undirected graph and a number m, determine if the graph can be colored with at most m colors such that no two adjacent vertices of the graph are colored with the same color.
Graph coloring is a special case of graph labeling. it is an assignment of labels traditionally called "colors" to elements of a graph subject to certain constraints. In its simplest form, it is a way of coloring the vertices of a graph such that no two adjacent vertices are of the same color; this is called a vertex coloring.
Graph coloring is the assignment of colors to the graph vertices and edges in the graph theory. We can
divide the graph coloring in two types. The first is vertex coloring and the second is edge coloring. The
condition which we follow in graph coloring is that the incident vertices/edges have not the same color.
There are some algorithms which solve the problem of graph coloring. Some are offline algorithm and
others are online algorithm. Where offline means the graph is known in advance and the online means that
the edges of the graph are arrive one by one as an input, and We need to color each edge as soon as it is
added to the graph and the main issue is that we want to minimize the number of colors. We cannot change
the color of an edge after colored in an online algorithm. In this paper, we improve the online algorithm
for edge coloring. There is also a theorem which proves that if the maximum degree of a graph is Δ, then it
is possible to color its edges, in polynomial time, using at most Δ+ 1 color. The algorithm provided by
Vizing is offline, i.e., it assumes the whole graph is known in advance. In online algorithm edges arrive one
by one in a random permutation. This online algorithm is inspired by a distributed offline algorithm of
Panconesi and Srinivasan, referred as PS algorithm, works on 2-rounds which we extend by reusing colors
online in multiple rounds.
Greedy Edge Colouring for Lower Bound of an Achromatic Index of Simple Graphsinventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Array is a container which can hold a fix number of items and these items should be of the same type. Most of the data structures make use of arrays to implement their algorithms. Following are the important terms to understand the concept of array.
Graph coloring is a special case of graph labeling. it is an assignment of labels traditionally called "colors" to elements of a graph subject to certain constraints. In its simplest form, it is a way of coloring the vertices of a graph such that no two adjacent vertices are of the same color; this is called a vertex coloring.
Graph coloring is the assignment of colors to the graph vertices and edges in the graph theory. We can
divide the graph coloring in two types. The first is vertex coloring and the second is edge coloring. The
condition which we follow in graph coloring is that the incident vertices/edges have not the same color.
There are some algorithms which solve the problem of graph coloring. Some are offline algorithm and
others are online algorithm. Where offline means the graph is known in advance and the online means that
the edges of the graph are arrive one by one as an input, and We need to color each edge as soon as it is
added to the graph and the main issue is that we want to minimize the number of colors. We cannot change
the color of an edge after colored in an online algorithm. In this paper, we improve the online algorithm
for edge coloring. There is also a theorem which proves that if the maximum degree of a graph is Δ, then it
is possible to color its edges, in polynomial time, using at most Δ+ 1 color. The algorithm provided by
Vizing is offline, i.e., it assumes the whole graph is known in advance. In online algorithm edges arrive one
by one in a random permutation. This online algorithm is inspired by a distributed offline algorithm of
Panconesi and Srinivasan, referred as PS algorithm, works on 2-rounds which we extend by reusing colors
online in multiple rounds.
Greedy Edge Colouring for Lower Bound of an Achromatic Index of Simple Graphsinventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Array is a container which can hold a fix number of items and these items should be of the same type. Most of the data structures make use of arrays to implement their algorithms. Following are the important terms to understand the concept of array.
Chromatic Number of a Graph (Graph Colouring)Adwait Hegde
A graph coloring is an assignment of labels, called colors, to the vertices of a graph such that no two adjacent vertices share the same color. The chromatic number χ(G) of a graph G is the minimal number of colors for which such an assignment is possible.
