1. COM S 228 Algorithms and Analysis Instructor: Ying Cai Department of Computer Science Iowa State University yingcai@iastate.edu Office: Atanasoff 201

2. Algorithm An algorithm is a strategy for solving a problem, independent of the actual implementation

5. Which algorithms are faster Implement both algorithms and measure them with a stopwatch or count CPU cycles This approach has several problems The system may be running other applications, which share the same CPU Many other factors impact the speed  Memory swapping  Garbage collection Both must be implemented, which is often not practical It is unclear how the algorithms scale when you give a faster CPU

6. Time Complexity (Run Time) The time complexity of an algorithm is a function that describes the number of basic execution steps in terms of the input size We express time complexity using Big-O notation

7. Algorithm: Sequential Search Basic idea Pseudocode The running time of the sequential search depends on the particular values in A. If v is the first item, it takes one step; If v is not present, which is the worst case, it takes T(n) = 3n+3. The worst-case time complexity of this algorithm is O(n), or “big-O of N”

8. Notion of O(f(n))

11. Array Equality Revisited Algorithm 1 In the worst case, this algorithm needs to search the entire array B for A[i]

12. Array Equality Revisited

14. Algorithm Analysis in Practice In practice, algorithm analysis is seldom done at the level of detail as we have done so far

15. Example 1 void printAll(int[] array) { for (int i=0; i< array.length; i++) { System.out.println(array[i]); }

16. Example 2 // assuming array.length >= 1000 void sumDouble(double [] array) { double sum = 0.0; for (int i=0; i< 1000; i++) { sum = sum + i; }

17. Example 3 for (int i =0; i< array.length; i++) { method1(); // take O(n) method2(); // takes O(n2) }

18. Example 4

20. Practical Implication of Asymptotic Analysis

21. Computation Time