1 / 47

Application of Data Structures

Application of Data Structures. Overview. Priority Queue structures Heaps Application: Dijkstra’s algorithm Cumulative Sum Data Structures on Intervals Augmenting data structures with extra info to solve questions. Priority Queue (PQ) Structures.

Audrey
Télécharger la présentation

Application of Data Structures

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Application of Data Structures Christopher Moh 2005

  2. Overview • Priority Queue structures • Heaps • Application: Dijkstra’s algorithm • Cumulative Sum Data Structures on Intervals • Augmenting data structures with extra info to solve questions Christopher Moh 2005

  3. Priority Queue (PQ) Structures • Stores elements in a list by comparing a key field • Often has other satellite data • For example, when sorting pixels by their R value, we consider the R as the key field and GB as satellite data • Priority queues allow us to sort elements by their key field. Christopher Moh 2005

  4. Common PQ operations • Create() • Creates an empty priority queue • Find_Min() • Returns the smallest element (by key field) • Insert(x) • Insert element x (with predefined key field) • Delete(x) • Delete position x from the queue • Change(x, k) • Change key field of position x to k Christopher Moh 2005

  5. Optional PQ operations • Union (a,b) • Combines two PQs a and b • Search (k) • Returns the position of the element in the heap with key value k Christopher Moh 2005

  6. Considerations when implementing a PQ in competition • How complicated is it? • Is the code likely to be buggy? • How fast does it need to be? • Does a constant factor also come into the equation? • Do I need to store extra data to do a Search? • During the course of this presentation, we shall assume that there exists existing extra data which allows us to do a search in O(1) time. The handling of this data structure will be assumed and not covered. Christopher Moh 2005

  7. Linear Array • Unsorted Array • Create, Insert, Change in O(1) time • Find_min, Delete in O(n) time • Sorted Array • Create, Find_min in O(1) time • Insert, Delete, Change in O(n + log n) = O(n) time Christopher Moh 2005

  8. Binary Heaps • Will be the most common structure that will be implemented in competition setting • Efficient for most applications • Easy to implement • A heap is a structure where the value of a node is less than the value of all of its children • A binary heap is a heap where the maximum number of children for each node is 2. Christopher Moh 2005

  9. Array implementation • Consider a heap of size nheap in an array BHeap[1..nheap] (Define BHeap[nheap+1 .. (nheap*2)+1] to be INFINITY for practical reasons) • The children of BHeap[x] are BHeap[x*2] and BHeap[x*2+1] • The parent of BHeap[x] are BHeap[x/2] • This allows a near uniform Binary Heap where we can ensure that the number of levels in this heap is O(log n) • Some properties wrt Key values: BHeap[x] >= BHeap[x/2], BHeap[x] <= BHeap[x*2], BHeap[x] <= BHeap[x*2+1], BHeap[x*2] ?? BHeap[x*2+1] Christopher Moh 2005

  10. PQ Operations on a BHeap • We define BTree(x) to be the Binary Tree rooted at BHeap[x] • We define Heapify(x) to be an operation that does the following: • Assume: BTree(x*2) and BTree(x*2+1) are binary heaps but BTree(x) is not necessarily a binary heap • Produce: BTree(x) binary heap • Details of Heapify in later slides – but for now, we assume Heapify is O(log n) • For the rest of the presentation, we assume the variable n refers to nheap Christopher Moh 2005

  11. Operations on a BHeap • Create is trivial – O(1) time • Find_min: • Return BHeap[1] • O(1) time • Insert (element with key value x) • nheap++ • BHeap[nheap] = x • T = nheap • While (T != 1 && Bheap[T] < BHeap[T/2]) • Swap (Bheap[T], BHeap[T/2] • T = T / 2 • O(log n) time as the number of levels is O(log n) Christopher Moh 2005

  12. Operations on a BHeap • ChangeDown (position x, new key value k) • Assume: k < existing BHeap[x] • BHeap[x] = k • T = x • While (T != 1 && BHeap[T] < BHeap[T/2]) • Swap (BHeap[T], BHeap[T/2]) • T = T/2 • Complexity: O(log n) • This procedure is known as “bubbling up” the heap Christopher Moh 2005

  13. Operations on a BHeap • ChangeUp (position x, new key value k) • Assume: k > existing BHeap[x] • BHeap[x] = k • Heapify(x) • O(log n) as complexity of Heapify is O(log n) Christopher Moh 2005

