# Data Structures and Algorithm Analysis in Java (3rd Edition)

Language: English

Pages: 640

ISBN: 0132576279

Format: PDF / Kindle (mobi) / ePub

*Data Structures and Algorithm Analysis in Java *is an “advanced algorithms” book that fits between traditional CS2 and Algorithms Analysis courses. In the old ACM Curriculum Guidelines, this course was known as CS7. This text is for readers who want to learn good programming and algorithm analysis skills simultaneously so that they can develop such programs with the maximum amount of efficiency. Readers should have some knowledge of intermediate programming, including topics as object-based programming and recursion, and some background in discrete math.

As the speed and power of computers increases, so does the need for effective programming and algorithm analysis. By approaching these skills in tandem, Mark Allen Weiss teaches readers to develop well-constructed, maximally efficient programs in Java.

Weiss clearly explains topics from binary heaps to sorting to *NP*-completeness, and dedicates a full chapter to amortized analysis and advanced data structures and their implementation. Figures and examples illustrating successive stages of algorithms contribute to Weiss’ careful, rigorous and in-depth analysis of each type of algorithm. A logical organization of topics and full access to source code complement the text’s coverage.

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matter). This is generally true in these j N−1 kinds of problems. The precise analysis is obtained from the sum N−1 j=i i=0 k=i 1, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 /** * Cubic maximum contiguous subsequence sum algorithm. */ public static int maxSubSum1( int [ ] a ) { int maxSum = 0; for( int i = 0; i < a.length; i++ ) for( int j = i; j < a.length; j++ ) { int thisSum = 0; for( int k = i; k <= j; k++ ) thisSum += a[ k ]; if( thisSum > maxSum ) maxSum = thisSum; } return

list. Figure 3.24 shows the outline and partial implementation of the MyLinkedList class. We can see at line 3 the beginning of the declaration of the private nested Node class. Figure 3.25 shows the Node class, consisting of the stored item, links to the previous and next Node, and a constructor. All the data members are public. Recall that in a class, the data members are normally private. However, members in a nested class are visible even in the outer class. Since the Node class is private,

read and passed through to the output. The state of affairs at this juncture is as follows: + Stack ab Output Next a * is read. The top entry on the operator stack has lower precedence than *, so nothing is output and * is put on the stack. Next, c is read and output. Thus far, we have * + Stack abc Output 3.6 The Stack ADT The next symbol is a +. Checking the stack, we ﬁnd that we will pop a * and place it on the output; pop the other +, which is not of lower but equal priority, on the

4.29 Two binary search trees. Only the left tree is AVL Figure 4.30 Smallest AVL tree of height 9 S(h) is closely related to the Fibonacci numbers, from which the bound claimed above on the height of an AVL tree follows. Thus, all the tree operations can be performed in O(log N) time, except possibly insertion (we will assume lazy deletion). When we do an insertion, we need to update all the balancing information for the nodes on the path back to the root, but the reason that insertion is

Figure 4.35 Left–right double rotation to ﬁx case 2 k2 k1 k3 k1 k3 k2 A B D B A C D C Figure 4.36 Right–left double rotation to ﬁx case 3 exactly one of tree B or C is two levels deeper than D (unless all are empty), but we cannot be sure which one. It turns out not to matter; in Figure 4.35, both B and C are drawn at 1 12 levels below D. To rebalance, we see that we cannot leave k3 as the root, and a rotation between k3 and k1 was shown in Figure 4.34 to not work, so the only