《演算法設計與分析基礎(第3版 影印版)》基本資料原書名：Introduction to the Design and Analysis of Algorithms, Third Edition作者： (美)Anany Levitin出版社：清華大學出版社ISBN：9787302311850上架時間：2013-5-17出版日期：2013 年5月開本：16開頁碼：596版次：3-1所屬分類：電腦 > 電腦科學理論與基礎知識 > 計算理論 > 演算法更多關於 》》》《演算法設計與分析基礎(第3版 影印版)》內容簡介電腦書籍 《演算法設計與分析基礎(第3版 影印版)》在講述演算法設計技術時採用了新的分類方法，在討論分析方法時條分縷析，形成了連貫有序、耳目一新的風格。為便於學生掌握，本書涵蓋演算法入門課程的全部內容，更注重對概念(而非形式)的理解。書中通過一些流行的謎題來激發學生的興趣，協助他們加強和提高解決演算法問題的能力。每章小結、習題提示和詳細解答，形成了非常鮮明的教學特色。 《演算法設計與分析基礎(第3版 影印版)》特色： 獨闢蹊徑，採用一種更全面的演算法設計技術分類方法 涵蓋遞迴與非遞迴演算法的數學分析，也涉及經驗分析和演算法可視化 探討演算法的局限性及解決方案 將演算法視為解決問題的工具，通過謎題和遊戲來開拓演算法思維 為學生提供600多道習題(含提示)，為教師提供有詳細解答的教師手冊目錄《演算法設計與分析基礎(第3版 影印版)》new to the third edition xviipreface xix1introduction 11.1 what is an algorithm? 3exercises 1.1 71.2 fundamentals of algorithmic problem solving 9understanding the problem 9ascertaining the capabilities of the computational device 9choosing between exact and approximate problem solving 11algorithm design techniques 11designing an algorithm and data structures 12methods of specifying an algorithm 12proving an algorithm’s correctness 13analyzing an algorithm 14coding an algorithm 15exercises 1.2 171.3 important problem types 18sorting 19searching 20.string processing 20graph problems 21combinatorial problems 21geometric problems 22numerical problems 22exercises 1.3 231.4 fundamental data structures 25linear data structures 25graphs 28trees 31sets and dictionaries 35exercises 1.4 37summary 382 fundamentals of the analysis of algorithm efficiency 412.1 the analysis framework 42measuring an input’s size 43units for measuring running time 44orders of growth 45worst-case, best-case, and average-case efficiencies 47recapitulation of the analysis framework 50exercises 2.1 502.2 asymptotic notations and basic efficiency classes 52informal introduction 52o-notation 53-notation 54-notation 55useful property involving the asymptotic notations 55using limits for comparing orders of growth 56basic efficiency classes 58exercises 2.2 582.3 mathematical analysis of nonrecursive algorithms 61exercises 2.3 672.4 mathematical analysis of recursive algorithms 70exercises 2.4 762.5 example: computing the nth fibonacci number 80exercises 2.5 832.6 empirical analysis of algorithms 84exercises 2.6 892.7 algorithm visualization 91summary 943 brute force and exhaustive search 973.1 selection sort and bubble sort 98selection sort 98bubble sort 100exercises 3.1 1023.2 sequential search and brute-force string matching 104sequential search 104brute-force string matching 105exercises 3.2 1063.3 closest-pair and convex-hull problems by brute force 108closest-pair problem 108convex-hull problem 109exercises 3.3 1133.4 exhaustive search 115traveling salesman problem 116knapsack problem 116assignment problem 119exercises 3.4 1203.5 depth-first search and breadth-first search 122depth-first search 122breadth-first search 125exercises 3.5 128summary 1304 decrease-and-conquer 1314.1 insertion sort 134exercises 4.1 1364.2 topological sorting 138exercises 4.2 1424.3 algorithms for generating combinatorial objects 144generating permutations 144generating subsets 146exercises 4.3 1484.4 decrease-by-a-constant-factor algorithms 150binary search 150fake-coin problem 152russian peasant multiplication 153josephus problem 154exercises 4.4 1564.5 variable-size-decrease algorithms 157computing a median and the selection problem 158interpolation search 161searching and insertion in a binary search tree 163the game of nim 164exercises 4.5 166summary 1675 divide-and-conquer 1695.1 mergesort 172exercises 5.1 1745.2 quicksort 176exercises 5.2 1815.3 binary tree traversals and related properties 182exercises 5.3 1855.4 multiplication of large integers andstrassen’s matrix multiplication 186multiplication of large integers 187strassen’s matrix multiplication 189exercises 5.4 1915.5 the closest-pair and convex-hull problemsby divide-and-conquer 192the closest-pair problem 192convex-hull problem 195exercises 5.5 197summary 1986 transform-and-conquer 2016.1 presorting 202exercises 6.1 2056.2 gaussian elimination 208lu decomposition 212computing a matrix inverse 214computing a determinant 215exercises 6.2 2166.3 balanced search trees 218avl trees 2182-3 trees 223exercises 6.3 2256.4 heaps and heapsort 226notion of the heap 227heapsort 231exercises 6.4 2336.5 horner’s rule and binary exponentiation 234horner’s rule 234binary exponentiation 236exercises 6.5 2396.6 problem reduction 240computing the least common multiple 241counting paths in a graph 242reduction of optimization problems 243linear programming 244reduction to graph problems 246exercises 6.6 248summary 2507 space and time trade-offs 2537.1 sorting by counting 254exercises 7.1 2577.2 input enhancement in string matching 258horspool’s algorithm 259boyer-moore algorithm 263exercises 7.2 2677.3 hashing 269open hashing (separate chaining) 270closed hashing (open addressing) 272exercises 7.3 2747.4 b-trees 276exercises 7.4 279summary 2808 dynamic programming 2838.1 three basic examples 285exercises 8.1 2908.2 the knapsack problem and memory functions 292memory functions 294exercises 8.2 2968.3 optimal binary search trees 297exercises 8.3 3038.4 warshall’s and floyd’s algorithms 304warshall’s algorithm 304floyd’s algorithm for the all-pairs shortest-paths problem 308exercises 8.4 311summary 312