Distributed coloring with O(sqrt. log n) bitsSubhajit Sahu
Distributed Coloring with O˜(√log n) Bits
K Kothapalli, M Onus, C Scheideler, C Schindelhauer
Proc. of IEEE International Parallel and Distributed Processing Symposium …
We consider the well-known vertex coloring problem: given a graph G, find a coloring of its vertices so that no two neighbors in G have the same color. It is trivial to see that every graph of maximum degree∆ can be colored with∆+ 1 colors, and distributed algorithms that find a (∆+ 1)-coloring in a logarithmic number of communication rounds, with high probability, are known since more than a decade. This is in general the best possible if only a constant number of bits can be sent along every edge in each round. In fact, we show that for the n-node cycle the bit complexity of the coloring problem is
Ω (log n). More precisely, if only one bit can be sent along each edge in a round, then every distributed coloring algorithm (ie, algorithms in which every node has the same initial state and initially only knows its own edges) needs at least Ω (log n) rounds, with high probability, to color the n–node cycle, for any finite number of colors. But what if the edges have orientations, ie, the endpoints of an edge agree on its orientation (while bits may still flow in both directions)? Edge orientations naturally occur in dynamic networks where new nodes establish connections to old nodes. Does this allow one to provide faster coloring algorithms?
Hello all, This is the presentation of Graph Colouring in Graph theory and application. Use this presentation as a reference if you have any doubt you can comment here.
SATISFIABILITY METHODS FOR COLOURING GRAPHScscpconf
The graph colouring problem can be solved using methods based on Satisfiability (SAT). An instance of the problem is defined by a Boolean expression written using Boolean variables and the logical connectives AND, OR and NOT. It has to be determined whether there is an assignment of TRUE and FALSE values to the variables that makes the entire expression true.A SAT problem is syntactically and semantically quite simple. Many Constraint Satisfaction Problems (CSPs)in AI and OR can be formulated in SAT. These make use of two kinds of
searchalgorithms: Deterministic and Randomized.It has been found that deterministic methods when run on hard CSP instances are frequently very slow in execution.A deterministic method always outputs a solution in the end, but it can take an enormous amount of time to do so.This has led to the development of randomized search algorithms like GSAT, which are typically based on local (i.e., neighbourhood) search. Such methodshave been applied very successfully to find good solutions to hard decision problems
C++ Programming! Make sure to divide the program into 3 files the head.docxBrianGHiNewmanv
Brief Exercise 5-2 (Algo) Record credit sale and cash collection with a sales discount ( LO5-1, 5-2) On February 3, a company provides services on account for $30 , 000 , terms 340 , n 30 . On February 9 , the company receives payment from the customer for those services on February 3. Record the service on account on February 3 and the collection of cash on February 9 . If no entry is required for a porticular transactionlevent, select "No Journal Entry Required" in the first occount field.) Journal entry worksheet 2 Record the services on account for $30 , 000 , terms 3/10 , n /30 . Wite: Enter debits before uredin.
.
Human Rights are those basic standards without which people cannot Human rights could be generally defined as those rights which are inherent in outs which we cannot live as human beings.
Computer instructions are normally stored in consecutive memory locations and are executed sequentially one at a time.
The control reads an instruction from a specific address in memory and executes it.
It then continues by reading the next instruction in sequence and executes it, and so on.
A computer instruction is a binary code that specifies a sequence of micro operations for the computer.
Instruction codes together with data are stored in memory.
The computer reads each instruction from memory and places it in a control register.
The control unit then interprets the binary code of the instruction and proceeds to execute it by issuing a sequence of micro operations.
Chromatic Number of a Graph (Graph Colouring)Adwait Hegde
A graph coloring is an assignment of labels, called colors, to the vertices of a graph such that no two adjacent vertices share the same color. The chromatic number χ(G) of a graph G is the minimal number of colors for which such an assignment is possible.
Distributed coloring with O(sqrt. log n) bitsSubhajit Sahu
Distributed Coloring with O˜(√log n) Bits
K Kothapalli, M Onus, C Scheideler, C Schindelhauer
Proc. of IEEE International Parallel and Distributed Processing Symposium …
We consider the well-known vertex coloring problem: given a graph G, find a coloring of its vertices so that no two neighbors in G have the same color. It is trivial to see that every graph of maximum degree∆ can be colored with∆+ 1 colors, and distributed algorithms that find a (∆+ 1)-coloring in a logarithmic number of communication rounds, with high probability, are known since more than a decade. This is in general the best possible if only a constant number of bits can be sent along every edge in each round. In fact, we show that for the n-node cycle the bit complexity of the coloring problem is
Ω (log n). More precisely, if only one bit can be sent along each edge in a round, then every distributed coloring algorithm (ie, algorithms in which every node has the same initial state and initially only knows its own edges) needs at least Ω (log n) rounds, with high probability, to color the n–node cycle, for any finite number of colors. But what if the edges have orientations, ie, the endpoints of an edge agree on its orientation (while bits may still flow in both directions)? Edge orientations naturally occur in dynamic networks where new nodes establish connections to old nodes. Does this allow one to provide faster coloring algorithms?
