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COSC 3101A - Design and Analysis of Algorithms 2

COSC 3101A - Design and Analysis of Algorithms 2. Asymptotic Notations Continued Proof of Correctness: Loop Invariant Designing Algorithms: Divide and Conquer. Typical Running Time Functions. 1 (constant running time): Instructions are executed once or a few times logN (logarithmic)

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COSC 3101A - Design and Analysis of Algorithms 2

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  1. COSC 3101A - Design and Analysis of Algorithms2 Asymptotic Notations Continued Proof of Correctness: Loop Invariant Designing Algorithms: Divide and Conquer

  2. Typical Running Time Functions • 1 (constant running time): • Instructions are executed once or a few times • logN (logarithmic) • A big problem is solved by cutting the original problem in smaller sizes, by a constant fraction at each step • N (linear) • A small amount of processing is done on each input element • N logN • A problem is solved by dividing it into smaller problems, solving them independently and combining the solution COSC3101A

  3. Typical Running Time Functions • N2 (quadratic) • Typical for algorithms that process all pairs of data items (double nested loops) • N3(cubic) • Processing of triples of data (triple nested loops) • NK (polynomial) • 2N (exponential) • Few exponential algorithms are appropriate for practical use COSC3101A

  4. Logarithms • In algorithm analysis we often use the notation “log n” without specifying the base Binary logarithm Natural logarithm COSC3101A

  5. Review: Asymptotic Notations(1) COSC3101A

  6. Review: Asymptotic Notations(2) if and only if if and only if COSC3101A

  7. Review: Asymptotic Notations(3) • A way to describe behavior of functions in the limit • How we indicate running times of algorithms • Describe the running time of an algorithm as n grows to  • O notation: asymptotic “less than”: f(n) “≤” g(n) •  notation: asymptotic “greater than”: f(n) “≥” g(n) •  notation: asymptotic “equality”: f(n) “=” g(n) COSC3101A

  8. Big-O Examples(1) • 2n2 = O(n3): • n2 = O(n2): • 1000n2+1000n = O(n2): • n = O(n2): 2n2≤ cn3  2 ≤ cn  c = 1 and n0= 2 n2≤ cn2  c ≥ 1  c = 1 and n0= 1 1000n2+1000n ≤ cn2 1000n+1000≤ cn  c=2000 and n0 = 1 n ≤ cn2  cn ≥ 1  c = 1 and n0= 1 COSC3101A

  9. Big-O Examples(2) • E.g.:prove that n2≠ O(n) • Assume  c & n0 such that:  n≥ n0: n2 ≤ cn • Choose n = max (n0, c) • n2 = n  n ≥ n c  n2 ≥ cn contradiction!!! COSC3101A

  10. More on Asymptotic Notations • There is no unique set of values for n0 and c in proving the asymptotic bounds • Prove that 100n + 5 = O(n2) • 100n + 5 ≤ 100n + n = 101n ≤ 101n2 for all n ≥ 5 n0 = 5 and c = 101is a solution • 100n + 5 ≤ 100n + 5n = 105n ≤ 105n2for all n ≥ 1 n0 = 1 and c = 105is also a solution Must findSOMEconstants c and n0 that satisfy the asymptotic notation relation COSC3101A

  11. Big- Examples • 5n2 = (n) • 100n + 5 ≠(n2) • n = (2n), n3 = (n2), n = (logn)  cn  5n2  c = 1 and n0 = 1 •  c, n0such that: 0  cn  5n2  c, n0 such that: 0  cn2  100n + 5 100n + 5  100n + 5n ( n  1) = 105n cn2  105n  n(cn – 105)  0  n  105/c Since n is positive  cn – 105  0  contradiction: n cannot be smaller than a constant COSC3101A

  12.  Examples • n2/2 –n/2 = (n2) • ½ n2 - ½ n ≤ ½ n2n ≥ 0  c2= ½ • ½ n2 - ½ n ≥ ½ n2 - ½ n * ½ n ( n ≥ 2 ) = ¼ n2 c1= ¼ • n ≠ (n2): c1 n2≤ n ≤ c2 n2 only holds for: n ≤ 1/c1 • 6n3 ≠ (n2): c1 n2≤ 6n3 ≤ c2 n2 only holds for: n ≤ c2 /6 • n ≠ (logn): c1logn≤ n ≤ c2 logn  c2 ≥ n/logn,  n≥ n0 – impossible COSC3101A

