Section IV.5: Recurrence Relations from Algorithms

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1 Sectio IV.5: Recurrece Relatios from Algorithms Give a recursive algorithm with iput size, we wish to fid a Θ (best big O) estimate for its ru time T() either by obtaiig a explicit formula for T() or by obtaiig a upper or lower boud U() for T() such that T() = Θ(U()). This ca be doe with the followig sequece of steps. (a) Fid the base cases ad a recurrece relatio for the algorithm. (b) (1) Obtai a iterative formula for T() if possible. The oe ca use methods i Epp, chapter 8, or Sectio IV.1 of these otes or () Obtai a upper or lower boud U() if the iterative formula is difficult or impossible to obtai explicitly. I Epp (sectio 8.), there are examples of obtaiig iterative formulas for T() for simple cases like arithmetic or geometric sequeces. Also illustrated are examples where familiar summatio idetities ca be used to get iterative formulas. Whe iterative formulas for T() are difficult or impossible to obtai, oe ca use either (1) a recursio tree method, () a iteratio method, or () a substitutio method with iductio to get T() or a boud U() of T() where T() = Θ(U()). Examples of these methods are foud i Corme (pp. 5-61). Examples of each of these methods are give below. Defiitio IV.5.1: Give a recursive algorithm (Defiitio i Sectio IV-1), a recurrece relatio for the algorithm is a equatio that gives the ru time o a iput size i terms of the ru times of smaller iput sizes. Defiitio IV.5.: A recursio tree is a tree geerated by tracig the executio of a recursive algorithm. (Corme, p. 59) Example IV.5.1: For Example IV.1.. i Sectio IV.1 (Summig a Array), get a recurrece relatio for the algorithm ad iterative formula T(). Use (a) a iteratio method ad (b) a recursio tree method. Get the Θ estimate of T(). Solutio: (a) T() = T(-1) + 1, sice additio of the -th elemet ca be doe by addig it to the sum of the -1 precedig elemets, ad additio ivolves oe operatio. Also T()=. Therefore, T(k) = T(k-1) + 1 for k betwee 1 ad is the recurrece relatio. Iteratio gives T() = T(-1) + 1 = T(-)+1 +1 = T(-) =... = T() = T() + =. (At the last stage we have added oe to itself times). Therefore, T() = Θ(). 1

2 Solutio: (b) At each level of the tree, replace the paret of that subtree by the costat term of the recurrece relatio T(k) = T(k-1) + 1 ad make T(k-1) the child. The tree is really a list here. It is developed from left to right i the diagram below. T() T(-1) 1 É 1 1 É É T(-) T() T() is computed by fidig the sum of the elemets at each level of the tree. Therefore T() =, ad T() = Θ(). Example IV.5.: Biary Search (recursive versio) The pseudo code for recursive biary search is give below. Algorithm Recursive Biary Search Iput: Value X of the same data type as array A, idices low ad high, which are positive itegers, ad A, the array A[low],..., A[high] i ascedig order. Output: The idex i array if X is foud, (zero) if X ot foud. Algorithm Body: low + high mid := if X = A[mid] the idex := mid else if X < A[mid] ad low < mid the call Biary Search with iputs X, low, mid-1, A else if X > A[mid] ad mid < high the call Biary Search with iputs X, mid+1, high, A else idex := ed Algorithm Recursive Biary Search Derive a recurrece relatio o Biary Search ad get a Θ estimate of the worst case ruig time T(). Use a recursio tree method. Solutio to Example IV.5.: Oe could cout the umber of comparisos of X to A[mid]. Biary Search is called o a subarray of legth approximately ad there are comparisos i the worst case before a recursive call is eeded agai. So the recurrece relatio is T() = T(/) +, or more specifically, T(k) = T(k/) + for iput size k. Also T(1) =.

