 # Chapter 9 Greedy Technique. Constructs a solution to an optimization problem piece by piece through a sequence of choices that are: b feasible - b feasible.

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Chapter 9 Greedy Technique

Constructs a solution to an optimization problem piece by piece through a sequence of choices that are: b feasible - b feasible - has to satisfy the problems constraints b locally optimal - b locally optimal - has to be the best choice among all feasible choices available at that step b Irrevocable b Irrevocable – once made, it cannot be changed on subsequent steps of the algorithm For some problems, yields an optimal solution for every instance. For most, does not but can be useful for fast approximations.

Applications of the Greedy Strategy b Optimal solutions: change making for normal coin denominationschange making for normal coin denominations minimum spanning tree (MST)minimum spanning tree (MST) single-source shortest pathssingle-source shortest paths simple scheduling problemssimple scheduling problems Huffman codesHuffman codes b Approximations: traveling salesman problem (TSP)traveling salesman problem (TSP) knapsack problemknapsack problem other combinatorial optimization problemsother combinatorial optimization problems

Change-Making Problem Given unlimited amounts of coins of denominations d 1 > … > d m, give change for amount n with the least number of coins Example: d 1 = 25c, d 2 =10c, d 3 = 5c, d 4 = 1c and n = 48c Greedy solution: Greedy solution is b optimal for any amount and normal set of denominations b may not be optimal for arbitrary coin denominations

Minimum Spanning Tree (MST) b Spanning tree of a connected graph G: a connected acyclic subgraph of G that includes all of Gs vertices b Minimum spanning tree of a weighted, connected graph G: a spanning tree of G of minimum total weight Example: c d b a 6 2 4 3 1

Prims MST algorithm b Start with tree T 1 consisting of one (any) vertex and grow tree one vertex at a time to produce MST through a series of expanding subtrees T 1, T 2, …, T n b On each iteration, construct T i+1 from T i by adding vertex not in T i that is closest to those already in T i (this is a greedy step!) b Stop when all vertices are included

Example c d b a 4 2 6 1 3

Notes about Prims algorithm b Proof by induction that this construction actually yields MST b Needs priority queue for locating closest fringe vertex b Efficiency O(n 2 ) for weight matrix representation of graph and array implementation of priority queueO(n 2 ) for weight matrix representation of graph and array implementation of priority queue O(m log n) for adjacency list representation of graph with n vertices and m edges and min-heap implementation of priority queueO(m log n) for adjacency list representation of graph with n vertices and m edges and min-heap implementation of priority queue

Induction Basis step: T 0 consists of a single vertex and hence must be part of any minimum spanning tree. Inductive step: Assume that T i-1 is part of some minimum spanning tree. Need to prove that T i, generated from T i-1 by Prims algorithm, is also a part of a minimum spanning tree. Proof by contradiction: assume that no minimum spanning tree of the graph can contain T i Let e i =(v, u) be the minimum weight edge in T i-1 to a vertex not in T i-1 used by Prims algorithm to expand T i-1 to T i. By our assumption, e i cannot belong to the minimum spanning tree T. Therefore, if we add e i to T, a cycle must be formed.

Another greedy algorithm for MST: Kruskals b Sort the edges in nondecreasing order of lengths b Grow tree one edge at a time to produce MST through a series of expanding forests F 1, F 2, …, F n-1 b On each iteration, add the next edge on the sorted list unless this would create a cycle. (If it would, skip the edge.)

Example c d b a 4 2 6 1 3

Notes about Kruskals algorithm b Algorithm looks easier than Prims but is harder to implement (checking for cycles!) b Cycle checking: a cycle is created iff added edge connects vertices in the same connected component b Union-find algorithms – see section 9.2

Kruskals Algorithm re-thought b View the algorithms operations as a progression through a series of forests Containing all of the vertices of a graph andContaining all of the vertices of a graph and Some its edgesSome its edges b The initial forest consists of |V| trivial trees, each comprising a single vertex of the graph On each iteration the algorithm takes the next edge (u,v) from the sorted list of the graphs edgesOn each iteration the algorithm takes the next edge (u,v) from the sorted list of the graphs edges Finds the trees containing the vertices u and v, andFinds the trees containing the vertices u and v, and If these trees are not the same, unites them in a larger tree by adding the edge (u,v)If these trees are not the same, unites them in a larger tree by adding the edge (u,v) b With an efficient sorting algorithm, Kruskal is O(|E| log |E|)

Kruskal and Union-Find b Union-find algorithms for checking whether two vertices belong to the same tree b Partition the n-element set S into a collection of n one- element subsets, each containing a different element of S b The collection is subjected to a sequence of intermixed union and find operations b ADT: makeset(x) creates a one element set {x}makeset(x) creates a one element set {x} find(x) returns a subset contain xfind(x) returns a subset contain x union (x, y) constructs the union of disjoint subsets Sx and Sy, containing x and y, and adds it to the collection to replace Sx and Sy which are deleted from itunion (x, y) constructs the union of disjoint subsets Sx and Sy, containing x and y, and adds it to the collection to replace Sx and Sy which are deleted from it

