Cis435 week02

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Cis435 week02

  1. 1. Solving Recurrences and the QuickSort Algorithm 1 Advanced Data Structures & Algorithm Design Solving Recurrences and the QuickSort Algorithm
  2. 2. Solving Recurrences and the QuickSort Algorith2 The Search Problem  A complement to the sorting problem discussed last week is the searching problem:  Given a set of elements A = {a1, a2, …, an} and a search parameter s, return the location i of s in A  If A is an ordered set, then the binary search algorithm can be used to search A for some value
  3. 3. Solving Recurrences and the QuickSort Algorith3 The Binary Search Algorithm  The binary search algorithm operates as follows (where A is the input set, and mid is the index of the element at the midpoint of A):  If s = Amid, return mid  If s < Amid, search the left half of A  If s > Amid, search the right half of A
  4. 4. Solving Recurrences and the QuickSort Algorith4 The Binary Search Algorithm int BinarySearch(const DataType A[], int first, int last, const DataType &s) { if ( last < first ) return –1; // not found int mid = (first + last) / 2; if ( s == A[mid] ) return mid; if ( s < A[mid] ) // search left return BinarySearch(A, first, mid-1, s); else return BinarySearch(A, mid+1, last, s); } int BinarySearch(const DataType A[], int first, int last, const DataType &s) { if ( last < first ) return –1; // not found int mid = (first + last) / 2; if ( s == A[mid] ) return mid; if ( s < A[mid] ) // search left return BinarySearch(A, first, mid-1, s); else return BinarySearch(A, mid+1, last, s); } What is the order of growth of the running time of BinarySearch? What is the order of growth of the running time of BinarySearch?
  5. 5. Solving Recurrences and the QuickSort Algorith5 Analysis of Binary Search  Each execution requires up to 4 constant operations and 1 recursive function call that operates on ½ the array, so:    >+ ≤ = 1if)1()2/( 1if)1( )( nOnT nO nT  We shall see tonight that this equates to T(n)=O(log2n)
  6. 6. Solving Recurrences and the QuickSort Algorith6 Solving Recurrences  Recall our recurrence relation from last week:    >+ = = 1if)()2/(2 1if)1( )( nnOnT nO nT  How do we solve this recurrence to determine the actual running time?
  7. 7. Solving Recurrences and the QuickSort Algorith7 Solving Recurrences  There are three methods that we can use:  The Substitution Method  We guess a bound, then use mathematical induction to prove our guess is correct  The Iteration Method  Converts the recurrence into a summation, then relies on techniques for bounding summations  The Master Method  Provides bounds for recurrences of the form T(n)=aT(n/b)+f(n) utilizing a set of rules
  8. 8. Solving Recurrences and the QuickSort Algorith8 The Substitution Method  Involves guessing the form of the solution, then using mathematical induction to find the constants and show that the solution works  Can only be applied in cases when it is easy to guess the form of the answer
  9. 9. Solving Recurrences and the QuickSort Algorith9 The Substitution Method: An Example  We can guess that the solution is of the form T(n)=O(nlog2n)  By definition, this means that T(n) has some upper bound of cnlog2n for some constant c > 0  We’ll use induction to prove that this bound holds    >+ = = 1if)2/(2 1if)1( )( nnnT nO nT
  10. 10. Solving Recurrences and the QuickSort Algorith10 The Substitution Method: An Example 1aslongas-log log 2loglog )2/(log ))2/(log]2/[(2)( :yieldsrecurrencetheintothisngSubstituti )2/(log)2/()2/( :isthat,2/forholdsboundthat thisassumingbystartWe 2 2 22 2 2 2 ≥≤ +−= +−= +≤ +≤ ≤ cncn ncnncn ncnncn nncn nnncnT nncnT n
  11. 11. Solving Recurrences and the QuickSort Algorith11 The Substitution Method: An Example  Now we must show that our solution holds for the boundary conditions  This means that we must be able to show that we can choose the constant c to be large enough so that the bound holds for all n, including the boundary conditions  For example, assume that our only boundary condition is that T(1)=1  With our solution, T(1) <= c1log21 = 0, no matter what c is
  12. 12. Solving Recurrences and the QuickSort Algorith12 The Substitution Method: An Example  This problem can normally be easily overcome  Asymptotic notation only requires us to prove that T(n)<=cnlog2n for some n >= n0  We can choose n0 to remove the difficult boundary condition from consideration  E.g., choosing n0 to be 2 or 3 removes the difficult boundary of 1 from consideration
  13. 13. Solving Recurrences and the QuickSort Algorith13 The Iteration Method  No guesses are required, but significant algebra is  The idea is to expand the recurrence and express it as a summation of terms dependent only on n
