Download presentation

Presentation is loading. Please wait.

Published byLeroy Houle Modified over 2 years ago

1
Software Quality What are the important qualities of a good algorithm? · correctness · robustness · ease of implementation · readability and maintainability · efficiency What does this mean?

2
Measuring time & space An algorithm’s run time and/or memory requirements depend upon ____. · consider an algorithm to alphabetize the La Crosse phone book. · consider a program to print new license plates for WI. · consider an algorithm to print class lists for UW-L courses. What is the relationship between time & space? · consider detasseling corn · consider the tile arranging puzzle... 832 154 76 Time and space may have different characteristics.... However, the same approach is used to study both.

3
Counting the Cost (execution time) private int[][] mileage = {{0, 722, 2244, 800, 896}, {722, 0, 1047, 2113, 831}, {2244, 1047, 0, 1425, 1576}, {800, 2113, 1425, 0, 2849, {896, 831, 1576, 2849, 0} }; private int[][] mileage = {{0, 722, 2244, 800, 896}, {722, 0, 1047, 2113, 831}, {2244, 1047, 0, 1425, 1576}, {800, 2113, 1425, 0, 2849, {896, 831, 1576, 2849, 0} }; Example: Averaging a distance table algorithm: Average the cells under the diagonal.

4
Counting the Cost (execution time) Count the number of times each instruction executes. public int averageDistance(int[][] mileage) { int fromCity, toCity; int totalDistance = 0; fromCity = 0; while (fromCity != mileage.length) { toCity = fromCity + 1; while (toCity != mileage[].length) { totalDistance = totalDistance + mileage[fromCity][toCity]; toCity++; } fromCity++; } return totalDistance / (mileage.length*(mileage.length-1)/2); } public int averageDistance(int[][] mileage) { int fromCity, toCity; int totalDistance = 0; fromCity = 0; while (fromCity != mileage.length) { toCity = fromCity + 1; while (toCity != mileage[].length) { totalDistance = totalDistance + mileage[fromCity][toCity]; toCity++; } fromCity++; } return totalDistance / (mileage.length*(mileage.length-1)/2); } Let n denote mileage.length t j denote time to execute the j th instruction............................... ______..................................... ______.................... ______............................. ______................... _________ _________.................................. ________.................................... ____... ____ Count Ex. time = t 1 + t 2 + t 3 + (n+1)*t 4 + n*t 5 + (n*(n-1)/2+1)*t 6 + (n*(n-1)/2)*t 7 + (n*(n-1)/2)*t 8 + n*t 9 + t 10

5
execution time (sec.) 1 2 3 4 5 6 Estimating time & space Consider an algorithm to determine the BCS rankings. Such an algorithm depends largely upon the number of Division 1 football teams. Our analysis of the algorithm’s execution time is based on (1) an estimate of this time. (2) an estimate based upon “larger” numbers of teams. · · · · · Number of BCS teams (n) 4080120160200240280320360400440480 estimate(n)

6
Big-O Approximation Rather than worry about exact execution time, we count... · count the number of instructions executed · count the number of variable assignments · count the number of method calls · etc. · Let count(n) = the exact count for data amount, n · We select estimate(n) = an approximation with the following property for all j (j > c 1 ) [c 2 *estimate(j) ≥ count(j)], for some constants c 1, c 2 · For any such estimate it it proper to write O(estimate(n)) = count(n) stated “the big-O for count(n) is estimate(n)” but an approximation is sufficient

7
Example · What is countOfConditions(n)? -- assume that n is arr.length Defn: O(est(n)) means for all j (j > c 1 ) ______________________, for constants c 1, c 2 int ndx = 0; while (ndx != arr.length-1) { if (arr[ndx] < arr[ndx+1]) { temp = arr[ndx]; arr[ndx] = arr[ndx+1]; arr[ndx+1] = temp; } ndx++; } int ndx = 0; while (ndx != arr.length-1) { if (arr[ndx] < arr[ndx+1]) { temp = arr[ndx]; arr[ndx] = arr[ndx+1]; arr[ndx+1] = temp; } ndx++; } · What is maxCountOfAssignments(n)? · This algorithm is O( n )

