LEADER 09327oam 2200553 450 001 9910153150703321 005 20211006014943.0 010 $a9781292014111 010 $a9780273764113 035 $a(MiAaPQ)EBC5173702 035 $a(MiAaPQ)EBC5175770 035 $a(MiAaPQ)EBC5138271 035 $a(MiAaPQ)EBC5832464 035 $a(MiAaPQ)EBC5483283 035 $a(Au-PeEL)EBL5138271 035 $a(CaONFJC)MIL645683 035 $a(OCoLC)1024263247 035 $a(PPN)204228506 035 $a(EXLCZ)992670000000568500 100 $a20210427d2012 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aIntroduction to the design & analysis of algorithms /$fAnany Levitin ; international edition contributions by Soumen Mukherjee, Aruf Kumar Bhattacharjee 205 $aThird edition. 210 1$aBoston :$cPearson,$d2012. 215 $a1 online resource (589 pages) $cillustrations 320 $aIncludes bibliographical references and index. 327 $aCover -- Title Page -- Contents -- New to the Third Edition -- Preface -- 1 Introduction -- 1.1 What Is an Algorithm? -- Exercises 1.1 -- 1.2 Fundamentals of Algorithmic Problem Solving -- Understanding the Problem -- Ascertaining the Capabilities of the Computational Device -- Choosing between Exact and Approximate Problem Solving -- Algorithm Design Techniques -- Designing an Algorithm and Data Structures -- Methods of Specifying an Algorithm -- Proving an Algorithm's Correctness -- Analyzing an Algorithm -- Coding an Algorithm -- Exercises 1.2 -- 1.3 Important Problem Types -- Sorting -- Searching -- String Processing -- Graph Problems -- Combinatorial Problems -- Geometric Problems -- Numerical Problems -- Exercises 1.3 -- 1.4 Fundamental Data Structures -- Linear Data Structures -- Graphs -- Trees -- Sets and Dictionaries -- Exercises 1.4 -- Summary -- 2 Fundamentals of the Analysis of Algorithm Efficiency -- 2.1 The Analysis Framework 68 -- Measuring an Input's Size -- Units for Measuring Running Time -- Orders of Growth -- Worst-Case, Best-Case, and Average-Case Efficiencies -- Recapitulation of the Analysis Framework -- Exercises 2.1 -- 2.2 Asymptotic Notations and Basic Efficiency Classes -- Informal Introduction -- O-notation -- ?-notation -- ?-notation -- Useful Property Involving the Asymptotic Notations -- Using Limits for Comparing Orders of Growth -- Basic Efficiency Classes -- Exercises 2.2 -- 2.3 Mathematical Analysis of Nonrecursive Algorithms -- Exercises 2.3 -- 2.4 Mathematical Analysis of Recursive Algorithms -- Exercises 2.4 -- 2.5 Example: Computing the nth Fibonacci Number -- Exercises 2.5 -- 2.6 Empirical Analysis of Algorithms -- Exercises 2.6 -- 2.7 Algorithm Visualization -- Summary -- 3 Brute Force and Exhaustive Search -- 3.1 Selection Sort and Bubble Sort -- Selection Sort -- Bubble Sort -- Exercises 3.1. 327 $a3.2 Sequential Search and Brute-Force String Matching -- Sequential Search -- Brute-Force String Matching -- Exercises 3.2 -- 3.3 Closest-Pair and Convex-Hull Problems by Brute Force -- Closest-Pair Problem -- Convex-Hull Problem -- Exercises 3.3 -- 3.4 Exhaustive Search -- Traveling Salesman Problem -- Knapsack Problem -- Assignment Problem -- Exercises 3.4 -- 3.5 Depth-First Search and Breadth-First Search -- Depth-First Search -- Breadth-First Search -- Exercises 3.5 -- Summary -- 4 Decrease-and-Conquer -- 4.1 Insertion Sort -- Exercises 4.1 -- 4.2 Topological Sorting -- Exercises 4.2 -- 4.3 Algorithms for Generating Combinatorial Objects -- Generating Permutations -- Generating Subsets -- Exercises 4.3 -- 4.4 Decrease-by-a-Constant-Factor Algorithms -- Binary Search -- Fake-Coin Problem -- Russian Peasant Multiplication -- Josephus Problem -- Exercises 4.4 -- 4.5 Variable-Size-Decrease Algorithms -- Computing a Median and the -- Interpolation Search -- Searching and Insertion in a Binary Search Tree -- The Game of Nim -- Exercises 4.5 -- Summary -- 5 Divide-and-Conquer -- 5.1 Mergesort -- Exercises 5.1 -- 5.2 Quicksort -- Exercises 5.2 -- 5.3 Binary Tree Traversals and Related Properties -- Exercises 5.3 -- 5.4 Multiplication of Large Integers and Strassen's Matrix Multiplication -- Multiplication of Large Integers -- Strassen's Matrix Multiplication -- Exercises 5.4 -- 5.5 The Closest-Pair and Convex-Hull Problems by Divide-and-Conquer -- The Closest-Pair Problem -- Convex-Hull Problem -- Exercises 5.5 -- Summary -- 6 Transform-and-Conquer -- 6.1 Presorting -- Exercises 6.1 -- 6.2 Gaussian Elimination -- LU Decomposition -- Computing a Matrix Inverse -- Computing a Determinant -- Exercises 6.2 -- 6.3 Balanced Search Trees -- AVL Trees -- 2-3 Trees -- Exercises 6.3 -- 6.4 Heaps and Heapsort -- Notion of the Heap -- Heapsort -- Exercises 6.4. 