c2009  xvi, 390 p. ; 25 cm
シリーズ名: Chapman & Hall/CRC machine learning & pattern recognition series
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Prologue
Introduction / 1：
If Data Had Mass, the Earth Would Be a Black Hole / 1.1：
Learning / 1.2：
Machine Learning / 1.2.1：
Types of Machine Learning / 1.3：
Supervised Learning / 1.4：
Regression / 1.4.1：
Classification / 1.4.2：
The Brain and the Neuron / 1.5：
Hebb's Rule / 1.5.1：
McCulloch and Pitts Neurons / 1.5.2：
Limitations of the McCulloch and Pitt Neuronal Model / 1.5.3：
Further Reading
Linear Discriminants / 2：
Preliminaries / 2.1：
The Perceptron / 2.2：
The Learning Rate ? / 2.2.1：
The Bias Input / 2.2.2：
The Perceptron Learning Algorithm / 2.2.3：
An Example of Perceptron Learning / 2.2.4：
Implementation / 2.2.5：
Testing the Network / 2.2.6：
Linear Separability / 2.3：
The Exclusive Or (XOR) Function / 2.3.1：
A Useful Insight / 2.3.2：
Another Example: The Pima Indian Dataset / 2.3.3：
Linear Regression / 2.4：
Linear Regression Examples / 2.4.1：
Practice Questions
The Multi-Layer Perceptron / 3：
Going Forwards / 3.1：
Biases / 3.1.1：
Going Backwards: Back-Propagation of Error / 3.2：
The Multi-Layer Preceptron Algorithm / 3.2.1：
Initialising the Weights / 3.2.2：
Different Output Activation Functions / 3.2.3：
Sequential and Batch Training / 3.2.4：
Local Minima / 3.2.5：
Picking Up Momentum / 3.2.6：
Other Improvements / 3.2.7：
The Multi-Layer Perceptron in Practice / 3.3：
Data Preparation / 3.3.1：
Amount of Training Data / 3.3.2：
Number of Hidden Layers / 3.3.3：
Generalisation and Overfitting / 3.3.4：
Training, Testing, and Validation / 3.3.5：
When to Stop Learning / 3.3.6：
Computing and Evaluating the Results / 3.3.7：
Examples of Using the MLP / 3.4：
A Regression Problem / 3.4.1：
Classification with the MLP / 3.4.2：
A Classification Example / 3.4.3：
Time-Series Prediction / 3.4.4：
Data Compression: The Auto-Associative Network / 3.4.5：
Overview / 3.5：
Deriving Back-Propagation / 3.6：
The Network Output and the Error / 3.6.1：
The Error of the Network / 3.6.2：
A Suitable Activation Function / 3.6.3：
Back-Propagation of Error / 3.6.4：
Radial Basis Functions and Splines / 4：
Concepts / 4.1：
Weight Space / 4.1.1：
Receptive Fields / 4.1.2：
The Radial Basis Function (RBF) Network / 4.2：
Training the RBF Network / 4.2.1：
The Curse of Dimensionality / 4.3：
Interpolation and Basis Functions / 4.4：
Bases and Basis Functions / 4.4.1：
The Cubic Spline / 4.4.2：
Fitting the Spline to the Data / 4.4.3：
Smoothing Splines / 4.4.4：
Higher Dimensions / 4.4.5：
Beyond the Bounds / 4.4.6：
Support Vector Machines / 5：
Optimal Separation / 5.1：
Kernels / 5.2：
Example: XOR / 5.2.1：
Extensions to the Support Vector Machine / 5.2.2：
Learning with Trees / 6：
Using Decision Trees / 6.1：
Constructing Decision Trees / 6.2：
Quick Aside: Entropy in Information Theory / 6.2.1：
ID3 / 6.2.2：
Implementing Trees and Graphs in Python / 6.2.3：
Implementation of the Decision Tree / 6.2.4：
Dealing with Continuous Variables / 6.2.5：
Computational Complexity / 6.2.6：
Classification and Regression Trees (CART) / 6.3：
Gini Impurity / 6.3.1：
Regression in Trees / 6.3.2：
