1. Algorithm Cost Algorithm Complexity

2. Algorithm Cost

3. Back to Bunnies Recall that we calculated Fibonacci Numbers using two different techniques –Recursion –Iteration LB

4. Back to Bunnies Recursive calculation of Fibonacci Numbers: Fib(1) = 1 Fib(2) = 1 Fib(N) = Fib(N-1) + Fib(N-2) So: Fib(3) = Fib(2) + Fib(1) = 1 + 1 = 2 LB

5. Tree Recursion? f(n) f(n-1)f(n-2) f(n-3)f(n-4)f(n-3) f(n-4) f(n-5) f(n-4)f(n-5) f(n-6) LB

6. Tree Recursion Example f(6) f(5)f(4) f(3) f(2) f(3) f(2) f(1) f(2)f(1) f(2)f(1) LB

7. Recursively public static int fibR(int n) { if(n == 1 || n ==2) return 1; else return fibR(n-1) + fibR(n-2); } LB

8. Iteratively public static int fibI(int n) { int oldest = 1; int old = 1; int fib = 1; while(n-- > 2) { fib = old + oldest; oldest = old; old = fib; } return fib; } LB

9. Slight Modifications LB public static int fibR(int n) { if(n == 1 || n ==2) return 1; else return fibR(n-1) + fibR(n-2); } public static int fibR(int n) { if(n == 1 || n ==2) return 1; else return fibR(n-1) + fibR(n-2); } public static int fibI(int n) { int oldest = 1; int old = 1; int fib = 1; while(n-- > 2) { fib = old + oldest; oldest = old; old = fib; } return fib; } public static int fibI(int n) { int oldest = 1; int old = 1; int fib = 1; while(n-- > 2) { fib = old + oldest; oldest = old; old = fib; } return fib; } Add Counters

10. Demo LB

11. Conclusion Algorithm choice or design can make a big difference! LB

12. Correctness is Not Enough It isn’t sufficient that our algorithms perform the required tasks. We want them to do so efficiently, making the best use of –Space –Time

13. Time and Space Time –Instructions take time. –How fast does the algorithm perform? –What affects its runtime? Space –Data structures take space. –What kind of data structures can be used? –How does the choice of data structure affect the runtime?

14. Time vs. Space Very often, we can trade space for time: For example: maintain a collection of students’ with SSN information. –Use an array of a billion elements and have immediate access (better time) –Use an array of 35 elements and have to search (better space)

15. The Right Balance The best solution uses a reasonable mix of space and time. –Select effective data structures to represent your data model. –Utilize efficient methods on these data structures.

16. Questions?

17. Algorithm Complexity

18. Scenarios I’ve got two algorithms that accomplish the same task –Which is better? Given an algorithm, can I determine how long it will take to run? –Input is unknown –Don’t want to trace all possible paths of execution For different input, can I determine how an algorithm’s runtime changes?

19. Measuring the Growth of Work While it is possible to measure the work done by an algorithm for a given set of input, we need a way to: –Measure the rate of growth of an algorithm based upon the size of the input –Compare algorithms to determine which is better for the situation

20. Introducing Big O Will allow us to evaluate algorithms. Has precise mathematical definition We will use simplified version in CS 1311 Caution for the real world: Only tells part of the story! Used in a sense to put algorithms into families LB

21. Why Use Big-O Notation Used when we only know the asymptotic upper bound. If you are not guaranteed certain input, then it is a valid upper bound that even the worst- case input will be below. May often be determined by inspection of an algorithm. Thus we don’t have to do a proof!

22. Size of Input In analyzing rate of growth based upon size of input, we’ll use a variable –For each factor in the size, use a new variable –N is most common… Examples: –A linked list of N elements –A 2D array of N x M elements –A Binary Search Tree of P elements

23. Formal Definition For a given function g(n), O(g(n)) is defined to be the set of functions O(g(n)) = {f(n) : there exist positive constants c and n 0 such that 0  f(n)  cg(n) for all n  n 0 }

24. Visual O() Meaning f(n) cg(n) n0n0 f(n) = O(g(n)) Size of input Work done Our Algorithm Upper Bound

25. Simplifying O() Answers (Throw-Away Math!) We say 3n 2 + 2 = O(n 2 )  drop constants! because we can show that there is a n 0 and a c such that: 0  3n 2 + 2  cn 2 for n  n 0 i.e. c = 4 and n 0 = 2 yields: 0  3n 2 + 2  4n 2 for n  2

26. Correct but Meaningless You could say 3n 2 + 2 = O(n 6 ) or 3n 2 + 2 = O(n 7 ) But this is like answering: What’s the world record for the mile? –Less than 3 days. How long does it take to drive to Chicago? –Less than 11 years.

27. Comparing Algorithms Now that we know the formal definition of O() notation (and what it means)… If we can determine the O() of algorithms… This establishes the worst they perform. Thus now we can compare them and see which has the “better” performance.

28. Comparing Factors N log N N2N2 1 Size of input Work done

29. Correctly Interpreting O() O(1) or “Order One” –Does not mean that it takes only one operation –Does mean that the work doesn’t change as N changes –Is notation for “constant work” O(N) or “Order N” –Does not mean that it takes N operations –Does mean that the work changes in a way that is proportional to N –Is a notation for “work grows at a linear rate”

30. Complex/Combined Factors Algorithms typically consist of a sequence of logical steps/sections We need a way to analyze these more complex algorithms… It’s easy – analyze the sections and then combine them!

31. Example: Insert in a Sorted Linked List Insert an element into an ordered list… –Find the right location –Do the steps to create the node and add it to the list 1738142 head // Inserting 75 Step 1: find the location = O(N)

32. Example: Insert in a Sorted Linked List Insert an element into an ordered list… –Find the right location –Do the steps to create the node and add it to the list 1738142 head // Step 2: Do the node insertion = O(1) 75

33. Combine the Analysis Find the right location = O(N) Insert Node = O(1) Sequential, so add: –O(N) + O(1) = O(N + 1) = Only keep dominant factor O(N)

34. Example: Search a 2D Array Search an unsorted 2D array (row, then column) –Traverse all rows –For each row, examine all the cells (changing columns) Row Column 1234512345 1 2 3 4 5 6 7 8 9 10 O(N)

35. Example: Search a 2D Array Search an unsorted 2D array (row, then column) –Traverse all rows –For each row, examine all the cells (changing columns) Row Column 1234512345 1 2 3 4 5 6 7 8 9 10 O(M)

36. Combine the Analysis Traverse rows = O(N) –Examine all cells in row = O(M) Embedded, so multiply: –O(N) x O(M) = O(N*M)

37. Sequential Steps If steps appear sequentially (one after another), then add their respective O(). loop... endloop loop... endloop N M O(N + M)

38. Embedded Steps If steps appear embedded (one inside another), then multiply their respective O(). loop... endloop MN O(N*M)

39. Correctly Determining O() Can have multiple factors: –O(N*M) –O(logP + N 2 ) But keep only the dominant factors: –O(N + NlogN)  –O(N*M + P)  –O(V 2 + VlogV)  Drop constants: –O(2N + 3N 2 )  O(NlogN) remains the same O(V 2 )  O(N 2 ) O(N + N 2 )

40. Summary We use O() notation to discuss the rate at which the work of an algorithm grows with respect to the size of the input. O() is an upper bound, so only keep dominant terms and drop constants

41. Questions?