Data Structures: A Pseudocode Approach with C (2nd Edition) by Behrouz A. Forouzan, Richard F. Gilberg

By Behrouz A. Forouzan, Richard F. Gilberg

This moment version expands upon the forged, useful origin demonstrated within the first version of the textual content. a brand new four-part organizational constitution raises the flexibleness of the textual content, and all fabric is gifted in a simple demeanour observed by means of an array of examples and visible diagrams.

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Additional resources for Data Structures: A Pseudocode Approach with C (2nd Edition)

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Figure 1-7 shows the situation. void* p; int i; float f; p = &i; ... p = &f; p = &i p = &f p p i p f FIGURE 1-7 Pointers for Program 1-1 Program 1-1 uses a pointer to void that we can use to print either an integer or a floating-point number. Chapter 1 Basic Concepts 19 PROGRAM 1-1 Demonstrate Pointer to void 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 /* Demonstrate pointer to void. 500000 Program 1-1 Analysis The program is trivial, but it demonstrates the point. The pointer p is declared as a void pointer, but it can accept the address of an integer or floating-point number.

The big-O notation can be derived from f (n) using the following steps: 1. In each term, set the coefficient of the term to 1. Chapter 1 Basic Concepts 33 2. Keep the largest term in the function and discard the others. Terms are ranked from lowest to highest as shown below. logn n nlogn n2 n3 ... nk 2n n! For example, to calculate the big-O notation for (n + 1) 1 2 1 f ( n ) =n ----------------- = --- n + --- n 2 2 2 we first remove all coefficients. This gives us n2 + n which after removing the smaller factors gives us n2 which in big-O notation is stated as 2 O(f(n)) = O(n ) To consider another example, let’s look at the polynomial expression k f ( n ) = aj n + aj – 1 n k–1 2 + … + a2 n + a1 n + a0 We first eliminate all of the coefficients as shown below.

The general format is f (n) = efficiency The basic concepts are discussed in this section. 3. : Prentice Hall, 1988), xiii. Chapter 1 Basic Concepts 29 Linear Loops Let us start with a simple loop. We want to know how many times the body of the loop is repeated in the following code:4 for (i = 0; i < 1000; i++) application code Assuming i is an integer, the answer is 1000 times. The number of iterations is directly proportional to the loop factor, 1000. The higher the factor, the higher the number of loops.

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