Σ P(i) ( depth T (K i ) + 1),

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1 EECS 3101 York Uiversity Istructor: Ady Mirzaia DYNAMIC PROGRAMMING: OPIMAL SAIC BINARY SEARCH REES his lecture ote describes a applicatio of the dyamic programmig paradigm o computig the optimal static biary search tree of keys to miimize the expected search time for a give search probability distributio. Suppose we are give acollectio of keys 1 < 2 <... < which are to be stored i a biary search tree. After the tree has bee costructed, oly search operatios will be performed i.e. there will be o isertios or deletios. We are also give aprobability desity fuctio P where P(i) isthe probability of searchig for key i. here are may differet biary search trees where the give keys ca be stored. For a particular tree with these keys, the average umber of comparisos to fid a key, for the give probability desity is Σ P(i) ( depth ( i ) + 1), i=1 where depth ( i )deotes the depth of the ode where i is stored i. he problem we would like to solve is to fid, amog all the possible biary search trees that cotai the keys, oe which miimizes this quatity. Such a tree is called a optimal (static) biary search tree. Note that there may be several optimal biary trees for the give desity fuctio. his is why we speak of a optimal, rather tha the optimum biary search tree. Asimple way to accomplish this is to try out all possible biary trees with odes, computig the average umber of comparisos to fid a key i each tree cosidered, ad selectig a tree with the miimum average. Ufortuately, this simple strategy is ridiculously iefficiet because there are too may trees to try out. I particular, there are ( 2 )/( +1) differet biary trees with odes (if iterested i the derivatio of this formula, see uth, vol. I, pp ). hus, if there are 20 keys, we have to try out 131,282,408,400 differet trees. Computig the average umber of comparisos i each at the rather astoishig speed of 1µsec per tree, will still take 2188 hours or approximately 91 days ad ights of computig to fid a optimal biary search tree (for just 20 keys)! Fortuately, there is a much more efficiet, if less straightforward, way to fid a optimal biary search tree. Let be a biary search tree that cotais i, i+1,..., j for some 1 i j. Weshall see shortly why itisuseful to cosider trees that cotai subsets of successive keys. We defie the cost of as, c( )= Σ j P(l) ( depth ( l ) + 1) Hece, if cotais all keys (i.e. i = 1ad j = ), the cost of is precisely the expected umber of comparisos to fid a key for the give desity fuctio. hus, we ca rephrase our problem as follows: Give adesity fuctio for the keys, fid a miimum cost tree with odes. Before givig the algorithm to fid a optimal biary search tree, we prove two key facts. his is ot so if is missig some of the keys, because i that case the probabilities of the keys that are i do ot sum to 1 that is, P is ot a proper desity fuctio relative to the set of keys ithe tree.

2 -2- Lemma 1: Let be a biary search tree cotaiig keys i, i+1,..., j, L ad R be the left ad right subtrees of respectively. he c( )=c( L ) + c( R ) + Σ j P(l). Proof: his is a easy cosequece of the defiitio of cost of a tree. You should prove it oyour ow. Lemma 2: Let be a biary search tree that has miimum cost amog all trees cotaiig keys i, i+1,..., j ad let m be the key at the root of (so i m j ). he L,the left subtree of, is a biary search tree that has miimum cost amog all trees cotaiig keys i,..., m 1 ;ad R,the right subtree of,isabiary search tree that has miimum cost amog all trees cotaiig keys m+1,..., j. Proof: We prove the cotrapositive. hat is, if either the left or right subtree of fails to satisfy the miimality property asserted i the lemma we show that does ot really have the miimum possible cost amog all trees that cotai i,..., j. Let L ad R be miimum cost biary search trees that cotai i,..., m 1 ad m+1,..., j respectively. hus, c( L ) c( L )ad c( R ) c( R )(*). Further, let be the tree with key m i the root, ad left ad right subtrees L ad R respectively. Evidetly, is a biary search tree that cotais keys i,..., j.if L or R do ot have the miimality property asserted by the lemma, it must be that either c( L )>c( L ), or c( R )>c( R ). his, together with (*) implies that c( L ) + c( R )>c( L ) + c( R ). From this ad Lemma 1 we have, c( )=c( L ) + c( R ) + Σ j P(l) > c( L ) + c( R ) + Σ j P(l) =c( ). hus, c( )>c( ) ad is ot amiimum cost biary search tree amog all trees that cotai keys i,..., j. Computig a Optimal Biary Search ree Lemma 2 is the basis of a efficiet algorithm to fid a optimal biary search tree. Let ij deote a optimal biary search tree cotaiig keys i, i+1,..., j.he 1 is precisely the optimal biary search tree that we wat to costruct. Lemma 2 says that ij must be of the followig form: m i,m-1 m+1,j hat is, its root has key m for some m, i m j, ad its subtrees are i,m 1 ad m+1, j,i.e. miimum cost subtrees cotaiig keys i,..., m 1 ad m+1,..., j respectively. But i,m 1 ad m+1, j are "smaller" trees tha ij. his suggests proceedig iductively, startig with small miimum cost trees (each cotaiig just oe key) ad progressively buildig larger ad larger