Classification Example / 6.4：
Decision by Committee: Ensemble Learning / 7：
Boosting / 7.1：
AdaBoost / 7.1.1：
Stumpting / 7.1.2：
Bagging / 7.2：
Subagging / 7.2.1：
Different Ways to Combine Classifiers / 7.3：
Probability and Learning / 8：
Turning Data into Probabilities / 8.1：
Minimising Risk / 8.1.1：
The Naive Bayes' Classifier / 8.1.2：
Some Basic Statistics / 8.2：
Averages / 8.2.1：
Variance and Covariance / 8.2.2：
The Gaussian / 8.2.3：
The Bias-Variance Tradeoff / 8.2.4：
Gaussian Mixture Models / 8.3：
The Expectation-Maximisation (EM) Algorithm / 8.3.1：
Nearest Neighbour Methods / 8.4：
Nearest Neighbour Smoothing / 8.4.1：
Efficient Distance Computations: the KD-Tree / 8.4.2：
Distance Measures / 8.4.3：
Unsupervised Learning / 9：
The ?-Means Algorithm / 9.1：
Dealing with Noise / 9.1.1：
The ?-Means Neural Network / 9.1.2：
Normalisation / 9.1.3：
A Better Weight Update Rule / 9.1.4：
Example: The Iris Dataset Again / 9.1.5：
Using Competitive Learning for Clustering / 9.1.6：
Vector Quantisation / 9.2：
The Self-Organising Feature Map / 9.3：
The SOM Algorithm / 9.3.1：
Neighbourhood Connections / 9.3.2：
Self-Organisation / 9.3.3：
Network Dimensionality and Boundary Conditions / 9.3.4：
Examples of Using the SOM / 9.3.5：
Dimensionality Reduction / 10：
Linear Discriminant Analysis (LDA) / 10.1：
Principal Components Analysis (PCA) / 10.2：
Relation with the Multi-Layer Perceptron / 10.2.1：
Kernel PCA / 10.2.2：
Factor Analysis / 10.3：
Independent Components Analysis (ICA) / 10.4：
Locally Linear Embedding / 10.5：
Isomap / 10.6：
Multi-Dimensional Scaling (MDS) / 10.6.1：
Optimisation and Search / 11：
Going Downhill / 11.1：
Least-Squares Optimisation / 11.2：
Taylor Expansion / 11.2.1：
The Levenberg-Marquardt Algorithm / 11.2.2：
Conjugate Gradients / 11.3：
Conjugate Gradients Example / 11.3.1：
Search: Three Basic Approaches / 11.4：
Exhaustive Search / 11.4.1：
Greedy Search / 11.4.2：
Hill Climbing / 11.4.3：
Exploitation and Exploration / 11.5：
Simulated Annealing / 11.6：
Comparison / 11.6.1：
Evolutionary Learning / 12：
The Genetic Algorithm (GA) / 12.1：
String Representation / 12.1.1：
Evaluating Fitness / 12.1.2：
Population / 12.1.3：
Generating Offspring: Parent Selection / 12.1.4：
Generating Offspring: Genetic Operators / 12.2：
Crossover / 12.2.1：
Mutation / 12.2.2：
Elitism, Tournaments, and Niching / 12.2.3：
Using Genetic Algorithms / 12.3：
Map Colouring / 12.3.1：
Punctuated Equilibrium / 12.3.2：
Example: The Knapsack Problem / 12.3.3：
Example: The Four Peaks Problem / 12.3.4：
Limitations of the GA / 12.3.5：
Training Neural Networks with Genetic Algorithms / 12.3.6：
Genetic Programming / 12.4：
Combining Sampling with Evolutionary Learning / 12.5：
Reinforcement Learning / 13：
Example: Getting Lost / 13.1：
State and Action Spaces / 13.2.1：
Carrots and Sticks: the Reward Function / 13.2.2：
Discounting / 13.2.3：
Action Selection / 13.2.4：
Policy / 13.2.5：
Markov Decision Processes / 13.3：
The Markov Property / 13.3.1：
Probabilities in Markov Decision Processes / 13.3.2：
Values / 13.4：
Back on Holiday: Using Reinforcement Learning / 13.5：
The Difference between Sarsa and Q-Learning / 13.6：
Uses of Reinforcement Learning / 13.7：