Minimum spanning tree vs. Steiner tree c db a 1 11 1 c db a vs See problem 11 on page 323

Shortest paths – Dijkstras algorithm Single Source Shortest Paths Problem: Given a weighted connected graph G, find shortest paths from source vertex s to each of the other vertices Dijkstras algorithm: Similar to Prims MST algorithm, with a different way of computing numerical labels: Among vertices not already in the tree, it finds vertex u with the smallest sum d v + w(v,u) d v + w(v,u)where v is a vertex for which shortest path has been already found on preceding iterations (such vertices form a tree) v is a vertex for which shortest path has been already found on preceding iterations (such vertices form a tree) d v is the length of the shortest path from source to v w(v,u) is the length (weight) of edge from v to u d v is the length of the shortest path from source to v w(v,u) is the length (weight) of edge from v to u

Dijkstras Algorithm (G,l) G is the graph, s is the start node l is the length of an edge Let S be the set of explored nodes For each u in S, we store the distance d(u) Initially S = {s} and d(s) = 0 While S is not equal to V Select a node v not in S with at least one edge from S for which d(v) = min e=(u,v):u is in S d(u) + l e is as small as possible Add v to S and define d(v) = d(v) EndWhile

Example d 4 Tree vertices Remaining vertices a(-,0) b(a,3) c(-,) d(a,7) e(-,) a b 4 e 3 7 6 2 5 c a b d 4 c e 3 7 4 6 2 5 a b d 4 c e 3 7 4 6 2 5 a b d 4 c e 3 7 4 6 2 5 b(a,3) c(b,3+4) d(b,3+2) e(-,) d(b,5) c(b,7) e(d,5+4) c(b,7) e(d,9) e(d,9) d a b d 4 c e 3 7 4 6 2 5

Notes on Dijkstras algorithm b Doesnt work for graphs with negative weights b Applicable to both undirected and directed graphs b Efficiency O(|V| 2 ) for graphs represented by weight matrix and array implementation of priority queueO(|V| 2 ) for graphs represented by weight matrix and array implementation of priority queue O(|E|log|V|) for graphs represented by adj. lists and min-heap implementation of priority queueO(|E|log|V|) for graphs represented by adj. lists and min-heap implementation of priority queue b Dont mix up Dijkstras algorithm with Prims algorithm!

Coding Problem Coding: assignment of bit strings to alphabet characters Codewords: bit strings assigned for characters of alphabet Two types of codes: b fixed-length encoding (e.g., ASCII) b variable-length encoding (e,g., Morse code) Prefix-free codes: no codeword is a prefix of another codeword Problem: If frequencies of the character occurrences are known, what is the best binary prefix-free code? known, what is the best binary prefix-free code?

Huffman Codes b Labeling the tree T*: We label all the leaves of depth 1 (if there are any) and label them with the highest-frequency letters in any order.We label all the leaves of depth 1 (if there are any) and label them with the highest-frequency letters in any order. We then take all leaves of depth 2 (if there are any) and label them with the next highest-frequency letters in any order.We then take all leaves of depth 2 (if there are any) and label them with the next highest-frequency letters in any order. Continue through the leaves in order of increasing depthContinue through the leaves in order of increasing depth b There is an optimal prefix code, with corresponding tree T*, in which the two lowest-frequency letters are assigned to leaves that are siblings in T*. Delete them and label their parent with a new letter having the combined frequencyDelete them and label their parent with a new letter having the combined frequency Yields an instance with a smaller alphabetYields an instance with a smaller alphabet

Huffmans Algorithm To construct a prefix code for an alphabet S, with given frequencies: If S has two letters then encode one letter using 0 and other letter using 1 Else Let y* and z* be the two lowest-frequency letters Form a new alphabet S by deleting y* and z* and replacing them with a new letter of frequency f y* + f z* Recursively construct a prefix code for S, with tree T Define a prefix code for S as follows: Start with T Take the leaf labeled and add two children below it labeled y* and z* Endif0…………………………………………………………………………………………………………………………………………… …………………………………………………………………………………………………………………………………………………… …………………………………………………………………………………………………………………………………………………… …………………….

Huffman codes b Any binary tree with edges labeled with 0s and 1s yields a prefix-free code of characters assigned to its leaves b Optimal binary tree minimizing the expected (weighted average) length of a codeword can be constructed as follows Huffmans algorithm Initialize n one-node trees with alphabet characters and the tree weights with their frequencies. Repeat the following step n-1 times: join two binary trees with smallest weights into one (as left and right subtrees) and make its weight equal the sum of the weights of the two trees. Mark edges leading to left and right subtrees with 0s and 1s, respectively.

Example character AB C D _ frequency 0.35 0.1 0.2 0.2 0.15 codeword 11 100 00 01 101 average bits per character: 2.25 for fixed-length encoding: 3 compression ratio: (3-2.25)/3*100% = 25%

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