1. CSC – 332 Data Structures Generics Analysis of Algorithms Dr. Curry Guinn

2. For Next Class, Thursday Homework 1 due tonight –Quiz 2 – Today, 01/21, before class!Quiz 2 Up to 3 submissions –Quiz 3 Thursday by class timeQuiz 3 Thursday by class time Homework 2 due Monday, 01/27, 11:59pm For Thursday –Chapter 2, Sections 2.1-2.4.2

3. Quiz Answers Let’s go to Blackboard Learn and see

4. Generics http://people.uncw.edu/guinnc/courses/spri ng14/332/notes/day2_unix/Generics.ppthttp://people.uncw.edu/guinnc/courses/spri ng14/332/notes/day2_unix/Generics.ppt

5. Is This Algorithm Fast? Problem: given a problem, how fast does this code solve that problem? "My program finds all the primes between 2 and 1,000,000,000 in 1.37 seconds." –How good is this solution? Could try to measure the time it takes, but that is subject to lots of errors –multitasking operating system –speed of computer –language solution is written in

6. Math background: exponents Exponents –X Y, or "X to the Y th power"; X multiplied by itself Y times Some useful identities –X A X B = X A+B –X A / X B = X A-B –(X A ) B = X AB –X N +X N = 2X N –2 N +2 N = 2 N+1

7. Math background:Logarithms Logarithms –definition: X A = B if and only if log X B = A –intuition: log X B means: "the power X must be raised to, to get B" –In this course, a logarithm with no base implies base 2. log B means log 2 B Examples –log 2 16 = 4(because 2 4 = 16) –log 10 1000 = 3(because 10 3 = 1000)

8. Identities for logs with addition, multiplication, powers: log (AB) = log A + log B log (A/B) = log A – log B log (A B ) = B log A log A (B) = log C (B)/log C (A) Logarithm identities

9. Some helpful mathematics N + N + N + …. + N (total of N times) –N*N = N 2 which is O(N 2 ) 1 + 2 + 3 + 4 + … + N –N(N+1)/2 = N 2 /2 + N/2 is O(N 2 )

10. Analysis of Algorithms What do we mean by an “efficient” algorithm? –We mean an algorithm that uses few resources. –By far the most important resource is time. –Thus, when we say an algorithm is efficient (assuming we do not qualify this further), we mean that it can be executed quickly.

11. Is there some way to measure efficiency that does not depend on the state of current technology? –Yes! The Idea –Determine the number of “steps” an algorithm requires when given some input. We need to define “step” in some reasonable way. –Write this as a formula, based on the input.

12. Generally, when we determine the efficiency of an algorithm, we are interested in: –Time Used by the Algorithm Expressed in terms of number of steps. People also talk about “space efficiency”, etc. –How the Size of the Input Affects Running Time Think about giving an algorithm a list of items to operate on. The size of the problem is the length of the list. –Worst-Case Behavior What is the slowest the algorithm ever runs for a given input size? Occasionally we also analyze average-case behavior.

13. Typically use a simple model for basic operation costs RAM (Random Access Machine) model –RAM model has all the basic operations: +, -, *, /, =, comparisons –fixed sized integers (e.g., 32-bit) –infinite memory –All basic operations take exactly one time unit (one CPU instruction) to execute RAM model

14. Critique of the model Strengths: –simple –easier to prove things about the model than the real machine –can estimate algorithm behavior on any hardware/software Weaknesses: –not all operations take the same amount of time in a real machine –does not account for page faults, disk accesses, limited memory, floating point math, etc

15. Relative rates of growth Most algorithms' runtime can be expressed as a function of the input size N Rate of growth: measure of how quickly the graph of a function rises Goal: distinguish between fast- and slow- growing functions –We only care about very large input sizes (for small sizes, most any algorithm is fast enough)

16. Growth rate example Consider these graphs of functions. Perhaps each one represents an algorithm: n 3 + 2n 2 100n 2 + 1000 Which grows faster?

17. Growth rate example How about now?

18. “The fundamental law of computer science: As machines become more powerful, the efficiency of algorithms grows more important, not less.” — Nick Trefethen An algorithm (or function or technique …) that works well when used with large problems & large systems is said to be scalable. –Or “it scales well”.

19. Big O Definition: T(N) = O(f(N)) if there exist positive constants c, n 0 such that: T(N)  c · f(N) for all N  n 0 Idea: We are concerned with how the function grows when N is large. We are not picky about constant factors: coarse distinctions among functions Lingo: "T(N) grows no faster than f(N)."