Markov Chain Monte Carlo (MCMC) Methods / 14：
Sampling / 14.1：
Random Numbers / 14.1.1：
Gaussian Random Numbers / 14.1.2：
Monte Carlo or Bust / 14.2：
The Proposal Distribution / 14.3：
Markov Chain Monte Carlo / 14.4：
Markov Chains / 14.4.1：
The Metropolis-Hastings Algorithm / 14.4.2：
Simulated Annealing (Again) / 14.4.3：
Gibbs Sampling / 14.4.4：
Graphical Models / 15：
Bayesian Networks / 15.1：
Example: Exam Panic / 15.1.1：
Approximate Inference / 15.1.2：
Making Bayesian Networks / 15.1.3：
Markov Random Fields / 15.2：
Hidden Markov Models (HMMs) / 15.3：
The Forward Algorithm / 15.3.1：
The Viterbi Algorithm / 15.3.2：
The Baum-Welch or Forward-Backward Algorithm / 15.3.3：
Tracking Methods / 15.4：
The Kalman Filter / 15.4.1：
The Particle Filter / 15.4.2：
Python / 16：
Installing Python and Other Packages / 16.1：
Getting Started / 16.2：
Python for MATLAB and R users / 16.2.1：
Code Basics / 16.3：
Writing and Importing Code / 16.3.1：
Control Flow / 16.3.2：
Functions / 16.3.3：
The doc String / 16.3.4：
map and lambda / 16.3.5：
Exceptions / 16.3.6：
Classes / 16.3.7：
Using NumPy and Matplotlib / 16.4：
Arrays / 16.4.1：
Linear Algebra / 16.4.2：
Plotting / 16.4.4：
Index
Prologue
Introduction / 1：
If Data Had Mass, the Earth Would Be a Black Hole / 1.1：
100.

図書
図書
100. Data structures and algorithms using Python (: pbk)
Rance D. Necaise
出版情報: Hoboken, N.J. : Wiley, c2011  xviii, 520 p. ; 26 cm
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Abstract Data Types / Chapter 1：
Introduction / 1.1：
Abstractions / 1.1.1：
Data Structures / 1.1.2：
The Date ADT / 1.2：
Preconditions and Postconditions / 1.2.1：
Using the ADT / 1.2.2：
Implementing the ADT / 1.2.3：
The Bag ADT / 1.3：
Selecting a Data Structure / 1.3.1：
The Class Definition / 1.3.3：
Iterators / 1.4：
The Set ADT / 1.5：
The Map ADT / 1.5.1：
Defining the ADT / 1.6.1：
Implementing the Map ADT / 1.6.2：
Alternate Implementation / 1.6.3：
Application: Histograms / 1.7：
Building a Histogram / 1.7.1：
Implementing the Histogram ADT / 1.7.2：
Programming Problems
Arrays and Vectors / Chapter 2：
The Array Structure / 2.1：
Simulating an Array / 2.1.1：
The Array ADT / 2.1.2：
The Python List (Vector) / 2.1.3：
Multi-Dimensional Arrays / 2.3：
The MultiArray ADT / 2.3.1：
Data Organization / 2.3.2：
Variable Length Arguments / 2.3.3：
MultiArray Implementation / 2.3.4：
The Matrix ADT / 2.4：
Matrix Operations / 2.4.1：
Application: The Game of Life / 2.4.2：
Rules of the Game / 2.5.1：
Designing a Solution / 2.5.2：
ADT Implementation / 2.5.3：
Exercises
Algorithm Analysis / Chapter 3：
Complexity Analysis / 3.1：
Big-O Notation / 3.1.1：
Classes of Algorithms / 3.1.2：
Empirical Analysis / 3.1.3：
Evaluating ADT Implementations / 3.2：
Evaluating the Python List / 3.2.1：
Evaluating the Set ADT / 3.2.2：
Searching / 3.3：
Linear Search / 3.3.1：
Binary Search / 3.3.2：
Working with Ordered Lists / 3.4：
Building An Ordered List / 3.4.1：
Merging Ordered Lists / 3.4.2：
The Set ADT Revisited / 3.5：
Application: The Sparse Matrix / 3.6：
Implementation / 3.6.1：
Analysis / 3.6.2：
The Linked List / Chapter 4：
A Linked Structure / 4.1：
The Singly-Linked List / 4.2：
