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  • 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
    图书信息来源: 互动出版网
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  • 原文地址:https://www.cnblogs.com/javawebsoa/p/3098123.html
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