  13. Comparisons of Functions • Theorem: f(n) = (g(n))  f = O(g(n)) and f = (g(n)) • Transitivity: • f(n) = (g(n))andg(n) = (h(n))  f(n) = (h(n)) • Same for O and  • Reflexivity: • f(n) = (f(n)) • Same for O and  • Symmetry: • f(n) = (g(n)) if and only if g(n) = (f(n)) • Transpose symmetry: • f(n) = O(g(n)) if and only if g(n) = (f(n)) COSC3101A

  14. More Examples(1) • For each of the following pairs of functions, either f(n) is O(g(n)), f(n) is Ω(g(n)), or f(n) = Θ(g(n)). Determine which relationship is correct. • f(n) = log n2; g(n) = log n + 5 • f(n) = n; g(n) = log n2 • f(n) = log log n; g(n) = log n • f(n) = n; g(n) = log2 n • f(n) = n log n + n; g(n) = log n • f(n) = 10; g(n) = log 10 • f(n) = 2n; g(n) = 10n2 • f(n) = 2n; g(n) = 3n f(n) = (g(n)) f(n) = (g(n)) f(n) = O(g(n)) f(n) = (g(n)) f(n) = (g(n)) f(n) = (g(n)) f(n) = (g(n)) f(n) = O(g(n)) COSC3101A

  15. More Examples(2) •  notation • n2/2 – n/2 • (6n3 + 1)lgn/(n + 1) • n vs. n2 •  notation • n vs. 2n • n3 vs. n2 • n vs. logn • n vs. n2 = (n2) = (n2lgn) n ≠ (n2) • O notation • 2n2 vs. n3 • n2 vs. n2 • n3 vs. nlogn n = (2n) 2n2 = O(n3) n2 = O(n2) n3 = (n2) n3 O(nlgn) n = (logn) n (n2) COSC3101A

  16. Asymptotic Notations in Equations • On the right-hand side • (n2) stands for some anonymous function in (n2) 2n2 + 3n + 1 = 2n2 + (n) means: There exists a function f(n)  (n) such that 2n2 + 3n + 1 = 2n2 + f(n) • On the left-hand side 2n2 + (n) = (n2) No matter how the anonymous function is chosen on the left-hand side, there is a way to choose the anonymous function on the right-hand side to make the equation valid. COSC3101A

  17. Limits and Comparisons of Functions Using limits for comparing orders of growth: • compare ½ n (n-1) and n2 COSC3101A

  18. Limits and Comparisons of Functions L’Hopital rule: • compare and COSC3101A

  19. Loop Invariant • A loop invariant is a relation among program variables that • is true when control enters a loop, • remains true each time the program executes the body of the loop, • and is still true when control exits the loop. • Understanding loop invariants can help us • analyze algorithms, • check for errors, • and derive algorithms from specifications. COSC3101A

  20. Proving Loop Invariants • Initialization (base case): • It is true prior to the first iteration of the loop • Maintenance (inductive step): • If it is true before an iteration of the loop, it remains true before the next iteration • Termination: • When the loop terminates, the invariant - usually along with the reason that the loop terminated - gives us a useful property that helps show that the algorithm is correct • Stop the induction when the loop terminates • Proving loop invariants works like induction COSC3101A

  21. 1 2 3 4 5 6 7 8 a1 a2 a3 a4 a5 a6 a7 a8 key Loop Invariant for Insertion Sort(1) Alg.:INSERTION-SORT(A) for j ← 2to n do key ← A[ j ] Insert A[ j ] into the sorted sequence A[1 . . j -1] i ← j - 1 while i > 0 and A[i] > key do A[i + 1] ← A[i] i ← i – 1 A[i + 1] ← key Invariant: at the start of the for loop the elements in A[1 . . j-1] arein sorted order COSC3101A

  22. Loop Invariant for Insertion Sort(2) • Initialization: • Just before the first iteration, j = 2: the subarray A[1 . . j-1] = A[1], (the element originally in A[1]) – is sorted COSC3101A

  23. Loop Invariant for Insertion Sort(3) • Maintenance: • the while inner loop moves A[j -1], A[j -2], A[j -3], and so on, by one position to the right until the proper position for key(which has the value that started out in A[j]) is found • At that point, the value of keyis placed into this position. COSC3101A

