A Practical Introduction to Data Structures and Algorithm Analysis

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Sec. 4.1 Lists 113curr... 23 12 ...Insert 10: 10curr(a)... 23 12 ...3101 2Figure 4.9 The linked list insertion process. (a) The linked list before insertion.(b) The linked list after insertion. 1 marks the element field of the new linknode. 2 marks the next field of the new link node, which is set to point to whatused to be the current node (the node with value 12). 3 marks the next field ofthe node preceding the current position. It used to point to the node containing 12;now it points to the new node containing 10.curr(b)...23 10 12(a)...curr2... 23 10 12 ...it1(b)Figure 4.10 The linked list removal process. (a) The linked list before removingthe node with value 10. (b) The linked list after removal. 1 marks the list nodebeing removed. it is set to point to the element. 2 marks the next field ofthe preceding list node, which is set to point to the node following the one beingdeleted.
114 Chap. 4 Lists, Stacks, and QueuesFigure 4.10 illustrates the remove method. Removing an element requires Θ(1)time.Method next simply moves curr one position toward the tail of the list,which takes Θ(1) time. Method prev moves curr one position toward the headof the list, but its implementation is more difficult. In a singly linked list, there isno pointer to the previous node. Thus, the only alternative is to march down the listfrom the beginning until we reach the current node (being sure always to rememberthe node before it, because that is what we really want). This takes Θ(n) time inthe average and worst cases. Implementation of method moveToPos is similar inthat finding the ith position requires marching down i positions from the head ofthe list, taking Θ(i) time.Implementations for the remaining operations are straightforward, each requiringΘ(1) time.FreelistsThe new operator is relatively expensive to use. Garbage collection is also expensive.Section 12.3 discusses how a general-purpose memory manager can beimplemented. The problem is that free-store routines must be capable of handlingrequests to and from free store with no particular pattern, as well as requests ofvastly different sizes.Most runtime environments today provide reasonable implementations for theirfree-store operators. However, the requirement that free-store managers be able tohandle any pattern of new operations, combined with unpredictable freeing ofspace by the garbage collector, makes them inefficient compared to what might beimplemented for more controlled patterns of memory access.The conditions under which list nodes are created and deleted in a linked listimplementation allow the Link class programmer to provide simple but efficientmemory management routines in place of the system-level free-store operators.Instead of making repeated calls to new, the Link class can handle its own freelist.A freelist holds those list nodes that are not currently being used. When a node isdeleted from a linked list, it is placed at the head of the freelist. When a newelement is to be added to a linked list, the freelist is checked to see if a list nodeis available. If so, the node is taken from the freelist. If the freelist is empty, thestandard new operator must then be called.Freelists are particularly useful for linked lists that periodically grow and thenshrink. The freelist will never grow larger than the largest size yet reached by thelinked list. Requests for new nodes (after the list has shrunk) can be handled by thefreelist.
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xviPrefacemake the examples as clea
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xviiiPrefaceOne of the most importa
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xxPrefaceOthers at Prentice Hall wh
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1Data Structures and AlgorithmsHow
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Sec. 1.1 A Philosophy of Data Struc
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Sec. 1.1 A Philosophy of Data Struc
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Sec. 1.2 Abstract Data Types and Da
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Sec. 1.3 Design Patterns 13Data Typ
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Sec. 1.4 Problems, Algorithms, and
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Sec. 1.5 Further Reading 194. It mu
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Sec. 1.6 Exercises 21If you want to
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2Mathematical PreliminariesThis cha
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Sec. 2.1 Sets and Relations 27examp
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Sec. 2.2 Miscellaneous Notation 29x
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Sec. 2.3 Logarithms 31positive inte
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Sec. 2.4 Summations and Recurrences
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Sec. 2.4 Summations and Recurrences
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Sec. 2.5 Recursion 37factorial of n
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Sec. 2.7 Estimating 47Example 2.17
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Sec. 2.8 Further Reading 49typical
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Sec. 2.9 Exercises 51(f) {〈2, 1
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Sec. 2.9 Exercises 532.17 Prove by
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Sec. 2.9 Exercises 552.38 When buyi
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3Algorithm AnalysisHow long will it
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Sec. 3.1 Introduction 59compiled wi
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Sec. 3.1 Introduction 61Example 3.3
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Sec. 5.3 Binary Tree Node Implement
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Sec. 5.4 Binary Search Trees 175/**
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Sec. 5.6 Huffman Coding Trees 189Le
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Sec. 5.6 Huffman Coding Trees 193/*
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Sec. 5.6 Huffman Coding Trees 195//
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Sec. 5.6 Huffman Coding Trees 197A
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Sec. 5.8 Exercises 199Many techniqu
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Sec. 5.9 Projects 203(d) The record
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6Non-Binary TreesMany organizations
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Sec. 6.7 Exercises 229CAFBEHGDIFigu
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Sec. 6.8 Projects 231proved perform
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7Internal SortingWe sort many thing
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Sec. 7.3 Shellsort 24559 20 17 13 2
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Sec. 7.5 Quicksort 249static
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Sec. 7.5 Quicksort 251static
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Sec. 7.5 Quicksort 253InitialPass 1
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Sec. 7.5 Quicksort 255The initial c
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Sec. 7.6 Heapsort 257and fast. The
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Sec. 7.7 Binsort and Radix Sort 259
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Sec. 7.9 Lower Bounds for Sorting 2
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Sec. 7.10 Further Reading 271• A
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Sec. 7.12 Projects 2757.16 (a) Devi
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Sec. 7.12 Projects 277classmates? W
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318 Chap. 9 SearchingThis and the f
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358 Chap. 10 Indexingfile is create
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PART IVAdvanced Data Structures387
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390 Chap. 11 Graphs04123147123(a)(b
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392 Chap. 11 Graphs0 1 2 3 40201 11
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394 Chap. 11 GraphsThe adjacency ma
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398 Chap. 11 Graphspublic int weigh
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410 Chap. 11 Graphs// Compute short
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PART VTheory of Algorithms479
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15Lower BoundsHow do I know if I ha
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Sec. 15.2 Lower Bounds on Searching
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Sec. 15.3 Finding the Maximum Value
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Sec. 15.4 Adversarial Lower Bounds
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Sec. 15.4 Adversarial Lower Bounds
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Sec. 15.5 State Space Lower Bounds
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Sec. 15.5 State Space Lower Bounds
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Sec. 15.6 Finding the ith Best Elem
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Sec. 15.7 Optimal Sorting 525search
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Sec. 15.8 Further Reading 527Merge
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16Patterns of AlgorithmsThis chapte
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Sec. 16.6 Projects 55716.8 What is
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594 BIBLIOGRAPHY[BG00] Sara Baase a
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596 BIBLIOGRAPHY[LLKS85] E.L. Lawle
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598 BIBLIOGRAPHY[WMB99][Zei07]I.H.
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600 INDEXbest fit, see memory manag
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602 INDEXrelation, 27, 50estimating
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604 INDEXinteger representation, 5,
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606 INDEXpath compression, 215-216,
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608 INDEXterminology, 236-237spatia
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A Practical Introduction to Data Structures and Algorithm Analysis

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