3 The recursio tree is developed similar to that i Example 1. At each level, let the paret be the costat term ad the child be the term T(k/). The process is illustrated below. T() T(/) T(/) T(1) The depth of the tree is approximately log. Addig the values i the levels of the tree we get T() log. Therefore T() = Θ (log ). Example IV.5.: Merge Sort The pseudocode for Merge Sort is give i Epp, p. 59. Develop a recurrece relatio ad get a Θ estimate for this algorithm. Algorithm Merge Sort Iput: positive itegers bot ad top, bot top, array items A[bot], A[bot+1],..., A[top] that ca be ordered. Output: array A[bot], A[bot+1],..., A[top] of the same elemets as i the iput array but i ascedig order. Algorithm Body: if bot < top the do bot + top mid := call Merge Sort with iput bot, mid, A[bot],..., A[mid] call Merge Sort with iput mid + 1, top, A[mid+1],..., A[top] Merge A[bot],..., A[mid] ad A[mid+1],..., A[top] [where Merge takes these two arrays i ascedig order ad gives a array i ascedig order] ed do ed Algorithm Merge Sort Solutio to Example IV..: For simplicity let the iput size be = k, k a positive iteger. Let T() deote the ru time. I the worst case of Merge, there are about comparisos eeded to determie the orderig i the merged array (actually -1 sice the last elemet eed ot be compared). Sice we desire a Θ estimate ad ot a exact formula, we ca assume. I the best case there are / comparisos i Merge. This algorithm is called recursively o two subarrays of legth approximately /. Therefore, the recurrece relatio is T() = T(/) +, so for particular k, T(k) = T( k ) + k.

4 The recursio tree is developed below, usig fact that, at each level of the tree, the paret k ode is the term ot ivolvig T ad there are two childre due to the term T( ). The tree is developed as show, goig from left to right ad top to bottom. T() à T(/) T(/) à T( ) T( ) T( ) T( ) The sum of the values at each level of the tree is (m odes times the value m i each ode). There are k = log levels sice we ca cut the array i half k times. Thus, the sum of the elemets i all odes of the tree is log. Therefore, T() = Θ( log ). Example IV.5.: Give the recurrece T() = T( ) + T( ) +, get a Θ estimate for T() usig the recursio tree method. Solutio: The recursio tree is developed below. T( ) T( ) T( ) T( ) T( ) T( ) The costructio cotiues util we get to the leaves. We keep multiplyig by util k ( ) = 1. The logest possible path of this tree is of legth k where k ( ) = 1, i.e.,

5 where k = log. The sum of the values i each level of the tree is at most. The left part of the tree plays out before the right side; the legth of the path from the root to the leaves ca vary from log to log, depedig o whether argumet k i T(k) goes to k/ or k/. Oe obtais T() log ad T() log. By the fact that growth rate of a log fuctio is idepedet of its base (Theorem II.5.8), we ca coclude T() = O( log ) ad T() = Ω( log ). Therefore T() = Θ( log ). The ext two examples use the iteratio method. Example IV.5.5 ivolves a coverget geometric series, ad Example IV.5.6 is more complicated sice the geometric series diverges. Example IV.5.5: Give the recurrece T() = T method that T() = Θ(). ( ) +, show by a iteratio Solutio: For simplicity, we shall use m for m sice the recurrece is i terms of the floor fuctio. The iteratio proceeds as follows: T() + T( ) = + (( ) + T( 16 ) ) = T( 6 ) i ( / ) = from the formula ( r i ) = i= i= 1 1 r whe r < 1 (here r = ). (The formula for T() ivolves a decreasig geometric series with ratio r = ). Therefore, T() = O() (use C = ad X = N = 1 i the defiitio of big O). Also, T() sice T( ) >. So T() = Ω () (use C = 1 ad X = N = 1 i the defiitio of Ω). By the defiitio of Θ (sectio II.5), we have T() = Θ(). Example IV.5.6: Give the recurrece T() = T( ) +, use the iteratio method to get a big O estimate for T(). Solutio: For simplicity, we shall use m for m sice the recurrece is i terms of the floor fuctio. The iteratio proceeds as follows: T() = + T( ) = + (( ) + T( ) ) = T( ) = The coefficiets form the terms of the geometric series ( r i ) where r = /. Sice i= 5

6 r > 1, the ifiite series diverges. However, there are oly fiitely may terms i the sum. The sum termiates for the smallest iteger k where k > (the umber of times we divide by is log ), i.e., whe k log. Sice we have T() ( 1 log 1 / log ) log 1 i = ( ) = (( ) 1) log /( log 1) = (( ) -1) i= = ( ) log - log = ( ) log - log = - usig log = = log -. I the last step, we used b (*) a log b a = log, which is derived i Sectio IV.6. Sice log > 1, we kow that term coclude that T() = O ( log ). log domiates. From sectio II.5, we ca Note: We use stadard properties of logarithms i this argumet. We also use the i idetity r = r + 1 ( 1) ( r 1) whe r 1. i= Note: The Master method give i Sectio IV.6 will give us a much easier way to do this problem. Substitutio method with iductio The ext two examples use the substitutio method with iductio. The method is described as follows. We show T() = O(U()). Showig T() = Ω(U()) is similar. (1) Guess the form of a boud U() of T(). () Assume by strog iductio that for some costats C ad, T(k) C U(k) for all k ad k <. Show that for these values of C ad, T() C U( )for all > () Fid a pair C ad that also satisfy the iitial coditios (values of T(1), T(). etc.). These must satisfy the coditios i () ad (); therefore, the values may chage. () If the give form does ot work, try a differet U() with same growth rate. If this fails, try a U() with a differet growth rate. Example IV.5.7: Fid a Θ boud for T() where T() = T(-1) + ad T() = 1. Use the substitutio method with iductio. 6

