1 / 28

Data Structures and Algorithms CSCE 221

Data Structures and Algorithms CSCE 221. Dr. J. Michael Moore. Adapted from slides provided with the textbook, Nancy Amato, and Scott Schaefer. Syllabus. http://courses.cse.tamu.edu/jmichael/sp15/221/syllabus/. More on Homework. Turn in code/homeworks via eCampus

galindom
Download Presentation

Data Structures and Algorithms CSCE 221

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Structures and Algorithms CSCE 221 Dr. J. Michael Moore Adapted from slides provided with the textbook, Nancy Amato, and Scott Schaefer

  2. Syllabus • http://courses.cse.tamu.edu/jmichael/sp15/221/syllabus/

  3. More on Homework • Turn in code/homeworks via eCampus • Due by 11:59pm on day specified • All programming in C++ • Code, proj file, sln file, and Win32 executable • Make your code readable (comment) • You may discuss concepts, but coding is individual (no “team coding” or web)

  4. Labs • Several structured labs at the beginning of class with (simple) exercises • Graded on completion • Time to work on homework/projects

  5. Homework Approximately 5 Written/Typed responses Simple coding if any

  6. Programming Assignments About 5 throughout the semester Implementation of data structures or algorithms we discusses in class Written portion of the assignment

  7. Academic Honesty • Assignments are to be done on your own • May discuss concepts, get help with a persistent bug • Should not copy work, download code, or work together with other students unless specifically stated otherwise • We use a software similarity checker

  8. Class Discussion Board http://piazza.com/tamu/spring2015/csce221moore/home Sign up as a student

  9. Asymptotic Analysis 9/38

  10. Running Time • The running time of an algorithm typically grows with the input size. • Average case time is often difficult to determine. • We focus on the worst case running time. • Crucial to applications such as games, finance and robotics • Easier to analyze worst case 5ms 4ms Running Time 3ms best case 2ms 1ms A B C D E F G Input Instance 10/38

  11. Experimental Studies • Write a program implementing the algorithm • Run the program with inputs of varying size and composition • Use a method like clock() to get an accurate measure of the actual running time • Plot the results 11/38

  12. Limitations of Experiments • It is necessary to implement the algorithm, which may be difficult • Results may not be indicative of the running time on other inputs not included in the experiment. • In order to compare two algorithms, the same hardware and software environments must be used 12/38

  13. Theoretical Analysis • Uses a high-level description of the algorithm instead of an implementation • Characterizes running time as a function of the input size, n. • Takes into account all possible inputs • Allows us to evaluate the speed of an algorithm independent of the hardware/software environment 13/38

  14. Important Functions • Seven functions that often appear in algorithm analysis: • Constant  1 • Logarithmic  log n • Linear  n • N-Log-N  n log n • Quadratic  n2 • Cubic  n3 • Exponential  2n 14/38

  15. Important Functions • Seven functions that often appear in algorithm analysis: • Constant  1 • Logarithmic  log n • Linear  n • N-Log-N  n log n • Quadratic  n2 • Cubic  n3 • Exponential  2n 15/38

  16. Why Growth Rate Matters runtime quadruples when problem size doubles 16/38

  17. Comparison of Two Algorithms insertion sort is n2 / 4 merge sort is 2 n lg n sort a million items? insertion sort takes roughly 70 hours while merge sort takes roughly 40 seconds This is a slow machine, but if 100 x as fast then it’s 40 minutes versus less than 0.5 seconds 17/38

  18. Constant Factors • The growth rate is not affected by • constant factors or • lower-order terms • Examples • 102n+105is a linear function • 105n2+ 108nis a quadratic function 18/38

  19. Big-Oh Notation • Given functions f(n) and g(n), we say that f(n) is O(g(n)) if there are positive constantsc and n0 such that f(n)cg(n) for n n0 • Example: 2n+10 is O(n) • 2n+10cn • (c 2) n  10 • n  10/(c 2) • Pick c = 3 and n0 = 10 19/38

  20. Big-Oh Example • Example: the function n2is not O(n) • n2cn • n c • The above inequality cannot be satisfied since c must be a constant 20/38

  21. More Big-Oh Examples • 7n-2 7n-2 is O(n) need c > 0 and n0 1 such that 7n-2  c•n for n  n0 this is true for c = 7 and n0 = 1 • 3n3 + 20n2 + 5 3n3 + 20n2 + 5 is O(n3) need c > 0 and n0 1 such that 3n3 + 20n2 + 5  c•n3 for n  n0 this is true for c = 4 and n0 = 21 • 3 log n + 5 3 log n + 5 is O(log n) need c > 0 and n0 1 such that 3 log n + 5  c•log n for n  n0 this is true for c = 8 and n0 = 2 21/38

  22. 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 22/38

  23. Big-Oh Rules • If is f(n) a polynomial of degree d, then f(n) is O(nd), i.e., • Drop lower-order terms • Drop constant factors • Use the smallest possible class of functions • Say “2n is O(n)” instead of “2n is O(n2)” • Use the simplest expression of the class • Say “3n+5 is O(n)” instead of “3n+5 is O(3n)” 23/38

  24. Computing Prefix Averages • We further illustrate asymptotic analysis with two algorithms for prefix averages • The i-th prefix average of an array X is average of the first (i+ 1) elements of X: A[i]= (X[0] +X[1] +… +X[i])/(i+1) 24/38

  25. Prefix Averages (Quadratic) • The following algorithm computes prefix averages in quadratic time by applying the definition AlgorithmprefixAverages1(X, n) Input array X of n integers Output array A of prefix averages of X #operations A new array of n integers n fori 0 ton 1 do n s  X[0] n forj 1 toido 1 + 2 + …+ (n 1) s  s+X[j] 1 + 2 + …+ (n 1) A[i]  s/(i+ 1)n returnA 1 25/38

  26. The running time of prefixAverages1 isO(1 + 2 + …+ n) The sum of the first n integers is n(n+ 1) / 2 Thus, algorithm prefixAverages1 runs in O(n2) time Arithmetic Progression 26/38

  27. Prefix Averages (Linear) • The following algorithm computes prefix averages in linear time by keeping a running sum AlgorithmprefixAverages2(X, n) Input array X of n integers Output array A of prefix averages of X #operations A new array of n integers n s  0 1 fori 0 ton 1 do n s  s+X[i] n A[i]  s/(i+ 1)n returnA 1 • Algorithm prefixAverages2 runs in O(n) time 27/38

  28. Math you need to Review • Summations • Logarithms and Exponents • Proof techniques • Basic probability • properties of logarithms: logb(xy) = logbx + logby logb (x/y) = logbx - logby logbxa = alogbx logba = logxa/logxb • properties of exponentials: a(b+c) = aba c abc = (ab)c ab /ac = a(b-c) b = a logab bc = a c*logab

More Related