Hello all, This is the presentation of Graph Colouring in Graph theory and application. Use this presentation as a reference if you have any doubt you can comment here.
SATISFIABILITY METHODS FOR COLOURING GRAPHScscpconf
The graph colouring problem can be solved using methods based on Satisfiability (SAT). An instance of the problem is defined by a Boolean expression written using Boolean variables and the logical connectives AND, OR and NOT. It has to be determined whether there is an assignment of TRUE and FALSE values to the variables that makes the entire expression true.A SAT problem is syntactically and semantically quite simple. Many Constraint Satisfaction Problems (CSPs)in AI and OR can be formulated in SAT. These make use of two kinds of
searchalgorithms: Deterministic and Randomized.It has been found that deterministic methods when run on hard CSP instances are frequently very slow in execution.A deterministic method always outputs a solution in the end, but it can take an enormous amount of time to do so.This has led to the development of randomized search algorithms like GSAT, which are typically based on local (i.e., neighbourhood) search. Such methodshave been applied very successfully to find good solutions to hard decision problems
C++ Programming! Make sure to divide the program into 3 files the head.docxBrianGHiNewmanv
Brief Exercise 5-2 (Algo) Record credit sale and cash collection with a sales discount ( LO5-1, 5-2) On February 3, a company provides services on account for $30 , 000 , terms 340 , n 30 . On February 9 , the company receives payment from the customer for those services on February 3. Record the service on account on February 3 and the collection of cash on February 9 . If no entry is required for a porticular transactionlevent, select "No Journal Entry Required" in the first occount field.) Journal entry worksheet 2 Record the services on account for $30 , 000 , terms 3/10 , n /30 . Wite: Enter debits before uredin.
.
Human Rights are those basic standards without which people cannot Human rights could be generally defined as those rights which are inherent in outs which we cannot live as human beings.
Computer instructions are normally stored in consecutive memory locations and are executed sequentially one at a time.
The control reads an instruction from a specific address in memory and executes it.
It then continues by reading the next instruction in sequence and executes it, and so on.
A computer instruction is a binary code that specifies a sequence of micro operations for the computer.
Instruction codes together with data are stored in memory.
The computer reads each instruction from memory and places it in a control register.
The control unit then interprets the binary code of the instruction and proceeds to execute it by issuing a sequence of micro operations.
The inventors of java wanted to design a language which could offer solutions to some of the problems encountered in modern programming
The primary objective of Java programming language creation was to make it portable, simple and secure programming language.
The features of Java are also known as Java buzzwords.
JVM is a part of java programming language.
JVM is the engine that drives the java code.
Mostly in other programming languages, complier produce code for a particular system ut Java cier produce Bytecode for a
JVM is a software, staying on top of Operating System, such as UNIX, Windows NT.
It create the environment that java language lives
Constructor are almost similar to methods except for two things
Name is same as the class name
It has no return type
Constructor in java is used to create the instance of the class
The ability to conceal the contents of sensitive messages and to verify the contents of messages and the identities of their senders have the potential to be useful in all areas of business
Deliberate software attacks occur when an individual or group designs and deploys software to attack a system.
This attack an consist of specially crafted software that attackers trick users into installing on their systems.
This software is used to overwhelm the processing capabilities of online system or to gain access to protected systems by hidden .
Forces of Nature, sometimes called acts of God, can present some of the most dangerous threats because they usually occur with little warning and are beyond the control of people.
It is not possible to avoid threats from forces of nature, organizations must implement controls to limit damage and prepare contingency plans for continued operations
A honeypot is a deliberately compromised computer system that allows an attacker to exploit and investigate its vulnerability to improve the security policy.