Basic Operations / 4.2.1：
Evaluating the Linked List / 4.2.2：
The Bag ADT Revisited / 4.3：
Implementation Details / 4.3.1：
Linked List Iterator / 4.3.2：
Using a Tail Pointer / 4.4：
The Ordered Linked List / 4.5：
The Sparse Matrix Revisited / 4.6：
The New Implementation / 4.6.1：
Comparing Implementations / 4.6.2：
Application: Polynomials / 4.7：
Polynomial Operations / 4.7.1：
The Polynomial ADT / 4.7.2：
Advanced Linked Lists / 4.7.3：
Doubly-Linked List / 5.1：
Organization / 5.1.1：
List Operations / 5.1.2：
Circular Linked List / 5.2：
Multi-Linked Lists / 5.2.1：
Multiple Chains / 5.3.1：
The Sparse Matrix / 5.3.2：
Complex Iterators / 5.4：
Application: Text Editor / 5.5：
Typical Editor Operations / 5.5.1：
The Edit Buffer ADT / 5.5.2：
Stacks / 5.5.3：
The Stack ADT / 6.1：
Implementing the Stack / 6.2：
Vector Based / 6.2.1：
Linked List Version / 6.2.2：
Stack Applications / 6.3：
Balanced Delimiters / 6.3.1：
Evaluating Postfix Expressions / 6.3.2：
Application: Solving a Maze / 6.4：
Backtracking / 6.4.1：
The Maze ADT / 6.4.2：
Queues / 6.4.4：
The Queue ADT / 7.1：
Implementing the Queue / 7.2：
Circular Array / 7.2.1：
The Priority Queue / 7.2.3：
Application: Computer Simulations / 7.4：
Airline Ticket Counter / 7.4.1：
Class Specifications / 7.4.2：
Hash Tables / Chapter 8：
Hash Functions / 8.1：
Open Addressing / 8.3：
Linear Probing / 8.3.1：
Collision Resolution / 8.3.2：
Bucket Hashing / 8.4：
Hashing Efficiency / 8.5：
The Map ADT Revisited / 8.6：
Application: The Color Histogram / 8.7：
Recursion / Chapter 9：
Recursive Functions / 9.1：
Properties of Recursion / 9.2：
Classic Example: The Factorial Function / 9.2.1：
Greatest Common Divisor / 9.2.2：
Recursion and Stacks / 9.3：
The Towers of Hanoi / 9.4：
Backtracking Revisited / 9.5：
The Eight-Queens Problem / 9.5.1：
Solving the Four-Queens / 9.5.2：
Recursive Solution / 9.5.3：
Application: Sudoku Puzzles / 9.6：
Binary Trees and Heaps / Chapter 10：
Tree Structure / 10.1：
The Binary Tree / 10.2：
Traversals / 10.2.1：
Arithmetic Expresssions / 10.2.2：
Tree Threading / 10.3：
Heaps / 10.4：
Insertions / 10.4.1：
Removals / 10.4.2：
Evaluating the Heap / 10.4.3：
The Priority Queue Revisited / 10.4.4：
Application: Morse Code / 10.5：
Advanced Search Trees / Chapter 11：
The Binary Search Tree / 11.1：
Deletions / 11.1.1：
Evaluating the BST / 11.1.4：
AVL Trees / 11.2：
Evaluating the AVL Tree / 11.2.1：
2-3 Trees / 11.3：
Splay Trees / 11.4：
Application: Improved Map ADT / 11.5：
Sorting Algorithms / Chapter 12：
The Simple Algorithms / 12.1：
Bubble Sort / 12.1.1：
Selection Sort / 12.1.2：
Insertion Sort / 12.1.3：
Radix Sort / 12.2：
Basic Algorithm / 12.2.1：
Bucket Sorting / 12.2.2：
Divide and Conquer / 12.3：
Merge Sort / 12.3.1：
Quick Sort / 12.3.2：
Heap Sort / 12.4：
Application: Empirical Analysis / 12.5：
Python Review / Appendix A：
Basic Concepts / A.1：
Functions / A.2：
Sequence Types / A.3：
Classes / A.4：
Copying Objects / A.5：
Exceptions / A.6：
Object-Oriented Programming / Appendix B：
Encapsulation / B.1：
Inheritance / B.3：
Polymorphism / B.4：
Abstract Data Types / Chapter 1：
Introduction / 1.1：
Abstractions / 1.1.1：
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