  14. Operations on a BHeap • Delete (position x on the heap) • BHeap[x] = BHeap[nheap] • nheap— • Heapify(x) • T = x • While (T != 1 && BHeap[T] < BHeap[T/2]) • Swap (BHeap[T], BHeap[T/2]) • T = T / 2 • Complexity is O(log n) • Why must I do both Heapify and “bubble up”? Christopher Moh 2005

  15. Operations on a BHeap • Heapify (position x on the heap) • T = min(BHeap[x], BHeap[x*2], BHeap[x*2+1]) • If (T == BHeap[x]) return; • K = position where BHeap[K] = T • Swap(BHeap[x], BHeap[K]) • Heapify(K) • O(log n) as the maximum number of levels in the heap is O(log n) and Heapify only goes through each level at most once Christopher Moh 2005

  16. BHeap Operations: Summary • Create, Find_min in O(1) time • Change (includes both ChangeUp and ChangeDown), Insert, and Delete are O(log n) time • Union operations are how long? • Insertion: O(n log n) union • Heapify: O(n) union Christopher Moh 2005

  17. Corollary: Heapsort • We can convert an unsorted array to a heap using Heapify (why does this work?): • For (i = n/2; i >= 1; i--) • Heapify(i) • We can then return a sorted list (list initially empty): • For (i = 1; i <= n; i++) • Append the value of find_min to the list • Delete(1) • Complexity is O(n log n) Christopher Moh 2005

  18. Binomial Trees • Define Binomial Tree B(k) as follows: • B(0) is a single node • B(n), n != 0, is formed by merging two B(n-1) trees in the following way: • The root of the B(n) tree is the root of one of the B(n-1) trees, and the (new) leftmost child of this root is the root of the other B(n-1) tree. • Within the tree, the heap property holds i.e. that the key field of any node is greater than the key field of all its children. Christopher Moh 2005

  19. Properties of Binomial Trees • The number of nodes in B(k) is exactly 2^k. • The height of B(k) is exactly (k + 1) • For any tree B(k) • The root of B(k) has exactly k children • If we take the children of B(k) from left to right, they form the roots of a B(k-1), B(k-2), …, B(0) tree in that order Christopher Moh 2005

  20. Binomial Heaps • Binomial Heaps are a forest of binomial trees with the following properties: • All the binomial trees are of different sizes • The binomial trees are ordered (from left to right) by increasing size • If we consider the fact that the size of B(k) is 2^k, the binomial tree B(k) exists in a binomial heap of n nodes iff the bit representing 2^k is “1” in the binary representation of n • For example: 13 (decimal) = 1101 (binary), so the binomial heap with 13 nodes consists of the binomial trees B(0), B(2), and B(3). Christopher Moh 2005

  21. Binomial Heap Implementation • Each node will store the following data: • Key field • Pointers (if non-existent, points to NIL) to • Parent • Next Sibling (ordered left to right; a sibling must have the same parent); For roots of binomial trees, next sibling points to the root of the next binomial tree • Leftmost child • Number of children in field degree • Any other data that might be useful for the program • The binomial heap is represented by a head pointer that points to the root of the smallest binomial tree (which is the leftmost binomial tree) Christopher Moh 2005

  22. Operations on Binomial Trees • Link (h1, h2) • Links two binomial trees with root h1 and h2 of the same order k to form a new binomial tree of order (k+1) • We assume h1->key < h2->key which implies that h1 is the root of the new tree • T = h1->leftchild • h1->leftchild = h2 • h2->parent = h1 • H2->next_sibling= T • O(1) time Christopher Moh 2005

  23. Operations on binomial heaps • Create – Create a new binomial heap with one node (key field set) • Set Parent, Leftchild, Next sibling to NIL • O(1) time • Find_min • X = head, min = INFINITY • While (X != nil) • If (X->key < min) min = X->key • X = X->next_sibling • Return min • O(log n) time as there are at most log n binomial trees (log n bits) Christopher Moh 2005

  24. More Operations • Merge (h1, h2, L) • Given binomial heaps with head pointers h1 and h2, create a list L of all the binomial trees of h1 U h2 arranged in ascending order of size • For any order k, there may be zero, one, or two binomial trees of order k in this list. Christopher Moh 2005

  25. More Operations • Merge (h1, h2, L) • Assume that NIL is a node of infinitely small order • L = empty • While (h1 != NIL || h2 != NIL) • If (h1->degree < h2->degree) • Append the (binomial)tree with root h1 to L • h1 = h1->next_sibling • Else • Apply above steps to h2 instead Christopher Moh 2005