3 -3- miimum cost trees, util we have a miimum cost tree with odes which is what we are lookig for. More specifically, we start the iductio with miimum cost trees each of which cotais exactly oe key, ad proceed by costructig miimum cost trees with 2, 3,..., successive keys. Note that there are exactly d +1 groups of d successive keys for each d = 1,...,. hus, istead of cosiderig all possible trees with odes we cosider oly (miimum cost) trees with 1 ode each, 1 (miimum cost) trees with 2 odes each,...,1miimum cost tree with odes, i.e. a total of ( +1) /2 trees much fewer tha ( 2 )/( +1). So ow the questio is how tocompute these trees ij iductively. he basis of the iductio, i.e. whe j i = 0istrivial. I this case we have j = i ad the miimum cost biary search tree that stores i (i fact the oly such tree!) is a sigle ode cotaiig i ;its cost is c( ii )=P(i). For the iductive step, take j i >0 ad assume that we have already computed all the uv s ad their costs, for v u < j i. Let imj be the tree with m i the root, ad left ad right subtrees i,m 1 ad m+1, j respectively. Aswesaw before, Lemma 2 implies that ij is the miimum cost tree amog the imj s. hus we ca fid ij simply by tryig out all the imj s for m = i, i +1,..., j. Ifact Lemma 1 tells us how tocompute c( imj )efficietly, sothat "tryig out" each possible m will ot take too log: Sice (m 1) i ad j (m +1) are both < j i, wehave already (iductively) computed i,m 1 ad m+1, j as well as their costs, c( i,m 1 )ad c( m+1, j ). Lemma 1 the tells us how toget c( imj )iterms of these. Note that whe m = i the left subtree of imj is i,i 1 ad whe m = j the right subtree of imj is j+1, j. Wedefie ij to be empty if i > j, ad the cost of a empty tree to be 0. Figure 1 shows this algorithm i pseudo-code. he algorithm takes as iput a array Prob[1.. ], which specifies the probability desity (i.e. Prob[i] = P(i)). It computes two twodimesioal arrays, Root ad Cost, where Root[i, j] isthe root of ij,ad Cost[i, j] = c( ij ), for 1 i j. o help compute Root ad Cost the algorithm maitais a third array, SumOf- Prob, where SumOfProb[i] = Σ i P(l) for 1 i, ad SumOfProb[0] = 0. Note that j Σ P(l) =SumOfProb[j] SumOfProb[i 1]. l=1 he algorithm of Figure 1 does ot explicitly costruct a optimal biary search tree but such a tree is implicit i the iformatio i array Root. Asaexercise you should write a algorithm which, give Root ad a array ey[1.. ], where ey[i] = i,costructs a optimal biary search tree. It is ot hard to show that this algorithm has worst case time complexity Θ( 3 ). A slight modificatio of this algorithm leads to a Θ( 2 )complexity (if iterested, see D.E. uth, "Optimum biary search trees," Acta Iformatica, vol. 1 (1971), pp ) Usuccessful Searches I this discussio we have oly cosidered successful searches. However, if we take ito accout usuccessful searches, maybe the costructed tree is o loger optimal. Fortuately, this For techical reasos that will become apparet whe you look at the algorithm carefully we eed to set Cost[i, i 1] = 0 for1 i +1. Recall that i,i 1 is empty ad thus has cost 0.