20. Big O  c, n 0 > 0 such that f(N)  c g(N) when N  n 0 f(N) grows no faster than g(N) for “large” N

21. pick tightest bound. If f(N) = 5N, then: f(N) = O(N 5 ) f(N) = O(N 3 ) f(N) = O(N log N) f(N) = O(N)  preferred ignore constant factors and low order terms T(N) = O(N), not T(N) = O(5N) T(N) = O(N 3 ), not T(N) = O(N 3 + N 2 + N log N) remove non-base-2 logarithms f(N) = O(N log 6 N) f(N) = O(N log N)  preferred Preferred big-O usage

22. Big-O of selected functions

23. Defn: T(N) =  (g(N)) if there are positive constants c and n 0 such that T(N)  c g(N) for all N  n 0 –Lingo: "T(N) grows no slower than g(N)." Defn: T(N) =  (g(N)) if and only if T(N) = O(g(N)) and T(N) =  (g(N)). –Big-O, Omega, and Theta establish a relative ordering among all functions of N Big Omega, Theta

24. notationintuition O (Big-O)   (Big-Omega)   (Theta) = o (little-O) < Intuition about the notations

25. Big-Omega  c, n 0 > 0 such that f(N)  c g(N) when N  n 0 f(N) grows no slower than g(N) for “large” N

26. Big Theta: f(N) =  (g(N)) the growth rate of f(N) is the same as the growth rate of g(N)

27. An O(1) algorithm is constant time. –The running time of such an algorithm is essentially independent of the input. –Such algorithms are rare, since they cannot even read all of their input. An O(log b n) [for some b] algorithm is logarithmic time. –We do not care what b is. An O(n) algorithm is linear time. –Such algorithms are not rare. –This is as fast as an algorithm can be and still read all of its input. An O(n log b n) [for some b] algorithm is log-linear time. –This is about as slow as an algorithm can be and still be truly useful (scalable). An O(n 2 ) algorithm is quadratic time. –These are usually too slow. An O(b n ) [for some b] algorithm is exponential time. –These algorithms are much too slow to be useful.

28. T(N) = O(f(N)) –f(N) is an upper bound on T(N) –T(N) grows no faster than f(N) T(N) =  (g(N)) –g(N) is a lower bound on T(N) –T(N) grows at least as fast as g(N) T(N) = o(h(N)) (little-O) –T(N) grows strictly slower than h(N) Hammerin’ the terminolgy

29. Asymptotically less than or equal to O (Big-O) Asymptotically greater than or equal to  (Big-Omega) Asymptotically equal to  (Big-Theta) Asymptotically strictly less o (Little-O) Notations

30. Facts about big-O If T(N) is a polynomial of degree k, then: T(N) =  (N k ) –example: 17n 3 + 2n 2 + 4n + 1 =  (n 3 )

31. Hierarchy of Big-O Functions, ranked in increasing order of growth: –1 –log log n –log n –n –n log n –n 2 –n 2 log n –n 3... –2 n –n! –n n

32. Various growth rates

33. Evaluate: limit isBig-Oh relation 0f(N) = o(g(N)) c  0f(N) =  (g(N))  g(N) = o(f(N)) no limitno relation Techniques for Determining Which Grows Faster

34. L'Hôpital's rule: If and, then example: f(N) = N, g(N) = log N Use L'Hôpital's rule f'(N) = 1, g'(N) = 1/N  g(N) = o(f(N)) Techniques, cont'd

35. for (int i = 0; i < n; i += c) // O(n) statement(s); Adding to the loop counter means that the loop runtime grows linearly when compared to its maximum value n. –Loop executes its body exactly n / c times. Program loop runtimes

36. for (int i = 0; i < n; i *= c) // O(log n) statement(s); Multiplying the loop counter means that the maximum value n must grow exponentially to linearly increase the loop runtime; therefore, it is logarithmic. –Loop executes its body exactly log c n times.

37. for (int i = 0; i < n * n; i += c) // O(n 2 ) statement(s); The loop maximum is n 2, so the runtime is quadratic. –Loop executes its body exactly (n 2 / c) times.

38. Nesting loops multiplies their runtimes. for (int i = 0; i < n; i += c) { //O(n 2 ) for (int j = 0; j < n; i += c) { statement; } More loop runtimes

39. Loops in sequence add together their runtimes, which means the loop set with the larger runtime dominates. for (int i = 0; i < n; i += c) { // O(n) statement; } // O(nlog n) for (int i = 0; i < n; i += c) { for (int j = 0; j < n; i *= c) { statement; }

40. Express the running time as f(N), where N is the size of the input worst case: your enemy gets to pick the input average case: need to assume a probability distribution on the inputs Types of runtime analysis

41. Some rules When considering the growth rate of a function using Big-O Ignore the lower order terms and the coefficients of the highest-order term No need to specify the base of logarithm –Changing the base from one constant to another changes the value of the logarithm by only a constant factor If T 1 (N) = O(f(N) and T 2 (N) = O(g(N)), then –T 1 (N) + T 2 (N) = max(O(f(N)), O(g(N))), –T 1 (N) * T 2 (N) = O(f(N) * g(N))

42. For Next Class, Thursday Homework 1 due tonight –Quiz 2 – Today, 01/21, before class!Quiz 2 Up to 3 submissions –Quiz 3 Thursday by class timeQuiz 3 Thursday by class time Homework 2 due Monday, 01/27, 11:59pm For Thursday –Chapter 2, Sections 2.1-2.4.2