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A graph is a nonlinear structure. Like linear data structures (lists), it can be implemented with either an indexed or a linked backing data structure. SIMPLE GRAPHS A (simple) graph is a pair G = (V, E), where V and E are finite sets and every element of E is a two-element subset of V (i.e., an unordered pair of distinct elements of V). The elements of V are called vertices (or nodes), and the elements of E are called edges (or arcs). If e E, then e = {a, b} for some a, b V. In this case, we can denote e more simply as e = ab = ba. We say that the edge e connects the two vertices a and b, that e is incident with a and b, that a and b are incident upon e, that a and b are the terminal points or end points of the edge e, and that a and b are adjacent. The size of a graph is the number of elements in its vertex set. EXAMPLE 15.1 A Simple Graph

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The proof is similar to that for the corresponding theorems for the bubble sort and the selection sort. On the first iteration of the outer i loop, the inner j loop iterates once. On the second, it iterates once or twice, depending upon whether a[1] > a[2]. On the third iteration, the inner j loop iterates at most three times, again depending upon how many of the elements on the left of a[3] are greater than a[3]. This pattern continues, so that on the kth iteration of the outer loop, the inner loop iterates at most k times. Thus the maximum total number of iterations is 1 + 2 + + (n 1) = n(n 1)/2. In this case, the inner loop will iterate only once for each iteration of the outer loop. So the total number of iterations of the inner loop is: 1 + 1 + 1 + +1 + 1 = n 1. For the quick sort pivoting on the last element, the only changes needed are in the partition() method:

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The design of an FIR low-pass filter using the window design technique is simple and generally results in a filter with relatively good performance. However, in two respects, these filters are not optimal:

First, the passband and stopband deviations, 6, and 6,, are approximately equal. Although it is common to require S, to be much smaller than S, these parameters cannot be independently controlled in the , window design method. Therefore, with the window design method, it is necessary to overdesign the filter in the passband in order to satisfy the stricter requirements in the stopband. Second, for most windows, the ripple is not uniform in either the passband or the stopband and generally decreases when moving away from the transition band. Allowing the ripple to be uniformly distributed over the entire band would produce a smaller peak ripple.

Figure 15.1 shows a simple graph (V, E) of size 4. Its vertex set is V = {a, b, c, d}, and its edge set is E = {ab, ac, ad, bd, cd}. This graph has four vertices and five edges.

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private static int partition(int[] a, int p, int q) { int pivot = a[q-1], i = p-1, j = q-1; while (i < j) { while (i < j && a[++i] <= pivot) ; // empty loop if (i < j) { a[j] = a[i]; } while (j > i && a[--j] >= pivot) ; // empty loop if (j > i) { a[i] = a[j]; } } a[j] = pivot; return j; }

An equiripple linear phase filter, on the other hand, is optimal in the sense that the magnitude of the ripple is minimized in all bands of interest for a given filter order, N. In the following discussion, we consider the design of a type I linear phase filter. The results may be easily modified to design other types of linear phase filters. The frequency response of an FIR linear phase filter may be written as

where the amplitude. A(eJw), a real-valued function of w. For a type I linear phase filter, is h(n) = Iz(N - n) where N is an even integer. The symmetry of h(n) allows the frequency response to be expressed as

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