  24. Loop Invariant for Insertion Sort(4) • Termination: • The outer for loop ends when j > n (i.e, j = n + 1)  j-1 = n • Replace nwith j-1 in the loop invariant: • the subarray A[1 . . n] consists of the elements originally in A[1 . . n], but in sorted order • The entire array is sorted! j - 1 j COSC3101A

  25. Steps in Designing Algorithms(1) • Understand the problem • Specify the range of inputs the algorithm should handle • Learn about the model of the implementation technology • RAM (Random-access machine), sequential execution • Choosing between an exact and an approximate solution • Some problems cannot be solved exactly: nonlinear equations, evaluating definite integrals • Exact solutions may be unacceptably slow • Choose the appropriate data structures COSC3101A

  26. Steps in Designing Algorithms(2) • Choose an algorithm design technique • General approach to solving problems algorithmically that is applicable to a variety of computational problems • Provide guidance for developing solutions to new problems • Specify the algorithm • Pseudocode: mixture of natural and programming language • Prove the algorithm’s correctness • Algorithm yields the correct result for any legitimate input, in a finite amount of time • Mathematical induction, loop-invariants COSC3101A

  27. Steps in Designing Algorithms(3) 8. Analyze the Algorithm • Predicting the amount of resources required: • memory: how much space is needed? • computational time: how fast the algorithm runs? • FACT: running time grows with the size of the input • Input size (number of elements in the input) • Size of an array, polynomial degree, # of elements in a matrix, # of bits in the binary representation of the input, vertices and edges in a graph Def: Running time = the number of primitive operations (steps) executed before termination • Arithmetic operations (+, -, *), data movement, control, decision making (if, while), comparison COSC3101A

  28. Steps in Designing Algorithms(4) • Coding the algorithm • Verify the ranges of the input • Efficient/inefficient implementation • It is hard to prove the correctness of a program (typically done by testing) COSC3101A

  29. By problem types Sorting Searching String processing Graph problems Combinatorial problems Geometric problems Numerical problems By design paradigms Divide-and-conquer Incremental Dynamic programming Greedy algorithms Randomized/probabilistic Classification of Algorithms COSC3101A

  30. Divide-and-Conquer • Divide the problem into a number of subproblems • Similar sub-problems of smaller size • Conquer the sub-problems • Solve the sub-problems recursively • Sub-problem size small enough  solve the problems in straightforward manner • Combine the solutions to the sub-problems • Obtain the solution for the original problem COSC3101A

  31. Merge Sort Approach • To sort an array A[p . . r]: • Divide • Divide the n-element sequence to be sorted into two subsequences of n/2 elements each • Conquer • Sort the subsequences recursively using merge sort • When the size of the sequences is 1 there is nothing more to do • Combine • Merge the two sorted subsequences COSC3101A

  32. Merge Sort r p q Alg.: MERGE-SORT(A, p, r) if p < rCheck for base case then q ← (p + r)/2Divide MERGE-SORT(A, p, q)Conquer MERGE-SORT(A, q + 1, r) Conquer MERGE(A, p, q, r)Combine • Initial call:MERGE-SORT(A, 1, n) 1 2 3 4 5 6 7 8 5 2 4 7 1 3 2 6 COSC3101A

  33. 1 2 3 4 5 6 7 8 q = 4 5 2 4 7 1 3 2 6 1 2 3 4 5 6 7 8 5 2 4 7 1 3 2 6 1 2 3 4 5 6 7 8 5 2 4 7 1 3 2 6 5 1 2 3 4 6 7 8 5 2 4 7 1 3 2 6 Example – n Power of 2 Example COSC3101A

  34. 1 2 3 4 5 6 7 8 1 2 2 3 4 5 6 7 1 2 3 4 5 6 7 8 2 4 5 7 1 2 3 6 1 2 3 4 5 6 7 8 2 5 4 7 1 3 2 6 5 1 2 3 4 6 7 8 5 2 4 7 1 3 2 6 Example – n Power of 2 COSC3101A