8
Simplify, Simplify, Simplify The best Big-O functions have two properties: (1) they are simplified (2) they are a good approximation of function shape Simplification axioms O( c * f(n) ) = O( f(n) ), if c is a constant O( f(n) + g(n) ) = O( f(n) ), if f(n) dominates g(n) O( f(n) * g(n) ) = O(f(n)) * O(g(n)) Common Dominating functions (for variables n and m) O(log n) dominates O(1) O( n ) dominates O(log n) O( n a ) dominates O( n b ) when a > b O( n(log n) ) dominates O( n ) O( n 2 ) dominates O( n(log n) ) O( a n ) dominates O( n b ) for any constants a, b ≥ 2 O( a n ) dominates O( b n ) when a > b O( n! ) dominates O( a n ) for any constant a O( n m ) dominates O( n! ) constantlogarithmic linear polynomial exponential factorial

9
Simplify the following 9 * n 3 2 * n 5 + 3 * n 2 2 * n 10 + 3 n 8*n 2 * (3*n 3 + 2*n 2 + 5*n) 3*m 2 + 4*n 3 25

10
Function Growth Does functional growth really matter? 101123 214849 4216641681 836451225643046721 164256409665536(note 3) 3251024327684294967296 6464096262144(note 1) 1287163842097152(note 2) 25686553616777216 nlog 2 nn2n2 n3n3 2n2n 3n3n note 1 - roughly ta day of computation for a 1 petaflop supercomputer note 2 - about 50 billion times the age of the universe note 3 - a really BIG number.

11
Function Pix (linear scale)

12
Function Pix (logarithmic scale)

13
Function Pix (w/o factorial)

14
Code Analysis Most single instructions... O(1) x = a*b / 2; System.out.println( arr[3] ); Sequence of instructions... O(S 1 ) + O(S 2 ) +... + O(S n ) { temp = arr[j]; arr[j] = arr[k]; arr[k] = temp; } Loop... O(loopBody) * O(numberOfRepetitions) int j=0; while (j!=arr.length-1) { arr[j] = arr[j+1]; for (int k=0; k!=arr.length/2; k++) { temp = arr[k]; arr[k] = arr[arr.length-1-k]; arr[arr.length-1-k] = temp; } j++; }

15
More Code Analysis Selection... It’s a choice! if (condition) S 1 ; else S 2 ; in the worst case... maximum of O(S 1 ), O(S 2 ) in the best case... minimum of O(S 1 ), O(S 2 ) in the average case... expectations regarding S 1 and S 2 WARNINGS 1) Average case is generally difficult/impossible. 2) Best case is worst!

16
What is the Big-O? Assume that S j is O(1). for (int r=0; r!=w/2; r++) { S 1 ; for (int c=0; r!=w; r++) { S 2 ; } S 3 ; } int z = w; while (z > 0) { S 4 ; for (int r=w; r>3; r--) { S 5 ; } z = z / 2; }.................. O(1)................. O(1) ____ _______................ O(1).................. O(1)................. O(1).............. O(1) _________________ Overall: ______

17
/** post: forAll j:(0<=j

18
/** post: forAll j:(0<=j

Similar presentations

Presentation is loading. Please wait....

OK

COMP s1 Computing 2 Complexity

COMP s1 Computing 2 Complexity

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on ram and rom difference Ppt on limits and continuity review Ppt on producers consumers and decomposers 3rd Ppt on electric meter testing model Ppt on delhi election 2013 Ppt on social media analytics Ppt on case study of apple Ppt on different types of computer softwares for resume Ppt on hydroelectric plants in india Administrative law ppt on rule of law