1. Chapter 10 A Algorithm Efficiency

2. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-2 Determining the Efficiency of Algorithms Analysis of algorithms –Provides tools for contrasting the efficiency of different methods of solution A comparison of algorithms –Should focus of significant differences in efficiency –Should not consider reductions in computing costs due to clever coding tricks

3. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-3 Determining the Efficiency of Algorithms Three difficulties with comparing programs instead of algorithms –How are the algorithms coded? –What computer should you use? –What data should the programs use? Algorithm analysis should be independent of –Specific implementations –Computers –Data

4. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-4 The Execution Time of Algorithms Counting an algorithm's operations is a way to access its efficiency –An algorithm’s execution time is related to the number of operations it requires –Examples Traversal of a linked list The Towers of Hanoi Nested Loops

5. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-5 Algorithm Growth Rates An algorithm’s time requirements can be measured as a function of the problem size An algorithm’s growth rate –Enables the comparison of one algorithm with another –Examples Algorithm A requires time proportional to n 2 Algorithm B requires time proportional to n Algorithm efficiency is typically a concern for large problems only

6. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-6 Algorithm Growth Rates Figure 10.1 Time requirements as a function of the problem size n

7. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-7 Order-of-Magnitude Analysis and Big O Notation Definition of the order of an algorithm Algorithm A is order f(n) – denoted O(f(n)) – if constants k and n 0 exist such that A requires no more than k * f(n) time units to solve a problem of size n ≥ n 0 Growth-rate function –A mathematical function used to specify an algorithm’s order in terms of the size of the problem Big O notation –A notation that uses the capital letter O to specify an algorithm’s order –Example: O(f(n))

8. © 2004 Pearson Addison-Wesley. All rights reserved10 A-8

9. © 2004 Pearson Addison-Wesley. All rights reserved10 A-9

10. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-10 Order-of-Magnitude Analysis and Big O Notation Figure 10.3a A comparison of growth-rate functions: a) in tabular form

11. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-11 Order-of-Magnitude Analysis and Big O Notation Figure 10.3b A comparison of growth-rate functions: b) in graphical form

12. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-12 Order-of-Magnitude Analysis and Big O Notation Order of growth of some common functions O(1) < O(log 2 n) < O(n) < O(n * log 2 n) < O(n 2 ) < O(n 3 ) < O(2 n ) Properties of growth-rate functions –You can ignore low-order terms –You can ignore a multiplicative constant in the high-order term –O(f(n)) + O(g(n)) = O(f(n) + g(n))

13. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-13 Order-of-Magnitude Analysis and Big O Notation Worst-case and average-case analyses –An algorithm can require different times to solve different problems of the same size Worst-case analysis –A determination of the maximum amount of time that an algorithm requires to solve problems of size n Average-case analysis –A determination of the average amount of time that an algorithm requires to solve problems of size n

14. Algorithm Growth Rate Analysis 14 l Sequential Statements – If the orders of code segments p 1 and p 2 are f 1 (n) and f 2 (n), respectively, then the order of p 1 ;p 2 = O(f 1 (n) + f 2 (n)) = O(max(f 1 (n), f 2 (n))) l For Loops (simple ones) – Use  to figure out total number of iterations – Shortcut l While Loops – No particular way, might be able to count l Recursion – Recurrence relation

15. 15 Important Recurrences l Constant operation reduces problem size by one  T n = T n-1 + c Solution: T n = O(n)  linear  Examples: find largest version 2 and linear search  find_max2(myArray, n) // finds largest value in myArray of size n if (n == 1) return myArray[0]; x = find_max2(myArray, n-1); if (myArray[n-1] > x) return myArray[n-1]; else return x;  linear_search(myArray, n, key) // returns index of key if found in myArray of size n, -1 otherwise if (n == 0) return -1; index = linear_search(myArray, n-1, key); if (index != -1) return index; if (myArray[n-1] == key) return n-1; else return -1;

16. 16 Important Recurrences l A pass through input reduces problem size by one  T n = T n-1 + n (or T n = T n-1 + (n – 1)) Solution: T n = O(n 2 )  quadratic  Example: insertion sort  insertion_sort(myArray, n) // sort myArray of size n if (n == 1) return; insertion_sort(myArray, n-1) find the right position to insert myArray[n-1] // it takes O(n) l Constant operation divides problem into two subproblems of half-size  T n = 2T n/2 + c Solution: T n = O(n)  linear  Example: find largest version 1  find_max1(myArray, i, j) // find largest value in myArray[i.. j] if (i == j) return myArray[i]; x = find_max2(myArray, i, (i+j)/2); y = find_max2(myArray, (i+j)/2+1, j); return (x > y)? x : y;

17. 17 Important Recurrences l Constant operation reduces problem size by half  T n = T n/2 + c Solution: T n = O(lg n)  logarithmic  Example: binary search l A pass through input divides problem into 2 subproblems of input size reduced by half  T n = 2T n/2 + n Solution: T n = O(n lg n)  n log n  Example: merge sort

18. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-18 Keeping Your Perspective Throughout the course of an analysis, keep in mind that you are interested only in significant differences in efficiency When choosing an ADT’s implementation, consider how frequently particular ADT operations occur in a given application Some seldom-used but critical operations must be efficient

19. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-19 Keeping Your Perspective If the problem size is always small, you can probably ignore an algorithm’s efficiency Weigh the trade-offs between an algorithm’s time requirements and its memory requirements Compare algorithms for both style and efficiency Order-of-magnitude analysis focuses on large problems

20. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-20 The Efficiency of Searching Algorithms Sequential search –Strategy Look at each item in the data collection in turn, beginning with the first one Stop when –You find the desired item –You reach the end of the data collection

21. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-21 The Efficiency of Searching Algorithms Sequential search –Efficiency Worst case: O(n) Average case: O(n) Best case: O(1)

22. © 2004 Pearson Addison-Wesley. All rights reserved 10 A-22 The Efficiency of Searching Algorithms Binary search –Strategy To search a sorted array for a particular item –Repeatedly divide the array in half –Determine which half the item must be in, if it is indeed present, and discard the other half –Efficiency Worst case: O(log 2 n) For large arrays, the binary search has an enormous advantage over a sequential search