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Dynamic Sets and Data Structures

Dynamic Sets and Data Structures. Over the course of an algorithm’s execution, an algorithm may maintain a dynamic set of objects The algorithm will perform operations on this set Queries Modifying operations We must choose a data structure to implement the dynamic set efficiently

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Dynamic Sets and Data Structures

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  1. Dynamic Sets and Data Structures • Over the course of an algorithm’s execution, an algorithm may maintain a dynamic set of objects • The algorithm will perform operations on this set • Queries • Modifying operations • We must choose a data structure to implement the dynamic set efficiently • The “correct” data structure to choose is based on • Which operations need to be supported • How frequently each operation will be executed

  2. Some Example Operations • Notation • S is the data structure • k is the key of the item • x is a pointer to the item • Search(S,k): returns pointer to item • Insert(S,x) • Delete(S,x): note we are given a pointer to item • Minimum or Maximum(S): returns pointer • Decrease-key(S,x) • Successor or Predecessor (S,x): returns pointer • Merge(S1,S2)

  3. Basic Data Structures/Containers • Unsorted Arrays • Sorted Array • Unsorted linked list • Sorted linked list • Stack • Queue • Heap

  4. Puzzles • How can I implement a queue with two stacks? • Running time of enqueue? • Dequeue? • How can I implement two stacks in one array A[1..n] so that neither stack overflows unless the total number of elements in both stacks exceeds n?

  5. Case Study: Dictionary • Search(S,k) • Insert(S,x) • Delete(S,x) • Is any one of the data structures listed so far always the best for implementing a dictionary? • Under what conditions, if any, would each be best? • What other standard data structure is often used for a dictionary?

  6. Case Study: Priority Queue • Insert(S,x) • Max(S) • Delete-max(S) • Decrease-key(S,x) • Which data structure seen so far is typically best for implementing a priority queue and why?

  7. Case Study: Minimum Spanning Trees • Input • Weighted, connected undirected graph G=(V,E) • Weight (length) function w on each edge e in E • Task • Compute a spanning tree of G of minimum total weight • Spanning tree • If there are n nodes in G, a spanning tree consists of n-1 edges such that no cycles are formed

  8. Prim’s algorithm • A greedy approach to edge selection • Initialize connected component N to be any node v • Select the minimum weight edge connecting N to V-N • Update N and repeat • Dynamic set in Prim’s algorithm • An item is a node in V-N • The value of a node is its minimum distance to any node in N • A minimum weight edge connecting N to V-N corresponds to the node with minimum value in V-N (Extract minimum) • When v is added to N, we need to update the value of the neighbors of v in V-N if they are closer to v than other nodes in N (Decrease key)

  9. 1 4 2 A B C D 5 6 3 10 2 5 E F G Illustration • Maintain dynamic set of nodes in V-N • If we started with node D, N is now {C,D} • Dynamic set values of other nodes: • A, E, F: infinity • B: 4 • G: 6 • Extract-min: Node B is added next to N

  10. 1 4 2 A B C D 5 6 3 10 2 5 E F G Updating Dynamic Set • Node B is added to N; edge (B,C) is added to T • Need to update dynamic set values of A, E, F • Decrease-key operation • Dynamic set values of other nodes: • A: 1 • E: 2 • F: 5 • G: 6 • Extract-min: Node A is added next to N

  11. Updating Dynamic Set Again • Node A is added to N; edge (A,B) is added to T • Need to update dynamic set values of E • Decrease-key operation • Dynamic set values of other nodes: • E: 2 (unchanged because 2 is smaller than 3) • F: 5 • G: 6 1 4 2 A B C D 5 6 3 10 2 5 E F G

  12. Dynamic Set Analysis • How many objects in initial dynamic set representation of V-N? • How many extract-min operations need to happen? • How many decrease-key operations may occur? • Given all of the above, choose a data structure and tell me the implementation cost. • Time to build initial dynamic set • Time to implement all extract-min operations • Time to implement all decrease-key operations

  13. Kruskal’s Algorithm • A greedy approach to edge selection • Initialize tree T to have no edges • Iterate through the edges starting with the minimum weight one • Add the edge (u,v) to tree T if this does not create a cycle

  14. 1 1 1 4 4 4 8 8 8 A A A B B B C C C D D D 7 7 7 6 6 6 2 2 2 9 9 9 3 3 3 5 5 5 1 4 8 E E E F F F G G G A B C D 7 6 2 9 3 5 E F G Example • (A,B) • (A,E) • (B,E): cycle • (B,C) • (F,G) • (C,G) • (B,F): cycle • (C,D) • (D,G): cycle

  15. Disjoint Set Data Structure • Given a universe U of objects (nodes V) • Maintain a collection of disjoint sets Si that partition U • Find-set(x): Returns set Si that contains x • Merge(Si, Sj): Returns new set Sk = Si union Sj • Disjoint Sets and Kruskal’s algorithm • Universe U is the set of vertices V • The sets are the current connected components • When an edge (u,v) is considered, we check for a cycle by determining if u and v belong to the same set • 2 calls to Find-set(x) • If we add (u,v) to T, we need to merge the 2 sets represented by u and v. • Merge(Su,Sv)

  16. Analysis • How do we initialize the universe? • How many calls to find-set do we perform? • How many calls to merge-set do we perform?

  17. Better data structures • We need mergeable data structures that still support fast searches • Binomial heaps (ch. 19) • Fibonacci heaps (ch. 20) • Disjoint set data structures (ch. 21) • linked lists • forests

  18. Disjoint-set forests • Representation • Each set is represented as a tree, nodes point to parent • Root element is the representative for the set, points to self or has null parent pointer • Height: maintain height of tree as an integer • Operations • Makeset: make a tree with one node • Find: progress from current element to root element following links • Union: connect root of lower height tree to point to root of larger height tree • Figures copied from Jeff Erickson, UIUC

  19. Naïve implementation Figure copied from Jeff Erickson’s slides at UIUC.

  20. union-by-rank or union-by-depth Leads to height of any tree of n nodes being at most O(lg n). Figure copied from Jeff Erickson’s slides at UIUC.

  21. Path Compression Leads to amortized cost of α(n), the inverse ackerman function. For all practical purposes, α(n) ≤ 4. Figure copied from Jeff Erickson’s slides at UIUC.

  22. Binomial Heaps • Binomial Tree • Binomial Heap • Figures copied from Dan Gildea, University of Rochester

  23. Key idea: Union in O(lg n) time

  24. Binomial Trees Tree Bk has 2k nodes. Bk has height k. Children of the root of Bk are Bk-1, Bk-2, …, B0 from left to right. Max degree of an n-node binomial tree is lg n.

  25. Binomial Heap • A binomial heap of n-elements is a collection of binomial trees with the following properties: • Each binomial tree is heap-ordered (parent is less than all children) • No two binomial trees in the collection have the same size • Number of trees will be O(lg n)

  26. Example Binomial Heap Binomial heap of 29 elements 29 = 11101 in binary.

  27. Minimum Operation Where does the minimum have to be? How can we find minimum in general? Running time?

  28. Union of 2 Binomial Heaps

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