4 -4- algorithm OptimalBS ( Prob[1.. ] ); begi (* iitializatio *) for i 1 to +1 do Cost[i, i 1] 0; SumOfProb[0] 0; for i 1 to do begi SumOfProb[i] Prob[i] + SumOfProb[i 1]; Root[i, i] i; Cost[i, i] Prob[i] ed; for d 1 to 1 do (* compute ifo about trees with d + 1 cosecutive keys *) for i 1 to d do begi (* compute Root[i, j] ad Cost[i, j] *) j i + d; MiCost + ; for m i to j do begi (* fid m betwee i ad j so that c( imj )ismiimum *) c Cost[i, m 1] + Cost[m +1, j] + SumOfProb[ j] SumOfProb[i 1]; if c < MiCost the begi MiCost c; r m ed ed; Root[i, j] r; Cost[i, j] MiCost ed ed Figure 1: Algorithm for optimal biary search tree. problem ca be take care of i a straightforward maer. o fid a optimal biary search tree i the case where both successful ad usuccessful searches are take ito accout, we must kow the probability desity for both successful ad usuccessful searches. So, i additio to P(i) weare also give Q(i) for 0 i, where Q(0) =prob. ofsearchig for keys < 1 ; Q(i) =prob. ofsearchig for keys x, i < x < i+1,for 1 i < ; Q() =prob. ofsearchig for keys >. I each biary search tree cotaiig keys 1, 2,..., we add +1 exteral odes,,..., E.his is illustrated below; the exteral odes are draw i boxes, as usual.

5 E 4 E2 he average umber of comparisos for a successful or usuccessful search i such a tree is Σ P(i) ( depth ( i ) + 1)+ Σ Q(i) depth (E i ). i=1 he left term is the cotributio to the average umber of comparisos by the successful searches ad the right term is the cotributio to the average umber of comparisos by the usuccessful searches. Now wewat to fid a tree that miimizes this quatity. We ca proceed exactly as before, except that the defiitio of the cost of a tree with cosecutive keys i, i+1,..., j is slightly modified to accout for the usuccessful searches. Namely, itbecomes, i=0 c ( )= Σ j P(l) ( depth ( l ) + 1)+ With this cost fuctio Lemma 1 is slightly differet: j Σ Q(l) depth (E l ). Lemma 1 : Let be a biary search tree cotaiig keys i, i+1,..., j, L ad R be the left ad right subtrees of respectively. he 1 c ( )=c ( L ) + c ( R ) + Q(i 1) + Σ j (P(l) + Q(l)). Everythig else works out exactly as before. I particular, Lemma 2 is still valid (check this!). As a exercise show how to modify the algorithm i Figure 1 to accout for these chages. Example: Suppose we wat to fid a optimal biary search tree for the dictioary {begi, ed, goto, repeat, util} for the followig probabilities of searchig for these keys (P(i) s) ad keys alphabetically i betwee (the Q(i) s): 1 =begi 2 =ed 3 =goto =repeat 5 =util P(1 ) =.1 P(2 ) =.1 P(3 ) =. 05 P(4)=.05 P(5 ) =. 05 Q(0)=.05 Q(1 ) =. 05 Q(2 ) =. 2 Q(3 ) =. 2 Q(4)=.1 Q(5 ) =. 05 he trees ij as computed by the algorithm o this example are show i the table below with their costs. he computatio proceeds row by row (i.e. to compute the trees ad costs i row d, all the trees up to row d 1 must have bee previously computed). he optimal biary search tree 1,5 is show o the last row. You are strogly ecouraged to trace this example carefully. Eve if you thik you uderstad the algorithm from the previous abstract discussio, you may be surprised at how much better you ll uderstad it after workig out a example.

6 -6- d = -1 : E c( )=0 10 c( )=0 21 c( )=0 32 c( 43)=0 c( )=0 54 c( 65)=0 d = 0 : E 4 c( )=.2 11 c( )= c( )= c( )= c( )=.2 55 d = 1 : E E 4 c( 12) =.7 c( ) =.95 c( ) =.95 c( ) =.65 45

7 -7- d = 2 : E E1 E2 E E 4 E 5 c( ) = 1.4 c( ) = 1.45 c( ) = d = 3 : E E E 4 5 c( ) = 1.95 c( 14 25) = 1.85 d = 4 : c( 15) = 2.35