算法设计与分析基础(第3版 影印版)

《算法设计与分析基础(第3版 影印版)》

基本信息

原书名：Introduction to the Design and Analysis of Algorithms, Third Edition

作者： (美)Anany Levitin

出版社：清华大学出版社

ISBN：9787302311850

上架时间：2013-5-17

出版日期：2013 年5月

开本：16开

页码：596

版次：3-1

所属分类：计算机 > 计算机科学理论与基础知识 > 计算理论 > 算法

更多关于 》》》《算法设计与分析基础(第3版 影印版)》

内容简介

计算机书籍

　　《算法设计与分析基础(第3版 影印版)》在讲述算法设计技术时采用了新的分类方法，在讨论分析方法时条分缕析，形成了连贯有序、耳目一新的风格。为便于学生掌握，本书涵盖算法入门课程的全部内容，更注重对概念(而非形式)的理解。书中通过一些流行的谜题来激发学生的兴趣，帮助他们加强和提高解决算法问题的能力。每章小结、习题提示和详细解答，形成了非常鲜明的教学特色。

　　 《算法设计与分析基础(第3版 影印版)》特色：

　　 独辟蹊径，采用一种更全面的算法设计技术分类方法

　　 涵盖递归与非递归算法的数学分析，也涉及经验分析和算法可视化

　　 探讨算法的局限性及解决方法

　　 将算法视为解决问题的工具，通过谜题和游戏来开拓算法思维

　　 为学生提供600多道习题(含提示)，为教师提供有详细解答的教师手册

目录

《算法设计与分析基础(第3版 影印版)》

new to the third edition xvii

preface xix

1introduction 1

1.1 what is an algorithm? 3

exercises 1.1 7

1.2 fundamentals of algorithmic problem solving 9

understanding the problem 9

ascertaining the capabilities of the computational device 9

choosing between exact and approximate problem solving 11

algorithm design techniques 11

designing an algorithm and data structures 12

methods of specifying an algorithm 12

proving an algorithm’s correctness 13

analyzing an algorithm 14

coding an algorithm 15

exercises 1.2 17

1.3 important problem types 18

sorting 19

searching 20

.string processing 20

graph problems 21

combinatorial problems 21

geometric problems 22

numerical problems 22

exercises 1.3 23

1.4 fundamental data structures 25

linear data structures 25

graphs 28

trees 31

sets and dictionaries 35

exercises 1.4 37

summary 38

2 fundamentals of the analysis of algorithm efficiency 41

2.1 the analysis framework 42

measuring an input’s size 43

units for measuring running time 44

orders of growth 45

worst-case, best-case, and average-case efficiencies 47

recapitulation of the analysis framework 50

exercises 2.1 50

2.2 asymptotic notations and basic efficiency classes 52

informal introduction 52

o-notation 53

-notation 54

-notation 55

useful property involving the asymptotic notations 55

using limits for comparing orders of growth 56

basic efficiency classes 58

exercises 2.2 58

2.3 mathematical analysis of nonrecursive algorithms 61

exercises 2.3 67

2.4 mathematical analysis of recursive algorithms 70

exercises 2.4 76

2.5 example: computing the nth fibonacci number 80

exercises 2.5 83

2.6 empirical analysis of algorithms 84

exercises 2.6 89

2.7 algorithm visualization 91

summary 94

3 brute force and exhaustive search 97

3.1 selection sort and bubble sort 98

selection sort 98

bubble sort 100

exercises 3.1 102

3.2 sequential search and brute-force string matching 104

sequential search 104

brute-force string matching 105

exercises 3.2 106

3.3 closest-pair and convex-hull problems by brute force 108

closest-pair problem 108

convex-hull problem 109

exercises 3.3 113

3.4 exhaustive search 115

traveling salesman problem 116

knapsack problem 116

assignment problem 119

exercises 3.4 120

3.5 depth-first search and breadth-first search 122

depth-first search 122

breadth-first search 125

exercises 3.5 128

summary 130

4 decrease-and-conquer 131

4.1 insertion sort 134

exercises 4.1 136

4.2 topological sorting 138

exercises 4.2 142

4.3 algorithms for generating combinatorial objects 144

generating permutations 144

generating subsets 146

exercises 4.3 148

4.4 decrease-by-a-constant-factor algorithms 150

binary search 150

fake-coin problem 152

russian peasant multiplication 153

josephus problem 154

exercises 4.4 156

4.5 variable-size-decrease algorithms 157

computing a median and the selection problem 158

interpolation search 161

searching and insertion in a binary search tree 163

the game of nim 164

exercises 4.5 166

summary 167

5 divide-and-conquer 169

5.1 mergesort 172

exercises 5.1 174

5.2 quicksort 176

exercises 5.2 181

5.3 binary tree traversals and related properties 182

exercises 5.3 185

5.4 multiplication of large integers and

strassen’s matrix multiplication 186

multiplication of large integers 187

strassen’s matrix multiplication 189

