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15.082J & 6.855J & ESD.78J Shortest Paths 2: Bucket implementations of Dijkstra’s Algorithm R-Heaps

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2 A Simple Bucket-based Scheme Let C = 1 + max(c ij : (i,j) ∈ A); then nC is an upper bound on the minimum length path from 1 to n. RECALL: When we select nodes for Dijkstra's Algorithm we select them in increasing order of distance from node 1. SIMPLE STORAGE RULE. Create buckets from 0 to nC. Let BUCKET(k) = {i ∈ T: d(i) = k}. Buckets are sets of nodes stored as doubly linked lists. O(1) time for insertion and deletion.

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3 Dial’s Algorithm u Whenever d(j) is updated, update the buckets so that the simple bucket scheme remains true. u The FindMin operation looks for the minimum non-empty bucket. u To find the minimum non-empty bucket, start where you last left off, and iteratively scan buckets with higher numbers. Dial’s Algorithm

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4 Running time for Dial’s Algorithm C = 1 + max(c ij : (i,j) ∈ A). u Number of buckets needed. O(nC) u Time to create buckets. O(nC) u Time to update d( ) and buckets. O(m) u Time to find min. O(nC). u Total running time. O(m+ nC). u This can be improved in practice; e.g., the space requirements can be reduced to O(C).

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5 Additional comments on Dial’s Algorithm u Create buckets when needed. Stop creating buckets when each node has been stored in a bucket. u Let d* = max {d*(j): j ∈ N}. Then the maximum bucket ever used is at most d* + C. Suppose j ∈ Bucket( d* + C + 1) after update(i). But then d(j) = d(i) + c ij ≤ d* + C d*d*+1 j d*+2d*+3 d*+Cd*+C+1 ∅

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A 2-level bucket scheme u Have two levels of buckets. Lower buckets are labeled 0 to K-1 (e.g., K = 10) Upper buckets all have a range of K. First upper bucket’s range is K to 2K – 1. Store node j in the bucket whose range contains d(j). 6 0123456789 10-1920-2930-3940-4950-5960-6970-7980-8990-99 2 1 2 1 5 distance label 3 21 5

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Find Min u FindMin consists of two subroutines SearchLower: This procedure searches lower buckets from left to right as in Dial’s algorithm. When it finds a non-empty bucket, it selects any node in the bucket. SearchUpper: This procedure searches upper buckets from left to right. When it finds a bucket that is non-empty, it transfers its elements to lower buckets. 7 FindMin If the lower buckets are non-empty, then SearchLower; Else, SearchUpper and then SearchLower.

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More on SearchUpper u SearchUpper is carried out when the lower buckets are all empty. u When SearchUpper finds a non-empty bucket, it transfers its contents to lower buckets. First it relabels the lower buckets. 8 10-1920-2930-3940-4950-5960-6970-7980-8990-99 0123456789 4 30313233343536373839 3 5 3 33 5 30 4 54 2-Level Bucket Algorithm

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Running Time Analysis u Time for SearchUpper: O(nC/K) O(1) time per bucket u Number of times that the Lower Buckets are filled from the upper buckets: at most n. u Total time for FindMin in SearchLower O(nK); O(1) per bucket scanned. u Total Time for scanning arcs and placing nodes in the correct buckets: O(m) u Total Run Time: O(nC/K + nK + m). Optimized when K = C.5 O(nC.5 + m) 9

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More on multiple bucket levels u Running time can be improved with three or more levels of buckets. u Runs great in practice with two levels u Can be improved further with buckets of range (width) 1, 1, 2, 4, 8, 16 … Radix Heap Implementation 10

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11 A Special Purpose Data Structure u RADIX HEAP: a specialized implementation of priority queues for the shortest path problem. u A USEFUL PROPERTY (of Dijkstra's algorithm): The minimum temporary label d( ) is monotonically non-decreasing. The algorithm labels node in order of increasing distance from the origin. u C = 1 + max length of an arc

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12 Radix Heap Example 1 24 53 6 13 5 2 8 15 20 9 0 012-34-7 8-1516-3132-63 Buckets: bucket sizes grow exponentially ranges change dynamically Radix Heap Animation

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Analysis: FindMin u Scan from left to right until there is a non-empty bucket. If the bucket has width 1 or a single element, then select an element of the bucket. Time per find min: O(K), where K is the number of buckets 13 01 2-34-7 8-1516-3132-63 2 4 5

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Analysis: Redistribute Range u Redistribute Range: suppose that the minimum non-empty bucket is Bucket j. Determine the min distance label d* in the bucket. Then distribute the range of Bucket j into the previous j-1 buckets, starting with value d*. Time per redistribute range: O(K). It takes O(1) steps per bucket. Time for determining d*: see next slide. 14 01 2-34-7 8-1516-3132-63 2 4 5 91011-1213-15 d(5) = 9 (min label)

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Analysis: Find min d(j) for j in bucket u Let b the the number of items in the minimum bucket. The time to find the min distance label of a node in the bucket is O(b). Every item in the bucket will move to a lower index bucket after the ranges are redistributed. Thus, the time to find d* is dominated by the time to update contents of buckets. We analyze that next 15

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Analysis: Update Contents of Buckets u When a node j needs to move buckets, it will always shift left. Determine the correct bucket by inspecting buckets one at a time. O(1) whenever we need to scan the bucket to the left. For node j, updating takes O(K) steps in total. 16 01 2-34-7 8-1516-3132-63 2 4 91011-1213-15 d(5) = 9 5

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Running time analysis u FindMin and Redistribute ranges O(K) per iteration. O(nK) in total u Find minimum d(j) in bucket Dominated by time to update nodes in buckets u Scanning arcs in Update O(1) per arc. O(m) in total. u Updating nodes in Buckets O(K) per node. O(nK) in total u Running time: O(m + nK) O(m + n log nC) u Can be improved to O(m + n log C) 17

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18 Summary u Simple bucket schemes: Dial’s Algorithm u Double bucket schemes: Denardo and Fox’s Algorithm u Radix Heap: A bucket based method for shortest path buckets may be redistributed simple implementation leads to a very good running time unusual, global analysis of running time

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MITOpenCourseWare http://ocw.mit.edu 15.082J / 6.855J / ESD.78J Network Optimization Fall 2010 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.http://ocw.mit.edu/terms

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