  26. More Operations • Union (h1, h2) • The fundamental operation involving binomial heaps • Takes two binomial heaps with head pointers h1 and h2 and creates a new binomial heap of the union of h1 and h2 Christopher Moh 2005

  27. More Operations • Union (h1, h2) • Start with empty binomial heap • Merge (h1, h2, L) • Go by increasing k in the list L until L is empty • If there is exactly one or exactly three (how can this happen?) binomial trees of order k in L, append one binomial tree of order k to the binomial heap and remove that tree from L • If there are two trees of order k, remove both trees, use Link to form a tree of order (k+1) and pre-pend this tree to L • Union is O(log n) Christopher Moh 2005

  28. More Operations • Inserting a new node with key field set • Create a new binomial heap with that one node • Union (existing heap with head h, new heap) • O (log n) time • ChangeDown (node at position x, new value) • Decreasing the key value of a node • Same idea as binary heap: “Bubble” up the binomial tree containing this node (exchange only key fields and satellite data! What’s the complexity if you physically change the node?) • O (log n) time Christopher Moh 2005

  29. More Operations • Delete (node at position x) • Deleting position x from the heap • ChangeDown(x, -INFINITY) • Now x is at the root of its binomial tree • Supposing that the binomial tree is of order k • Recall that the children of the root of the binomial tree, from right to left, are binomial trees of order 0, 1, 2, 3, 4, …, k-1 • Form a new binomial heap with the children of the root of this binomial tree the roots in the new binomial heap • Remove the original binomial tree from the original binomial heap • Union (original heap, new heap) • O(log n) complexity Christopher Moh 2005

  30. More Operations • ChangeUp (node at position X, new value) • Delete (X) • Insert (new value) • O (log n) time Christopher Moh 2005

  31. Summary – Binomial Heaps • Create in O(1) time • Union, Find_min, Delete, Insert, and Change operations take O(log n) time • In general, because they are more complicated, in competition it is far more prudent (saves time coding and debugging) to use a binary heap instead • Unless there are MANY Union operations Christopher Moh 2005

  32. Application of heaps: Dijkstra • The following describes how Dijkstra’s algorithm can be coded with a binary heap • Initializing phase: • Let n be the number of nodes • Create a heap of size n, all key fields initialized to INFINITY • Change_val (s, 0) where s is the source node Christopher Moh 2005

  33. Running of Dijkstra’s algorithm • While (heap is not empty) • X = node corresponding to find_min value • Delete (position of X in heap = 1) • For all nodes k that are adjacent to X • If (cost[X] + distance[X][k] < cost[k]) • ChangeDown (position of k in heap, cost[X] + distance[X][k]) Christopher Moh 2005

  34. Analysis of running time • At most n nodes are deleted • O(n log n) • Let m be the number of edges. Each edge is relaxed at most once. • O(m log n) • Total running time O([m+n] log n) • This is faster than using a basic array list unless the graph is very dense, in which case m is about O(n^2) which leads to a running time of O(n^2 log n) Christopher Moh 2005

  35. Cumulative Sum on Intervals • Problem: We have a line that runs from x coordinate 1 to x coordinate N. At x coordinate X [X an integer between 0 and N], there is g(X) gold. Given an interval [a,b], how much gold is there between a and b? • How efficiently can this be done if we dynamically change the amount of gold and the interval [a,b] keeps changing? Christopher Moh 2005

  36. Cumulative Sum Array • Let us define C(0) = 0, and C(x) = C(x-1) + g(x) where g(x) is the amount of gold at position x • C(x) then defines the total amount of gold from position 1 to position x • The amount of gold in interval [a,b] is simply C(b) – C(a-1) • For any change in a or b, we can perform the update in O(1) time • However, if we change g(x), we will have to change C(x), C(x+1), C(x+2), …, C(N) • Any change in gold results in an update in O(N) time Christopher Moh 2005

  37. Cumulative Sum Tree • We can use the binary representation of any number to come up with a cumulative sum tree • For example, let say we take 13 (decimal) = 1101 (binary) • The cumulative sum of g(1) + g(2) + … g(13) can be represented as the sum of: • g(1) + g(2) + … + g(8) [ 8 elements ] • g(9) + g(10) + … + g(12) [ 4 elements ] • g(13) [ 1 element ] • Notice that the number of elements in each case represents a bit that is “1” in the binary representation of the number Christopher Moh 2005