  35. 1 2 3 4 5 6 7 8 9 10 11 q = 6 4 7 2 6 1 4 7 3 5 2 6 1 2 3 4 5 6 7 8 9 10 11 q = 3 4 7 2 6 1 4 7 3 5 2 6 q = 9 1 2 3 4 5 6 7 8 9 10 11 4 7 2 6 1 4 7 3 5 2 6 1 2 3 4 5 6 7 8 9 10 11 4 7 2 6 1 4 7 3 5 2 6 1 2 4 5 7 8 4 7 6 1 7 3 Example – n Not a Power of 2 COSC3101A

  36. 1 2 3 4 5 6 7 8 9 10 11 1 2 2 3 4 4 5 6 6 7 7 1 2 3 4 5 6 7 8 9 10 11 1 2 4 4 6 7 2 3 5 6 7 1 2 3 4 5 6 7 8 9 10 11 2 4 7 1 4 6 3 5 7 2 6 1 2 3 4 5 6 7 8 9 10 11 4 7 2 1 6 4 3 7 5 2 6 1 2 4 5 7 8 4 7 6 1 7 3 Example – n Not a Power of 2 COSC3101A

  37. r p q 1 2 3 4 5 6 7 8 2 4 5 7 1 2 3 6 Merging • Input: Array Aand indices p, q, rsuch that p ≤ q < r • Subarrays A[p . . q] and A[q + 1 . . r] are sorted • Output: One single sorted subarray A[p . . r] COSC3101A

  38. Merging • Idea for merging: • Two piles of sorted cards • Choose the smaller of the two top cards • Remove it and place it in the output pile • Repeat the process until one pile is empty • Take the remaining input pile and place it face-down onto the output pile COSC3101A

  39. p q 2 4 5 7  L q + 1 r r p q 1 2 3 6  R 1 2 3 4 5 6 7 8 2 4 5 7 1 2 3 6 n2 n1 Merge - Pseudocode Alg.:MERGE(A, p, q, r) • Compute n1and n2 • Copy the first n1 elements into L[1 . . n1 + 1] and the next n2 elements into R[1 . . n2 + 1] • L[n1 + 1] ← ;R[n2 + 1] ←  • i ← 1; j ← 1 • for k ← pto r • do if L[ i ] ≤ R[ j ] • then A[k] ← L[ i ] • i ←i + 1 • else A[k] ← R[ j ] • j ← j + 1 COSC3101A

  40. p q r Example: MERGE(A, 9, 12, 16) COSC3101A

  41. Example: MERGE(A, 9, 12, 16) COSC3101A

  42. Example (cont.) COSC3101A

  43. Example (cont.) COSC3101A

  44. Example (cont.) Done! COSC3101A

  45. Running Time of Merge • Initialization (copying into temporary arrays): • (n1 + n2) = (n) • Adding the elements to the final array (the last for loop): • n iterations, each taking constant time  (n) • Total time for Merge: • (n) COSC3101A

  46. Analyzing Divide-and Conquer Algorithms • The recurrence is based on the three steps of the paradigm: • T(n) – running time on a problem of size n • Divide the problem into a subproblems, each of size n/b: takes D(n) • Conquer (solve) the subproblems aT(n/b) • Combine the solutions C(n) (1) if n ≤ c T(n) = aT(n/b) + D(n) + C(n) otherwise COSC3101A

  47. MERGE-SORT Running Time • Divide: • compute qas the average of pand r:D(n) = (1) • Conquer: • recursively solve 2 subproblems, each of size n/2  2T (n/2) • Combine: • MERGE on an n-element subarray takes (n) time  C(n) = (n) (1) if n =1 T(n) = 2T(n/2) + (n) if n > 1 COSC3101A

  48. Loop invariant (at the start of the for loop) A[p…k-1] contains the k-p smallest elements of L[1 . . n1 + 1] and R[1 . . n2 + 1] in sorted order L[i] and R[j] are the smallest elements not yet copied back to A Correctness of Merge Sort p r COSC3101A

  49. Proof of the Loop Invariant • Initialization • Prior to first iteration: k = p  subarray A[p..k-1] is empty • A[p..k-1] contains the k – p = 0 smallest elements of L and R • L and R are sorted arrays (i = j = 1) L[1] and R[1] are the smallest elements in L and R COSC3101A

  50. Proof of the Loop Invariant • Maintenance • Assume L[i] ≤ R[j]  L[i] is the smallest element not yet copied to A • After copying L[i] into A[k], A[p..k] contains the k – p + 1 smallest elements of L and R • Incrementing k (for loop) and i reestablishes the loop invariant COSC3101A

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