Introduction to Algorithms
3rd Edition
ISBN: 9780262033848
Author: Thomas H. Cormen, Ronald L. Rivest, Charles E. Leiserson, Clifford Stein
Publisher: MIT Press
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Chapter 35.1, Problem 5E
Program Plan Intro
To prove that Polynomial- Time Approximation
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Let G be a graph with n vertices. The k-coloring problem is to decide whether the vertices of G can be labeled from the set {1, 2, ..., k} such that for every edge (v,w) in the graph, the labels of v and w are different.
Is the (n-4)-coloring problem in P or in NP? Give a formal proof for your answer. A 'Yes' or 'No' answer is not sufficient to get a non-zero mark on this question.
Give proof that the adapted form of the choice problem has an NP-complete solution. Is there a spanning tree in which the highest possible payment at any vertex does not exceed the target cost, given a graph G and a target cost c?
Recall the Clique problem: given a graph G and a value k, check whether G has a set S of k vertices that's a clique. A clique is a subset of vertices S such that for all u, v € S, uv is an edge of G. The goal of this problem is to establish the NP-hardness of Clique by reducing VertexCover, which is itself an NP-hard problem, to Clique.
Recall that a vertex cover is a set of vertices S such that every edge uv has at least one endpoint (u or v) in S, and the VertexCover problem is given a graph H and a value 1, check whether H has a vertex cover of size at most 1. Note that all these problems are already phrased as decision problems, and you only need to show the NP-Hardness of Clique. In other words, we will only solve the reduction part in this problem, and you DO NOT need to show that Clique is in NP.
Q4.1
Let S be a subset of vertices in G, and let C be the complement graph of G (where uv is an edge in C if and only if uv is not an edge in G).
Prove that for any subset of vertices…
Chapter 35 Solutions
Introduction to Algorithms
Ch. 35.1 - Prob. 1ECh. 35.1 - Prob. 2ECh. 35.1 - Prob. 3ECh. 35.1 - Prob. 4ECh. 35.1 - Prob. 5ECh. 35.2 - Prob. 1ECh. 35.2 - Prob. 2ECh. 35.2 - Prob. 3ECh. 35.2 - Prob. 4ECh. 35.2 - Prob. 5E
Ch. 35.3 - Prob. 1ECh. 35.3 - Prob. 2ECh. 35.3 - Prob. 3ECh. 35.3 - Prob. 4ECh. 35.3 - Prob. 5ECh. 35.4 - Prob. 1ECh. 35.4 - Prob. 2ECh. 35.4 - Prob. 3ECh. 35.4 - Prob. 4ECh. 35.5 - Prob. 1ECh. 35.5 - Prob. 2ECh. 35.5 - Prob. 3ECh. 35.5 - Prob. 4ECh. 35.5 - Prob. 5ECh. 35 - Prob. 1PCh. 35 - Prob. 2PCh. 35 - Prob. 3PCh. 35 - Prob. 4PCh. 35 - Prob. 5PCh. 35 - Prob. 6PCh. 35 - Prob. 7P
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- Given: graph G, find the smallest integer k such that the vertex set V of G contains a set A consisting of k elements satisfying the condition: for each edge of G at least one of its ends is in A. The size of the problem is the number n of vertices in G. Please help answer problems 3 & 4 from the given information. 3. Find an instance for which the suggested greedy algorithm gives an erroneous answer. 4. Suggest a (straightforward) algorithm which solves the problem correctly.arrow_forwardProvide evidence that the modified version of the choice problem has an NP-complete solution; Exists, given a graph G and a target cost c, a spanning tree in which the highest possible payment at any vertex does not exceed the target cost?arrow_forwardProve that this choice problem variation is NP-complete; Does the graph G have a spanning tree where the highest cost paid by any vertex is less than the goal cost c?arrow_forward
- What is the largest and what is the smallest number of distinct solutions the maximum-cardinality-matching problem can have for a bipartite graph G = <V, U, E> with n vertices in each vertex set V and U and at least n edges?arrow_forwardLet G = (V, E) be a connected, undirected graph, and let s be a fixed vertex in G. Let TB be the spanning tree of G found by a bread first search starting from s, and similarly TD the spanning tree found by depth first search, also starting at s. (As in problem 1, these trees are just sets of edges; the order in which they were traversed by the respective algorithms is irrelevant.) Using facts, prove that TB = TD if and only if TB = TD = E, i.e., G is itself a tree.arrow_forwardProve that the decision problem variant is NP-complete; Given a graph G and a target cost c does thereexist a spanning tree where the maximum payment of any vertex is no more than c?arrow_forward
- An independent set of a graph G = (V, E) is a subset V’ is subset of V of verticessuch that each edge in E is incident on at most one vertex in V’. Theindependent-set problem is to find a maximum-size independent set in G. Question: Prove that this decision problem is NP-complete. (Hint: Reduce fromthe clique problem or from the vertex cover problem.)arrow_forwardProve that the following problem is NP-complete: Given a graph G, and an integer k, find whether or not graph G has a spanning degree where the maximum degree of any node is k. (Hint: Show a reduction from one of the following known NP-complete problems: Vertex Cover, Ham Path or SAT.)arrow_forwardhow could someone solce this one? The Chinese postman problem: Consider an undirected connected graph and a given starting node. The Chinese postman has to find the shortest route through the graph that starts and ends in the starting node such that all links are passed. The same problem appears for instance for snow cleaning or garbage collection in a city. For a branch-and-bound algorithm, find a possible lower bound function. (Remark: If the problem is to pass through all nodes of the graph, it is called the travelling salesman problem - which needs different solution algorithms, but also of the branch-and-bound type).).arrow_forward
- Propose an approximation algorithm for solving the independent-setproblem where an independent set of a graph G = (V, E) is a subset V’ is a subset of V of verticessuch that each edge in E is incident on at most one vertex in V’. Theindependent-set problem is to find a maximum-size independent set in G.arrow_forwardThe third-clique problem is about deciding whether a given graph G = (V, E) has a clique of cardinality at least |V |/3.Show that this problem is NP-complete.arrow_forwardConsider a graph G that is comprised only of non-negative weight edges such that (u, v) € E, w(u, w) > 0. Is it possible for Bellman-Ford and Dijkstra's algorithm to produce different shortest path trees despite always producing the same shortest-path weights? Justify your answer.arrow_forward
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