  14. 14. Solving Recurrences and the QuickSort Algorith14 The Iteration Method: An Example )64/(27)16/(9)4/(3 )))64/(3)16/((3)4/((3 ))16/(3)4/((3 )4/(3)( :followsasititeratecanWe :)4/(3)(recurrenceheConsider t nTnnn nTnnn nTnn nTnnT nnTnT +++= +++= ++= += += How much iteration is enough?How much iteration is enough?
  15. 15. Solving Recurrences and the QuickSort Algorith15 The Iteration Method: An Example  The i-th term in the series is 3i (n/4i )  This iteration hits n=1 when (n/4i )=1 or when i>log4n  If we continue the iteration to this point, we discover a decreasing geometric series: )( )(4 2.9)(identity)( 4 3 )1(3...64/2716/94/3)( 0 3log log 4 4 nO nOn nn nnnnnT i i n = += Θ+      ≤ Θ+++++≤ ∑ ∞ =
  16. 16. Solving Recurrences and the QuickSort Algorith16 The Iteration Method  There are two important points to focus on when utilizing the Iteration Method:  How many times must the recurrence be iterated to reach the boundary condition?  What is the summation of the terms?  The book describes a technique called “recurrence trees”, which are helpful in finding a solution
  17. 17. Solving Recurrences and the QuickSort Algorith17 The Master Method  Provides a “cookbook” approach to solving recurrences  It is used specifically to solve recurrences of the form T(n)=aT(n/b)+cnk  Recurrences of this form describe solutions that divide problems into subproblems of size n/b, where cnk is the cost of dividing the problem and combining the results
  18. 18. Solving Recurrences and the QuickSort Algorith18 The Master Method  To use the master method, you must memorize three case:      < = > = k k k k k a ba ba ba nO nnO nO nT b if if if )( )log( )( )( log  Note that these are simplified versions of the solutions found in the book
  19. 19. Solving Recurrences and the QuickSort Algorith19 QuickSort  QuickSort is a typical example of a divide- and-conquer algorithm:  Divide – partitions the input around an arbitrary element  Conquer – sorts each partition  Combine – sorting is performed in-place, and so requires zero work  The algorithm consists of two functions, the recursive QuickSort(), which utilizes Partition() to divide the array
  20. 20. Solving Recurrences and the QuickSort Algorith20 QuickSort void QuickSort(ArrayType &A, int begin, int end) { if ( begin < end ) { int q = Partition(A, begin, end); QuickSort(A, begin, q-1); QuickSort(A, q+1, end); } } void QuickSort(ArrayType &A, int begin, int end) { if ( begin < end ) { int q = Partition(A, begin, end); QuickSort(A, begin, q-1); QuickSort(A, q+1, end); } }
  21. 21. Solving Recurrences and the QuickSort Algorith21 QuickSort: Partition int Partition(ArrayType A[], int begin, int end) { ArrayType x = A[end]; int i = begin - 1; for ( int j = begin ; j < end ; ++j ) { if ( A[j] < x ) { ++i; swap(A[i], A[j]); } } swap(A[i+1], A[end]); return i+1; } int Partition(ArrayType A[], int begin, int end) { ArrayType x = A[end]; int i = begin - 1; for ( int j = begin ; j < end ; ++j ) { if ( A[j] < x ) { ++i; swap(A[i], A[j]); } } swap(A[i+1], A[end]); return i+1; }
  22. 22. Solving Recurrences and the QuickSort Algorith22 How Partition Works 9 7 4 5 2 1 6 3 i j 9 7 4 5 2 1 6 3 i j 9 7 4 5 2 1 6 3 i j 97 4 52 1 6 3 i j
  23. 23. Solving Recurrences and the QuickSort Algorith23 How Partition Works 9 7 452 1 63 The final result of the first partitioning: •The partition element is in it’s final sorted position •QuickSort now operates on each subarray independently
  24. 24. Solving Recurrences and the QuickSort Algorith24 Analysis of QuickSort  In the best case, Partition() divides the array evenly at every level  Each problem is divided into two equal subproblems of size n/2  Partition() itself is O(n) (it has a single loop that executes some number of times based on the size of the input)  The recurrence then is T(n)=2T(n/2)+n  Using the Master Method:  a=2, bk =21 =2, a=bk  this is Case 2  T(n)=O(nk log2n)=O(n1 log2n)=O(nlog2n)
  25. 25. Solving Recurrences and the QuickSort Algorith25 Analysis of QuickSort  In the worst case, the size of one partition is 1 and the other is n-1  Observing that T(1)=O(1), then the worst-case recurrence is:  T(n)=T(n-1)+O(n)+O(1)  T(n)=T(n-1)+O(n)
  26. 26. Solving Recurrences and the QuickSort Algorith26 Analysis of QuickSort  When will this worst- case occur?  How likely will it occur?  Why does it occur?  How can we improve it? )( )( )()1()( :iterationusecanweHere 2 1 1 n k k nnTnT n k n k Θ=       Θ= Θ= Θ+−= ∑ ∑ = =
  27. 27. Solving Recurrences and the QuickSort Algorith27 Homework  Reading: 4.1-4.3; 8.1-8.2; 8.4  Exercises:  4.3-1, 2, 3

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