CS171:Introduction to Computer Science II. Algorithm Analysis. Li Xiong

Transcription

1 CS171:Introduction to Computer Science II Algorithm Analysis Li Xiong

2 Announcement/Reminders Hw3 due Friday Quiz 2 on October 17, Wednesday (after Spring break, based on class poll) Linked List, Algorithm Analysis

3 Algorithm Analysis Simple sorting Road Map

4 Algorithm Analysis An algorithm is a method for solving a problem expressed as a sequence of steps that is suitable for execution by a computer (machine) E.g. Search in an ordered array E.g. N-Queens problem We are interested in designing good algorithms Linear search vs. binary search Brute-force search vs. backtracking Good algorithms Running time Space usage (amount of memory required)

5 Running time of an algorithm Running time typically increases with the input size (problem size) Also affected by hardware and software environment We would like to focus on the relationship between the running time and the input size Input Algorithm Output

6 Experimental Studies Write a program implementing the algorithm 9000 Run the program with inputs 8000 of varying size and 7000 composition Use a method like 4000 System.currentTimeMillis()to get 3000 an accurate measure of the 2000 actual running time 1000 Plot the results E.g. Stopwatch.java Time (ms s) Input Size

7 Limitations of Experiments It is necessary to implement the algorithm, which may be difficult In order to compare two algorithms, the same hardware and software environments must be used Results may not be indicative of the running time on other inputs not included in the experiment.

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9 Algorithm Analysis -insight Total running time of a program is determined by two primary factors: Cost of executing each statement (property of computer, Java compiler, OS) Frequency of execution of each statement (property of program and input)

10 Algorithm analysis: Algorithm Analysis Determine frequency of primitive operations Characterizes it as a function of the input size A primitive operation: corresponds to a low-level (basic) computation with a constant execution time E.g. Evaluating an expression; assigning a value to a variable; indexing into an array Benefit: Takes into account all possible inputs independent of the hardware/software environment

11 Average-case vs. worst-case An algorithm may run faster on some inputs than it does on others (with the same input size) Average case: taking the average over all possible inputs of the same size Depends on input distribution Best case Worst case Easier analysis Typically leads to better algorithms

12 Misleading Average A statistician who put her head in the oven and her feet in the refrigerator. She said, On average, I feel just fine.

13 Misleading Average Statistician drowning in a pond with an average depth of 3ft

14 Loop Analysis Programs typically use loops to enumerate through input data items Count number of operations or steps in loops Each statement within the loop is counted as a step

15 Example1 double sum = 0.0; for (int i = 0; i < n; i ++) { sum += array[i]; } How many steps? Only count the loop statements(update to the loop variable i is ignored)

16 Example1 double sum = 0.0; for (int i = 0; i < n; i ++) { sum += array[i]; } How many steps? Only count the loop statements(update to the loop variable i is ignored) n

17 Example2 double sum = 0.0; for (int i = 0; i < n; i += 2) { sum += array[i]; } How many steps?

18 Example2 double sum = 0.0; for (int i = 0; i < n; i += 2) { sum += array[i]; } How many steps? Loops will be executed n/2 times: n/2

19 Example3 Multiple Loops for (int i = 0; i < n; i ++) { for (int j = 0; j < n; j ++) { int x = i*j; sum += x; } } How many steps?

20 Example3 Multiple Loops for (int i = 0; i < n; i ++) { for (int j = 0; j < n; j ++) { int x = i*j; sum += x; } } How many steps? Nested loops, each loop will be executed n times: n 2

21 Increase of Cost w.r.t. n Example 1: n Example 2: n/2 Example 3: 2n 2 What if n is 100? What if n is 3 times larger? Example 1 and 2 are linear to the input size Example 3 is quadratic to the input size

22 Mathematical notations for algorithm analysis The cost function can be complicated and lengthy mathematical expressions E.g. 3n n We careabout how thecost increasesw.r.t. theproblem size,rather than the absolute cost Use simplified mathematical notions Tilde notation Big O notation

23 Tilde Notation Tilde notation: ignore insignificant terms Definition: we write f(n) ~ g(n) if f(n)/g(n) approaches 1 as n grows 2n+10 ~ 2n 3n n ~ 3n 3

24 Big-Oh Notation Given functions f(n) and 10,000 g(n), we say that f(n) is O(g(n))if there are 1,000 positive constants cand n 0 such that n 2n+10 n f(n) cg(n) for n n 0 Example: 2n + 10is O(n) pick c = 3 and n 0 = ,000 n

25 Big-Oh Example Example: the function n 2 is not O(n) n 2 cn n c 1,000, ,000 10,000 1,000 The above inequality cannot 100 be satisfied since c 10 must be a constant 1 n^2 100n 10n n ,000 n

26 Big-Oh and Growth Rate The big-oh notation gives an upper bound on the growth rate of a function The statement f(n) is O(g(n)) means that the growth rate of f(n) is no more than the growth rate of g(n) We can use the big-oh notation to rank functions according to their growth rate

27 Important Functions in Big-Oh Analysis Constant: 1 Logarithmic: log n Linear: n N-Log-N: n log n Quadratic: n 2 Cubic: n 3 Polynomial: n d Exponential: 2 n Factorial: n!

28 Growth Rate Interms oftheorder: exponentials > polynomials > logarithms> constant. Increasing order n O(3 ) n O(2 ) 3 O(n ) 2 O(n ) O(nlogn) O(n) O(logn) O(1) Exponential Polynomial Log-linear Linear Log Constant

29 Big-Oh Analysis Write down cost function f(n) 1. Look for highest-order term (tilde notation) 2. Drop constant factors Examples 3n n n log n + 10

30 Useful Approximations Harmonic sum 1 + 1/2 + 1/ /N ~ lnn Triangular sum N = N(N+1)/2 ~ N 2 /2 Geometric sum N = 2N -1 ~ 2N when N = 2 n Stirling sapproximation lgn! = lg1 + lg2 + lg3 + + lgn~ NlgN