exercises 5.4 191

5.5 the closest-pair and convex-hull problems

by divide-and-conquer 192

the closest-pair problem 192

convex-hull problem 195

exercises 5.5 197

summary 198

6 transform-and-conquer 201

6.1 presorting 202

exercises 6.1 205

6.2 gaussian elimination 208

lu decomposition 212

computing a matrix inverse 214

computing a determinant 215

exercises 6.2 216

6.3 balanced search trees 218

avl trees 218

2-3 trees 223

exercises 6.3 225

6.4 heaps and heapsort 226

notion of the heap 227

heapsort 231

exercises 6.4 233

6.5 horner’s rule and binary exponentiation 234

horner’s rule 234

binary exponentiation 236

exercises 6.5 239

6.6 problem reduction 240

computing the least common multiple 241

counting paths in a graph 242

reduction of optimization problems 243

linear programming 244

reduction to graph problems 246

exercises 6.6 248

summary 250

7 space and time trade-offs 253

7.1 sorting by counting 254

exercises 7.1 257

7.2 input enhancement in string matching 258

horspool’s algorithm 259

boyer-moore algorithm 263

exercises 7.2 267

7.3 hashing 269

open hashing (separate chaining) 270

closed hashing (open addressing) 272

exercises 7.3 274

7.4 b-trees 276

exercises 7.4 279

summary 280

8 dynamic programming 283

8.1 three basic examples 285

exercises 8.1 290

8.2 the knapsack problem and memory functions 292

memory functions 294

exercises 8.2 296

8.3 optimal binary search trees 297

exercises 8.3 303

8.4 warshall’s and floyd’s algorithms 304

warshall’s algorithm 304

floyd’s algorithm for the all-pairs shortest-paths problem 308

exercises 8.4 311

summary 312

9 greedy technique 315

9.1 prim’s algorithm 318

exercises 9.1 322

9.2 kruskal’s algorithm 325

disjoint subsets and union-find algorithms 327

exercises 9.2 331

9.3 dijkstra’s algorithm 333

exercises 9.3 337

9.4 huffman trees and codes 338

exercises 9.4 342

summary 344

10 iterative improvement 345

10.1 the simplex method 346

geometric interpretation of linear programming 347

an outline of the simplex method 351

further notes on the simplex method 357

exercises 10.1 359

10.2 the maximum-flow problem 361

exercises 10.2 371

10.3 maximum matching in bipartite graphs 372

exercises 10.3 378

10.4 the stable marriage problem 380

exercises 10.4 383

summary 384

11 limitations of algorithm power 387

11.1 lower-bound arguments 388

trivial lower bounds 389

information-theoretic arguments 390

adversary arguments 390

problem reduction 391

exercises 11.1 393

11.2 decision trees 394

decision trees for sorting 395

decision trees for searching a sorted array 397

exercises 11.2 399

11.3 p, np, and np-complete problems 401

p and np problems 402

np-complete problems 406

exercises 11.3 409

11.4 challenges of numerical algorithms 412

exercises 11.4 419

summary 420

12 coping with the limitations of algorithm power 423

12.1 backtracking 424

n-queens problem 425

hamiltonian circuit problem 426

subset-sum problem 427

general remarks 428

exercises 12.1 430

12.2 branch-and-bound 432

assignment problem 433

knapsack problem 436

traveling salesman problem 438

exercises 12.2 440

12.3 approximation algorithms for np-hard problems 441

approximation algorithms for the traveling salesman problem 443

approximation algorithms for the knapsack problem 453

exercises 12.3 457

12.4 algorithms for solving nonlinear equations 459

bisection method 460

method of false position 464

newton’s method 464

exercises 12.4 467

summary 468

epilogue 471

appendix a

useful formulas for the analysis of algorithms 475

properties of logarithms 475

combinatorics 475

important summation formulas 476

sum manipulation rules 476

approximation of a sum by a definite integral 477

floor and ceiling formulas 477

miscellaneous 477

appendix b

short tutorial on recurrence relations 479

sequences and recurrence relations 479

methods for solving recurrence relations 480

common recurrence types in algorithm analysis 485

references 493

hints to exercises 503

index 547

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