Advanced Data Structures Introduction Brought to you by Max (ICQ:31252512 TEL:61337706) February 5, 2005 Outline • Review of some data structures Array Linked List Sorted Array • New stuff 3 of the most important data structures in OI (and your own programming) Binary Search Tree Heap (Priority Queue) Hash Table Page 2 Review • How to measure the merits of a data structure? • Time complexity of common operations Function Find(T : DataType) : Element Function Find_Min() : Element Procedure Add(T : DataType) Procedure Remove(E : Element) Procedure Remove_Min() Page 3 Review - Array • Here Element is simply the integer index of the array cell • Find(T) Must scan the whole array, O(N) • Find_Min() Also need to scan the whole array, O(N) • Add(T) Simply add it to the end of the array, O(1) • Remove(E) Deleting an element creates a hole Copy the last element to fill the hole, O(1) • Remove_Min() Need to Find_Min() then Remove(), O(N) Page 4 Review - Linked List • Element is a pointer to the object • Find(T) Scan the whole list, O(N) • Find_Min() Scan the whole list, O(N) • Add(T) Just add it to a convenient position (e.g. head), O(1) • Remove(E) With suitable implementation, O(1) • Remove_Min() Need to Find_Min() then Remove(), O(N) Page 5 Review - Sorted Array • Like array, Element is the integer index of the cell • Find(T) We can use binary search, O(logN) • Find_Min() The first element must be the minimum, O(1) • Add(T) First we need to find the correct place, O(logN) Then we need to shift the array by 1 cell, O(N) • Remove(E) Deleting an element creates a hole Need to shift the of array by 1 cell, O(N) • Remove_Min() Can be O(1) or O(N) depending on choice of implementation Page 6 Review - Summary Array Linked List Sorted Array Find O(N) O(N) O(logN) Find_Min O(N) O(N) O(1) Add O(1) O(1) O(N) Remove O(1) O(1) O(N) Remove_Min O(N) O(N) O(1) or O(N) • If we are going to perform a lot of these operations (e.g. N=100000), none of these is fast enough! Page 7 Advanced Data Structures Binary Search Tree Brought to you by Max (ICQ:31252512 TEL:61337706) February 5, 2005 What is a Binary Search Tree? • Use a binary tree to store the data • Maintain this property Left Subtree < Node < Right Subtree Page 9 Binary Search Tree - Implementation • Definition of a Node: Node = Record Left, Right : ^Node; Value : Integer; End; • To search for a value (pseudocode) Node Find(Node N, Value V) :If (N.Value = V) Return N; Else If (V < N.Value) and (V.Left != NULL) Return Find(N.Left); Else If (V > N.Value) and (V.Right != NULL) Return Find(N.Right); Else Return NULL; // not found Page 10 Binary Search Tree - Find Page 11 Binary Search Tree - Remove • Case I : Removing a leaf node Easy • Case II : Removing a node with a single child Replace the removed node with its child • Case III : Removing a node with 2 children Replace the removed node with the minimum element in the right subtree (or maximum element in the left subtree) This may create a hole again Apply Case I or II • Sometimes you can avoid this by using “Lazy Deletion” Mark a node as removed instead of actually removing it Less coding, performance hit not big if you are not doing this frequently (may even save time) Page 12 Binary Search Tree - Remove Page 13 Binary Search Tree - Summary • Add() is similar to Find() • Find_Min() Just walk to the left, easy • Remove_Min() Equivalent to Find_Min() then Remove() • Summary Find() : O(logN) Find_Min() : O(logN) Remove_Min() : O(logN) Add() : O(logN) Remove() : O(logN) The BST is “supposed” to behave like that Page 14 Binary Search Tree - Problems • In reality… All these operations are O(logN) only if the tree is balanced Inserting a sorted sequence degenerates into a linked list • The real upper bounds Find() : O(N) Find_Min() : O(N) Remove_Min() : O(N) Add() : O(N) Remove() : O(N) • Solution AVL Tree, Red Black Tree Use “rotations” to maintain balance Both are difficult to implement, rarely