module6

module6 - Self-organizing Search We consider a list of n...

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Unformatted text preview: Self-organizing Search We consider a list of n distinct items, say 1 , 2 ,...,n , stored in an array A . Items are accessed according to some fixed probability distribution: Pr[ i is accessed] = p i , 1 ≤ i ≤ n . Every access of an item is an independent event . WLOG we will assume that p 1 ≥ p 2 ≥ ··· ≥ p n . Items are accessed sequentially, via a linear search . Therefore the cost of accessing an item x i (at a given point of time) is equal to the index k such that A [ k ] = x i . 122 / 193 Request Sequences and Static Orderings Consider a static ordering of the n items. Let π i denote the position in the list where item i is placed, i.e., A [ π i ] = i for i = 1 ,...,n . Observe that π is a permutation of { 1 ,...,n } . π i is also the cost of accessing item i . The expected cost of the ordering π , denoted C ( π ), is a weighted average that can be computed as follows: C ( π ) = n X j =1 π j p j . A request sequence is a list of requests, r = ( r 1 ,...,r m ), which is made according to the given probability distribution. We have r j ∈ { 1 ,...,n } for 1 ≤ j ≤ m . The average cost of accessing the items in the request sequence r is equal to C ( π ). 123 / 193 Optimal Static Ordering The optimal static ordering is the ordering that yields the minimum expected cost. It can be shown that the optimal static ordering is the ordering 1 , 2 ,...,n . (Intuitively, this seems reasonable: we are placing the most frequently accessed items towards the front of A .) The permutation π is the identity permutation , where π j = j , 1 ≤ j ≤ n . The optimal expected cost , which is attained by the optimal static ordering, is computed to be C oso = n X j =1 j p j . 124 / 193 Dynamic Orderings A static ordering works well if the probability distribution is known ahead of time. If the probability distribution is not known, then we can employ a dynamic ordering in which the array is rearranged every time an item is accessed, using a certain heuristic . A popular heuristic is move-to-front ( MTF ), where the current item being accessed is always moved to the front of the array. We will study the expected cost of move-to-front, which we denote by C mtf , and compare it to C opt . For simplicity, suppose that we have only two items, 1 and 2. The cost of accessing an item i is 1 if the item i was the last item accessed ; otherwise the cost is 2. Let p 1 = p ; and p 2 = q ; then q = 1- p . Recall that we are assuming that p 1 ≥ p 2 , so p ≥ 1 / 2 and q ≤ 1 / 2. 125 / 193 Comparison of C mtf and C oso When using the move-to-front heuristic with two items, the probability that two consecutive accesses are for the same item is p 2 + q 2 , and the probability that two consecutive accesses are for different items is 1- ( p 2 + q 2 ) = 2 pq ....
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This note was uploaded on 02/21/2012 for the course PSYCH 101 taught by Professor Ennis during the Winter '09 term at Waterloo.

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module6 - Self-organizing Search We consider a list of n...

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