7 Solutio: From Epp, sectio 8., oe ca get the iterative formula T() = 1 + ( +1). So we kow that T() = Θ( ). However, we show how substitutio ad iductio works i this example Assume T() C for all >, k <, ad fid C ad that work. Usig the iductio assumptio, we try to fid C ad where T() = T(-1) + C ( 1) + = C ( - +1) + = C - C + C + C for all >. This iequality implies that C C, i.e., C (-1) or C. 1 Sice the expressio is decreasig (verify by takig derivatives), we substitute 1 1 for ad must have 1/(-1) C, that is, C 1. However, o C works for T(), ad T(1) =. For T(1) C (1) to hold, we must have C. Puttig together the costraits C 1 ad C, we kow that C works, ad also that 1 must hold. So take C = ad = 1 i the defiitio of big O. It is true that we did ot eed such a complicated procedure to solve Example II.5.7. However, may times oe caot easily get a closed form for T(). Yet the substitutio method with iductio works. Example IV.5.8 illustrates this. Example IV.5.8: Use the substitutio ad iductio approach to show T() = T( ) +, where T(1) = 1, is Θ( log ). Solutio: Show that T() C log for all > k ad fid a C ad. Usig the iductio assumptio ad recurrece formula, oe obtais T C ( ) ( log ) + () C log + = C log C log + = C log C + Clog if C 1. (We use properties of logs ad fact that i this derivatio). 7

8 However, T() C log is ot true for = 1, for 1 log 1 =. So we must choose. Now, T() = T(1) + = ad T() = T(1) + = 5. We must pick C large eough that T() C ( log ) ad T() C ( log ). Ay C works. So we ca take C = ad = i the defiitio of big O. Chagig Variables Sometimes oe ca do algebraic maipulatio to write a ukow recurrece i terms of a familiar oe. The oe solves the familiar oe ad writes the solutio i terms of the origial variable. Example IV.5.9 illustrates this. Example IV.5.9: Solve T() = T( ) + log. m m Solutio: Let m = log. The this recurrece becomes T( ) = T ( ) + m. Let S(m) = T( m m (m ), i.e. S(m) = T(g(m)) where g(m) =. So S ( m ) = T( ) ad the ew recurrece S(m) = S(m/) + m is produced. This is the same form as Example m IV.5.8; therefore it has the solutio S(m) = Θ(m log m ). By the substitutio =, we m get T() = T( ) = S(m) = Θ(m log m ) = Θ( log log (log )). Exercises: (1) Use the recursio tree method to get a Θ estimate, coefficiet 1, for the recurrece T() = T( ) +. () Use the recursio tree method to get a Θ estimate, coefficiet 1, for the recurrece i Example IV.5.5. You eed to get the height of the recursio tree. () Use the recursio tree method to get a Θ estimate, coefficiet 1, for the recurrece T T() = T( ) + T( ) +. () Use a iteratio method (similar to Example IV.5.5) to get a Θ estimate, coefficiet 1, for recurrece the T() = T 5 +. (5) Use a iteratio method (similar to Example IV.5.6) to get a Θ estimate, coefficiet 1, for the recurrece T() = 5T +. 8

9 (6) Give the recurrece T() = T ( ) + T ( ) + 1, show that T() C for ay C caot be show usig a iductio argumet. Show T() = O() is correct by fidig a C ad b where T() C b works. Assume that the iitial coditio is T(1) = 1. (7) Solve the recurrece T ( ) = T ( 1/ ) + T ( ) + log usig a substitutio m i terms of similar to that i Example IV.5.9. You will get a recurrece i m that is oe of the previous examples. Solve it i terms of m ad solve the give recurrece i terms of. 9

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