used Page 15 Advanced Data Structures Heap (Priority Queue) Brought to you by Max (ICQ:31252512 TEL:61337706) February 5, 2005 What is a Heap? • A (usually) complete binary tree for Priority Queue Enqueue = Add Dequeue = Find_Min and Remove_Min • Heap Property Every node’s value is greater than those of its decendants Page 17 Heap - Implementation • • Usually we use an array to simulate a heap Assume nodes are indexed 1, 2, 3, ... Parent = [Node / 2] Left Child = Node*2 Right Child = Node*2 + 1 Page 18 Heap - Add • Append the new element at the end • Shift it up until the heap property is restored • Why always works? Page 19 Heap - Remove_Min • Replace the root with the last element • Shift it down until the heap property is restored • Again, why it always works? Page 20 Heap - Build_Heap • There is a special operation called Build_Heap Transform an ordinary into a heap without using extra memory • The Remove_Min operation has two steps Replace the root with a leaf node Restore the heap structure by shifting the node down • This is called “Heapify” • If we apply the Heapify step to ALL internal nodes, bottom to up, we get a heap Page 21 Heap - Build_Heap Page 22 Heap - Summary • Find() is usually not supported by a heap You may scan the whole tree / array if you really want • Remove() is equivalent to applying Remove_Min() on a subtree Remember that any subtree of a heap is also a heap • Summary Find() : O(N) // We usually don’t use Heap for this Find_Min() : O(1) Remove_Min() : O(logN) Add() : O(logN) Remove() : O(logN) Page 23 Advanced Data Structures Hash Table Brought to you by Max (ICQ:31252512 TEL:61337706) February 5, 2005 What is a Hash Table? • Question We have a Mark Six result (6 integers in the range 1..49) We want to check if our bet matches it What is the most efficient way? • Answer Use a boolean array with 49 cells Checking a number is O(1) • Problem What if the range of number is very large? What if we need to store strings? • Solution Use a “Hash Function” to compress the range of values Page 25 Hash Table • Suppose we need to store values between 0 and 99, but only have an array with 10 cells • We can map the values [0,99] to [0,9] by taking modulo 10. The result is the “Hash Value” • Adding, finding and removing an element are O(1) • It is even possible to map the strings to integers, e.g. “ATE” to (1*26*26+20*26+5) mod 10 Page 26 Hash Table - Collision • But this approach has an inherent problem What happens if two data has the same hash value? • Two major methods to deal with this Chaining (Also called Open Hashing) Open Addressing (Also called Closed Hashing) Page 27 Hash Table - Chaining • Keep a link list at each hash table cell • On average, Add / Find / Remove is O(1+a) a = Load Factor = # of stored elements / # of cells • If hash function is “random” enough, usually can get the average case Page 28 Hash Table - Open Addressing • If you don’t want to implement a linked list… • An alternative is to skip a cell if it is occupied • The following diagram illustrates “Linear Probing” Page 29 Hash Table - Open Addressing • Find() must continue until a blank cell is reached • Remove() must use Lazy Deletion, otherwise further operations may fail Page 30 Hash Table - Summary • Find_Min() and Remove_Min() are usually not supported in a Hash Table You may scan the whole tree / array if you really want • For Chaining Find() : O(1+a) Add() : O(1+a) Remove() : O(1+a) • For Open Adressing Find() : O(1 / 1-a) Add() : O(1 / 1-a) Remove() : O(ln(1/1-a)/a + 1/a) • Both are close to O(1) if a is kept small (< 50%) Page 31 Miscellaneous Stuff • Judge problems 1020 – Left Join 1021 – Inner Join 1019 – Addition II • Past contest problems NOI2004 Day 1 – Cashier Any more? • Good place to find related information - Wikipedia http://en.wikipedia.org/wiki/Binary_search_tree http://en.wikipedia.org/wiki/Binary_heap http://en.wikipedia.org/wiki/Hash_table Page 32