Honeypots apply to any computing resource, such as software and networks.
Subset sum problem is to find subset of elements that are selected from a given set whose sum adds up to a given number K. We are considering the set contains non-negative values. It is assumed that the input set is unique (no duplicates are presented).
Apache storm is a frame work for distributed and fault tolerant and real time computation
Apache storm coding are written in CLOJURE language created by NATHAM MARZ and the team Back type
Apache storm is developed by the Twitter and release in 17 sep 2011
Apache storm is a open source
Storm is open source, robust, and user friendly. It could be utilized in small companies as well as large corporations.
Storm is fault tolerant, flexible, reliable, and supports any programming language.
Allows real-time stream processing.
Storm provides guaranteed data processing even if any of the connected nodes in the cluster die or messages are lost.
Hadoop YARN is a specific component of the open source Hadoop platform for big data analytics.
YARN stands for “Yet Another Resource Negotiator”. YARN was introduced to make the most out of HDFS.
Job scheduling is also handled by YARN.
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1. M-COLORING PROBLEM ON A GRAPH
Mrs.G.Chandraprabha., M.Sc.,M.Phil.,
Assistant Professor,
Department of Information Technology,
V.V.V.anniaperumal College for Women,
Virudhunagar.
2. OBJECTIVES
M-COLORING GRAPH:
Given an undirected graph and a number m, determine if the
graph can be colored with at most m colors such that no two adjacent
vertices of the graph are colored with the same color.
Starting from vertex 0, we will try to assign colors one by one
to different nodes. But before assigning, we have to check whether the
color is safe or not. A color is not safe whether adjacent vertices are
containing the same color.
Note: Here coloring of a graph means the assignment of
colors to all vertices
Following is an example of a graph that can be colored
with 3 different colors:
3.
4. PROBLEM:-
PROBLEM: Determine all ways in which the vertices in all
undirected graph can be colored, using only m colors, so that adjacent
vertices are not the same color.
Input: The adjacency matrix of a graph
G(V, E) and an integer m, which indicates the maximum number of colors
that can be used.
5. Let the maximum color m = 3.
OUTPUT: This algorithm will return which node will
be assigned with which color. If the solution is not possible,
it will return false.
For this input the assigned colors are:
Node 0 -> color 1
Node 1 -> color 2
Node 2 -> color 3
Node 3 -> color 2
6. EXAMPLE:-
INPUT: graph = {0, 1, 1, 1},
{1, 0, 1, 0},
{1, 1, 0, 1},
{1, 0, 1, 0}
OUTPUT: Solution Exists: Following are the assigned
colors: 1 2 3 2
EXPLANATION: By coloring the vertices with
following colors, adjacent vertices does not have same
colors.
INPUT: graph = {1, 1, 1, 1},
{1, 1, 1, 1},
{1, 1, 1, 1},
{1, 1, 1, 1}
OUTPUT:
Solution does not exist
7. EXPLANATION:
No solution exits .
NAIVE APPROACH: To solve the problem follow the below idea:
Generate all possible configurations of colors. Since each node can be
colored using any of the m available colors, the total number of color
configurations possible is mV. After generating a configuration of color, check
if the adjacent vertices have the same color or not. If the conditions are met,
print the combination and break the loop.
Follow the given steps to solve the problem:
Create a recursive function that takes the current index,number of vertices
and output color array.
If the current index is equal to number of vertices. Check if the output
color configuration is safe, i.e check if the adjacent vertices do not have
same color. If the conditions are met, print the configuration and break.
Assign a color to a vertex (1 to m).
8. every assigned color recursively call the
function with next index and number of
verticersive function returns true break the loop
and returns true.
ALGORITHMS:-
Void m_coloring(index i)
{
int color;
if(promising(i))
if(i==n)
cout<<vcolor[1] through vcolor[n];
else
for (color=1; color<=m; color++){
vcolor[i+1] = color;
m_coloring(i+1);
9. bool promising (index i)
{
index I;
boot switch;
switch= True;
j=1;
while (j< I && switch)
if (W[i] [j] && vcolor [i] = = vcolor[j])
switch =false;
j++;
}
return switch;
}