327 $a6.5 Horner's Rule and Binary Exponentiation -- Horner's Rule -- Binary Exponentiation -- Exercises 6.5 -- 6.6 Problem Reduction -- Computing the Least Common Multiple -- Counting Paths in a Graph -- Reduction of Optimization Problems -- Linear Programming -- Reduction to Graph Problems -- Exercises 6.6 -- Summary -- 7 Space and Time Trade-Offs -- 7.1 Sorting by Counting -- Exercises 7.1 -- 7.2 Input Enhancement in String Matching -- Horspool's Algorithm -- Boyer-Moore Algorithm -- Exercises 7.2 -- 7.3 Hashing -- Open Hashing (Separate Chaining) -- Closed Hashing (Open Addressing) -- Exercises 7.3 -- 7.4 B-Trees -- Exercises 7.4 -- Summary -- 8 Dynamic Programming -- 8.1 Three Basic Examples -- Exercises 8.1 -- 8.2 The Knapsack Problem and Memory Functions -- Memory Functions -- Exercises 8.2 -- 8.3 Optimal Binary Search Trees -- Exercises 8.3 -- 8.4 Warshall's and Floyd's Algorithms -- Warshall's Algorithm -- Floyd's Algorithm for the All-Pairs Shortest-Paths Problem -- Exercises 8.4 -- Summary -- 9 Greedy Technique -- 9.1 Prim's Algorithm -- Exercises 9.1 -- 9.2 Kruskal's Algorithm -- Disjoint Subsets and Union-Find Algorithms -- Exercises 9.2 -- 9.3 Dijkstra's Algorithm -- Exercises 9.3 -- 9.4 Huffman Trees and Codes -- Exercises 9.4 -- Summary -- 10 Iterative Improvement -- 10.1 The Simplex Method -- Geometric Interpretation of Linear Programming -- An Outline of the Simplex Method -- Further Notes on the Simplex Method -- Exercises 10.1 -- 10.2 The Maximum-Flow Problem -- Exercises 10.2 -- 10.3 Maximum Matching in Bipartite Graphs -- Exercises 10.3 -- 10.4 The Stable Marriage Problem -- Exercises 10.4 -- Summary -- 11 Limitations of Algorithm Power -- 11.1 Lower-Bound Arguments -- Trivial Lower Bounds -- Information-Theoretic Arguments -- Adversary Arguments -- Problem Reduction -- Exercises 11.1 -- 11.2 Decision Trees. 327 $aDecision Trees for Sorting -- Decision Trees for Searching a Sorted Array -- Exercises 11.2 -- 11.3 P, NP, and NP-Complete Problems -- P and NP Problems -- NP-Complete Problems -- Exercises 11.3 -- 11.4 Challenges of Numerical Algorithms -- Exercises 11.4 -- Summary -- 12 Coping with the Limitations of Algorithm Power -- 12.1 Backtracking -- n-Queens Problem -- Hamiltonian Circuit Problem -- Subset-Sum Problem -- General Remarks -- Exercises 12.1 -- 12.2 Branch-and-Bound -- Assignment Problem -- Knapsack Problem -- Traveling Salesman Problem -- Exercises 12.2 -- 12.3 Approximation Algorithms for NP-Hard Problems -- Approximation Algorithms for the Traveling Salesman Problem -- Approximation Algorithms for the Knapsack Problem -- Exercises 12.3 -- 12.4 Algorithms for Solving Nonlinear Equations -- Bisection Method -- Method of False Position -- Newton's Method -- Exercises 12.4 -- Summary -- Epilogue -- APPENDIX A -- Useful Formulas for the Analysis of Algorithms -- Properties of Logarithms -- Combinatorics -- Important Summation Formulas -- Sum Manipulation Rules -- Approximation of a Sum by a Definite Integral -- Floor and Ceiling Formulas -- Miscellaneous -- APPENDIX B -- Short Tutorial on Recurrence Relations -- Sequences and Recurrence Relations -- Methods for Solving Recurrence Relations -- Common Recurrence Types in Algorithm Analysis -- References -- Hints to Exercises -- Index -- Numbers and Symbols. 330 $aBased on a new classification of algorithm design techniques and a clear delineation of analysis methods, Introduction to the Design and Analysis of Algorithms presents the subject in a coherent and innovative manner. Written in a student-friendly style, the book emphasizes the understanding of ideas over excessively formal treatment while thoroughly covering the material required in an introductory algorithms course. Popular puzzles are used to motivate students' interest and strengthen their skills in algorithmic problem solving. Other learning-enhancement features include chapter summaries, hints to the exercises, and a detailed solution manual. 517 3 $aIntroduction to the design and analysis of algorithms 606 $aComputer algorithms 615 0$aComputer algorithms. 676 $a005.1 700 $aLevitin$b Anany$0601491 702 $aMukherjee$b Soumen 702 $aBhattacharjee$b Aruf Kumar 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910153150703321 996 $aIntroduction to the design & analysis of algorithms$92868127 997 $aUNINA LEADER 01205nam 2200397 450 001 9910151716203321 005 20230810001356.0 010 $a1-68123-687-7 035 $a(CKB)3710000000952404 035 $a(MiAaPQ)EBC4744488 035 $a(EXLCZ)993710000000952404 100 $a20161006h20172017 uy| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aHelping parents understand schools $ea different perspective on education and schooling in America /$fLyndon G. Furst, Andrews University 210 1$aCharlotte, NC :$cInformation Age Publishing, Incorporated,$d[2017] 210 4$dİ2017 215 $a1 online resource (233 pages) 311 $a1-68123-685-0 320 $aIncludes bibliographical references. 606 $aEducation$zUnited States 606 $aSchools$zUnited States 615 0$aEducation 615 0$aSchools 676 $a371.010973 700 $aFurst$b Lyndon G.$01244931 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910151716203321 996 $aHelping parents understand schools$92887723 997 $aUNINA