  38. Cumulative Sum Tree • Another example: C(19) • 19 (decimal) is 10011 (binary) • C(19) is the sum of the following: • g(1) + g(2) + … + g(16) [ 16 elements ] • g(17) + g(18) [ 2 elements ] • g(19) [ 1 element ] Christopher Moh 2005

  39. Cumulative Sum Tree • Let us define C2(x) to be the sum of g(x) + g(x-1) + … + g(p + 1) where p is a number with the same binary representation as x except the least significant bit of x (the rightmost bit of x that is “1”) is “0” • Examples of x and the corresponding p: • x = 6 [110], p = 4 [100] • x = 13 [1101], p = 12 [1100] • x = 16 [10000], p = 0 [00000] Christopher Moh 2005

  40. Cumulative Sum Tree • If we want to find the cumulative sum C(x) = g(1) + g(2) + … + g(x), we can trace through the values of C2 using the binary representation of x • Examples: • C(13) = C2(8) + C2(8+4) + C2(8+4+1) • C(16) = C2(16) • C(21) = C2(16) + C2(16+4) + C2(16+4+1) • C(99) = C2(64) + C2(64+32) + C2(64+32+2) + C2(64+32+2+1) • This allows us to find C(x) in log x time • Hence the amount of gold in interval [a,b] = C(b) – C(a-1) can be found in log N time, which implies updates of a and b can be done in O(log N) Christopher Moh 2005

  41. Cumulative Sum Tree • What happens when we change g(x)? • If g(x) is changed, we only need to update C2(y) where C2(y) covers g(x) • We can go through all necessary C2(y) in the following way: • While (x <= N) • Update C2(x) • Add the value of the least significant bit of x to x • This runs in O(log N) time • Hence updates to g can also be done in O(log n) time, which is a great improvement over the O(N) needed for an array. Christopher Moh 2005

  42. Cumulative Sum Tree • Examples [binary representation in brackets] • Change to g(5) [ 101 ] : Update C2(5), C2(6), C2(8), C2(16) and all C2(power of 2 > 16) • Change to g(13) [ 1101 ]: Update C2(13), C2(14), C2(16), and all C2(power of 2 > 16) • Change to g(35) [ 100011 ]: Update C2(35), C2(36), C2(40), C2(48), C2(64), and all C2(power of 2 > 64) • We can implement a cumulative sum tree very simply: By simply using a linear array to store the values of C2. • Can we extend a cumulative sum tree to 2 or more dimensions? • See IOI 2001 Day 1 Question 1 Christopher Moh 2005

  43. Sum of Intervals Tree • Another way to solve the question is to use a “Sum of Intervals” Binary Tree • Each node in the tree is represented by (L, R) and the value of (L,R) is g(L) + g(L+1) + … + g(R) • The root of the tree has L = 1 and R = N • Every leaf has L = R • Every non-leaf has children (L, [L+R]/2) [left child] and ([L+R]/2+1, R) [right child] • The number of nodes in the tree is O(2*N) [ why? ] • In an implementation, every node should have pointers to its children and its parent Christopher Moh 2005

  44. Sum of Intervals Tree • How to find C(x) = g(1) + g(2) + … + g(x)? • We trace from the root downwards • L = 1, R = N, C = 0 • While (L != R) • M = (L + R) / 2 • If (M < x) • C += value of (L,R) • Set L and R to the left child of the current node • Else • Set L and R to the right child of the current node • C += value at (L,R) [ or (L,L) or (R,R) as L = R ] • Time complexity: O(log n) Christopher Moh 2005

  45. Sum of Intervals Tree • What happens when g(x) is changed? • Trace from (x,x) upwards to the root • Let L = R = x • While (L,R) is not the root • Update the value of (L,R) • Set (L,R) to the parent of (L,R) • Update the root • Complexity of O(log N) • Hence all updates of interval [a,b] and g(x) can be done in O(log N) time Christopher Moh 2005

  46. Augmenting Data Structures • It is often useful to change the data structure in some way, by adding additional data in each node or changing what each node represents. • This allows us to use the same data structure to solve problems • For example, we can use so-called “interval trees” to solve not just cumulative sum problems • We can use properties of elements in the interval (L,R) that are related to L and R. Christopher Moh 2005

  47. Other data structures • Balanced (and unbalanced) binary trees • Red-Black trees • 2-3-4 trees • Splay trees • Suffix Trees • Fibonacci Heaps Christopher Moh 2005

More Related