Max- coloring in trees SRIRAM V.PEMMARAJU AND RAJIV RAMAN BY JAYATI JENNIFER LAW.

Slides:



Advertisements
Similar presentations
Chapter 5: Tree Constructions
Advertisements

Connectivity - Menger’s Theorem Graphs & Algorithms Lecture 3.
Covers, Dominations, Independent Sets and Matchings AmirHossein Bayegan Amirkabir University of Technology.
Minimum Vertex Cover in Rectangle Graphs
Every edge is in a red ellipse (the bags). The bags are connected in a tree. The bags an original vertex is part of are connected.
Set Cover 資工碩一 簡裕峰. Set Cover Problem 2.1 (Set Cover) Given a universe U of n elements, a collection of subsets of U, S ={S 1,…,S k }, and a cost.
Approximation Algorithms
GRAPH BALANCING. Scheduling on Unrelated Machines J1 J2 J3 J4 J5 M1 M2 M3.
Generalization and Specialization of Kernelization Daniel Lokshtanov.
Bart Jansen, Utrecht University. 2  Max Leaf  Instance: Connected graph G, positive integer k  Question: Is there a spanning tree for G with at least.
Polynomial Time Approximation Schemes Presented By: Leonid Barenboim Roee Weisbert.
Complexity 16-1 Complexity Andrei Bulatov Non-Approximability.
Advanced Topics in Algorithms and Data Structures 1 Rooting a tree For doing any tree computation, we need to know the parent p ( v ) for each node v.
Computability and Complexity 23-1 Computability and Complexity Andrei Bulatov Search and Optimization.
1 Discrete Structures & Algorithms Graphs and Trees: II EECE 320.
Graph Algorithms: Minimum Spanning Tree We are given a weighted, undirected graph G = (V, E), with weight function w:
Greedy Algorithms Reading Material: Chapter 8 (Except Section 8.5)
CSE 421 Algorithms Richard Anderson Lecture 4. What does it mean for an algorithm to be efficient?
Optimization problems INSTANCE FEASIBLE SOLUTIONS COST.
Algorithms for Maximum Induced Matching Problem Somsubhra Sharangi Fall 2008 CMPT 881.
Vertex cover problem S  V such that for every {u,v}  E u  S or v  S (or both)
Greedy Algorithms Like dynamic programming algorithms, greedy algorithms are usually designed to solve optimization problems Unlike dynamic programming.
Using Homogeneous Weights for Approximating the Partial Cover Problem
Design and Analysis of Algorithms Minimum Spanning trees
TECH Computer Science Graph Optimization Problems and Greedy Algorithms Greedy Algorithms  // Make the best choice now! Optimization Problems  Minimizing.
1 Introduction to Approximation Algorithms. 2 NP-completeness Do your best then.
Design Techniques for Approximation Algorithms and Approximation Classes.
1 Introduction to Approximation Algorithms. 2 NP-completeness Do your best then.
Trees and Distance. 2.1 Basic properties Acyclic : a graph with no cycle Forest : acyclic graph Tree : connected acyclic graph Leaf : a vertex of degree.
1 Section 1.4 Graphs and Trees A graph is set of objects called vertices or nodes where some pairs of objects may be connected by edges. (A directed graph.
Batch Scheduling of Conflicting Jobs Hadas Shachnai The Technion Based on joint papers with L. Epstein, M. M. Halldórsson and A. Levin.
Lecture 8 Tree.
Princeton University COS 423 Theory of Algorithms Spring 2001 Kevin Wayne Approximation Algorithms These lecture slides are adapted from CLRS.
 Rooted tree and binary tree  Theorem 5.19: A full binary tree with t leaves contains i=t-1 internal vertices.
1/24 Introduction to Graphs. 2/24 Graph Definition Graph : consists of vertices and edges. Each edge must start and end at a vertex. Graph G = (V, E)
Discrete Mathematics Chapter 5 Trees.
The Manhattan Tourist Problem Shane Wood 4/29/08 CS 329E.
Vertex Coloring Distributed Algorithms for Multi-Agent Networks
CSE 421 Algorithms Richard Anderson Winter 2009 Lecture 5.
Algorithms for hard problems Introduction Juris Viksna, 2015.
Lecture 19 Minimal Spanning Trees CSCI – 1900 Mathematics for Computer Science Fall 2014 Bill Pine.
Steiner Tree Problem Given: A set S of points in the plane = terminals
Graphs Definition: a graph is an abstract representation of a set of objects where some pairs of the objects are connected by links. The interconnected.
CSE 421 Algorithms Richard Anderson Autumn 2015 Lecture 5.
Power domination in block graphs Guangjun Xu Liying Kang Erfang Shan Min Zhao.
5.6 Prefix codes and optimal tree Definition 31: Codes with this property which the bit string for a letter never occurs as the first part of the bit string.
Trees.
Minimum Spanning Tree Chapter 13.6.
Discrete Mathematicsq
12. Graphs and Trees 2 Summary
Algorithms for hard problems
COMP 6/4030 ALGORITHMS Prim’s Theorem 10/26/2000.
CS 3343: Analysis of Algorithms
Lecture 12 Algorithm Analysis
Advanced Algorithms Analysis and Design
Computability and Complexity
CSE 421: Introduction to Algorithms
First Fit Coloring of Interval Graphs
Introduction Wireless Ad-Hoc Network
Lecture 11 Overview Self-Reducibility.
Problem Solving 4.
Richard Anderson Autumn 2016 Lecture 5
Algorithms Lecture # 27 Dr. Sohail Aslam.
Lecture 12 Algorithm Analysis
Richard Anderson Winter 2019 Lecture 6
Richard Anderson Lecture 5 Graph Theory
Lecture 11 Graph Algorithms
Richard Anderson Winter 2019 Lecture 5
A Variation of Minimum Latency Problem on Path, Tree and DAG
Richard Anderson Autumn 2015 Lecture 6
Presentation transcript:

Max- coloring in trees SRIRAM V.PEMMARAJU AND RAJIV RAMAN BY JAYATI JENNIFER LAW

INTRODUCTION  Classical vertex coloring problem : coloring the vertices of a graph such that no two adjacent vertices share the same color and minimize the no of colors used.  Max – coloring problem : computing a legal coloring of the vertices of a graph with vertex weights w such that ∑Max vЄCi w(v i ) where i = {1,2,3,…..,k} is minimized,where C 1, C 2, C 3... C k are various color classes.

 Batch scheduling where edges capture conflict between pair of jobs and vertices are the number of jobs using same resources.  Memory management in which the total buffer size has to be minimized. Applications:

Objective:  To minimize the sum of maximum weights of each color class

Max – Coloring Trees:  Lemma 1 : For any arbitrary graph G the optimal max- coloring {C 1, C 2,....., C k } of G with w i = weight(C i ) and w 1 >=w 2 >=......>=w k, there is w i >= ∑ k j=i+1 w j, i=1,2,…..,k-1.  Corollary 1 : In any optimal max-coloring of G coloring {C 1, C 2,....., C k } of G with w i = weight(C i ) and w 1 >=w 2 >=......>=w k,we have w i /2 >= w i+2 for i=1,2,…,k-1.  Lemma 2 : If G be a vertex- weighted graph with maximum vertex degree ∆ then χ mc (G) <= ∆+1.

 Lemma 3 : If T is a vetex weighted tree on n vertices then χ mc (T) <= floor(log 2 n) +1  Lemma 4 : Let W be the ratio of the weight of heaviest vertex to least heaviest vertex in a vertex- weighted tree T then χ mc (T) <= floor(log 2 W) +1.

Construction of tree:  To construct tree Ti, start with Ti-1 and set of new vertices {v 1,v 2, …, v k }, each will weight 2 i and edges {u i,v i } for all i = 1,2,…,k.  For example T 0 is the single vertex tree with weight 1 and the sequence of tree T 1, T 2, T 3, … can be constructed from it.

 |V(T n )| = 2 n and the maximum vertex degree of T n is ∆(T n ) = n -1 for n=1,2,...k.  Since the number of colors are floor( log n)+1, which is giving the sub-exponential algorithm of O(n floor(log n) +1).

PTAS:  Take a reduction factor ε > 0 and a constant integer c > 0 such that ε >= (2 log c + 3).  Let a = (w i -1)/c and I 1, I 2,...., I c be the partition of the range [1, w 1 ) where I i = [ 1+(i-1)a, 1+ ia) for 1 <= I <= c.  Let T’ be the tree with reduced weights w’ such that if w(v) Є i j then w’(v) = 1+(j-1).a Except for the maximum weight w 1 all the weights are rounded down.  The weights used to color the tree using PTAS is OPT’ <= (1+ε)OPT

Feasible k coloring:  The algorithm is as follows :  Input:  A tree T with weight function w and a sequence such that W 1 >= W 2 >= … >=W K  Output:  Coloring of the tree into color classes A 1,…,A k such that w(v)<= W i

 Procedure:  Take an empty subset S(v) of feasible colors of T(v).  Do a post –order traversal of T for each vertex.  Find a set of colors for each node by removing the forbidden colors f(v)={ j|w(v)>W j }. If S(v) is empty return “no feasible coloring”.  From the set of feasible coloring set color i for root r.  Do pre-order traversal for all the children of r such that S(v)-color(parent(v)) and set feasible colors to those children.

Running Time:  O(nk) running time of feasible k-coloring algorithm.  There is an O(n + 1/ε) approximation for max - coloring. PTAS :

Questions?