d-ary heaps

d_ary_heap indirect example and d-ary heap implementation c++
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Published Date:22-07-2017
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ECE 250 Algorithms and Data Structures d-ary heaps Douglas Wilhelm Harder, M.Math. LEL Department of Electrical and Computer Engineering University of Waterloo Waterloo, Ontario, Canada ece.uwaterloo.ca dwharderalumni.uwaterloo.ca © 20143 by Douglas Wilhelm Harder. Some rights reserved.d-aryheaps 2 Outline In this topic, we will: – Definition of a d-ary min heap – Implementation as a complete tree – Examples of binary, ternary, quaternary, and quinary min heaps – Properties – Relative speeds • Optimal choice is a quaternary heapd-aryheaps 3 Definition The relationship between a binary min heap and a d-ary min heap is the same as a binary tree and an N-ary tree – Every node has up to d children The relationship is the same—all children are greater than their parentd-aryheaps 4 d-ary heaps as complete N-ary trees The implementation of a d-ary heap is similar to that of a binary heap: use a complete N-ary tree which can be stored as an array Observation: – With binary heaps, we started at index 1, and for the entry at index k: • The parent is at k/2 • The children are at 2k and 2k + 1 – Recall the form: parent = k 1; left_child = k 1; right_child = left_child 1;d-aryheaps 5 d-ary heaps as complete N-ary trees Initial Calculations Operations index parent = (k - 2) 2; 3 arithmetic third_child = k 2; 3 logic 1 first_child = third_child - 2; second_child = third_child - 1; fourth_child = third_child 1; parent = (k - 1) 2; 2 arithmetic first_child = k 2; 5 logic second_child = first_child 2; 0 third_child = first_child 3; fourth_child = first_child + 4; first_child = 1; parent = (k 4) - 1; 2 arithmetic first_child = (k + 1) 2; 5 logic -1 second_child = first_child 1; third_child = first_child 2; fourth_child = first_child 3;d-aryheaps 6 d-ary heaps as complete N-ary trees Finally, if we start at -1, our calculations are: parent = (k 4 ? -1 : k 4; first_child = (k + 1) 2; second_child = first_child 1; third_child = first_child 1; fourth_child = first_child 1; Now, if we start at index 0, our calculations are: parent = (k - 1) 2; first_child = k 2; second_child = first_child 2; third_child = first_child 3; fourth_child = first_child + 4; first_child = 1;d-aryheaps 7 d-ary heaps as complete N-ary trees The implementation of a d-ary heap is similar to that of a binary heap: we use a complete N-ary tree which can be stored as an array To find the root, children, and parent: – The root is at 0 (not 1 like a binary heap) – The children of k are at: dk + 1, dk + 2, ..., dk + d k1 – The parent of k is at for k 0 dd-aryheaps 8 Examples Example of a binary min-heap: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 2 5 4 16 7 9 11 15 31 27 12 26 35 23 14 18 17 42d-aryheaps 9 Examples The same 18 elements in a ternary min-heap: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 2 11 4 5 12 17 14 18 31 27 7 9 35 23 16 15 26 42d-aryheaps 10 Examples In a quaternary min-heap: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 2 16 4 7 12 26 14 18 31 27 5 9 35 23 11 15 17 42d-aryheaps 11 Examples And a quinary heap: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 2 5 4 7 12 26 16 18 31 27 14 9 35 23 11 15 17 42d-aryheaps 12 Properties The properties of a complete d-ary heap are: – The average depth of a node is given by the formula 1 h d1 – The proportion of leaf nodes to the full number of nodes is approximately d1 dd-aryheaps 13 Properties For example, in a complete quaternary heap: – The average height of a node is h–⅓, and – The leaf nodes comprise ¾ of all nodes Therefore: – A push will require approximately 1⅓ comparisons and ⅓ copies k1  3 1 4  k   k1 4 4 3  – A pop will require almost 4h comparisons (= (3 + 1)h) and h + 1 copiesd-aryheaps 14 Relative Speed In general, d-ary heaps have different performance versus binary heaps: – A d-ary heap makes log (n) comparisons for each push (worst case) d • Percolating up compares only the parent – A d-ary heap must, however, make d log (n) comparisons for each pop d • Percolating down compares a node with all d children Assuming an equal number of pushes and pops: log (n) + d log (n) = (d + 1) log (n) d d dd-aryheaps 15 Relative Speed Calculating the relative number of comparisons with a binary heap log n  2 d1  d1 log n log d  d1 d 2  3log n 3log n 3log d  2 2 2 83 % d – The comparisons are minimized when d = 4:d-aryheaps 16 Relative Speed A quaternary heap requires 41 5 5  3log 4 3 2 6  2 of the comparisons required for a binary heap – It should be 16.67 % fasterd-aryheaps 17 Relative Speed From binary heaps, however, a push was Q(1) on average – At least half the entries are stored in leaf nodes Assuming an equal number of pushes and pops, we expect a run time of d log (n) d dn log  d d  Thus, 2lognd 2log  22 This suggests using a ternary 94 % heap—not a quaternary heap dd-aryheaps 18 Relative Speed In order to test this, 1.5 billion pushes and pops were performed on similar implementations of binary, ternary, quaternary, and quinary min heaps with two cases http://ece.uwaterloo.ca/dwharder/aads/Algorithms/d-ary_heaps/d-aryheaps 19 Relative Speed Using the worst-case insertions: every newly inserted entry has higher priority than all other entries in the heap: – The time closely follows the pattern we expect – Percent relative to a binary heap d1 Expected time 3log d  2 Actual timed-aryheaps 20 Relative Speed However, if we make random insertions, we get closer to the other expected pattern—a ternary tree appears to be better – Percent relative to a binary heap d Expected time 2log d  2 Actual time
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