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DAA
PRESENTATION
BY
YASH BRID 2019130008
ABHISHEK CHOPRA 2019130009
ADWAIT HEGDE 2019130019
DECISION PROBLEM
CHROMATIC NUMBER
CHROMATIC NUMBER IS THE MINIMUM NUMBER
OF COLORS REQUIRED TO COLOR ANY GRAPH
SUCH THAT NO TWO ADJACENT VERTICES OF IT
ARE ASSIGNED THE SAME COLOR.
PROBLEM STATEMENT
What are NP Problems?
NP is set of decision problems whose solutions are hard to find but easy to verify and
can be solved by a Non-deterministic Turing Machine in Polynomial time.
A Non-deterministic Turing Machine is a theoretical model of computation whose
governing rules specify more than one possible action when in some given situations i.e.
the set of rules of a Non-deterministic Turing Machine may prescribe more than one
action to be performed for any given situation.
A problem X is NP-Complete if there is an NP problem Y, such that Y is reducible to X in
polynomial time. NP-Complete problems can be solved by a Non-deterministic Turing
Machine in polynomial time.
What is a Non-deterministic Turing Machine?
What is a NP-Complete problem?
PROBLEM DEFINITION
What is a NP-Hard Problem?
Satisfiability with at most 3 literals per clause.
Reduction to NP-Hard problem is possible
It is a reduction from 3-SAT.
The complexity class of decision problems that are intrinsically harder than those that
can be solved by a nondeterministic Turing machine in polynomial time. When a decision
version of a combinatorial optimization problem is proved to belong to the class of NP-
complete problems, then the optimization version is NP-hard.
Therefore, it is a NP-Hard Problem.
Why is CNDP a NP-Hard Problem?
Graph Colouring: Graph Coloring is the process of assigning colors to the vertices of a
graph such that no two adjacent vertices of it are assigned the same
color.
THEORY
Chromatic number: Chromatic Number is the minimum number of colors required
to color any graph such that no two adjacent vertices of it are
assigned the same color.
Example:
ALGORITHM
Create a recursive function that takes 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.
If the conditions are met, print the configuration and break.
Assign color to a vertex (1 to m).
For every assigned color recursively call the function with next index and number of
vertices.
If any recursive function returns true break the loop and return true.
There are quite a few ways in which we can solve the Chromatic Number Decision
Problem (CNDP), backtracking and naive are two of them.
Naive:
1.
2.
3.
4.
5.
6.
Create a recursive function that takes the graph, current index, number of vertices
and output color array.
If the current index is equal to number of vertices. Print the color configuration in
output array.
Assign color to a vertex (1 to m).
For every assigned color, check if the configuration is safe, recursively call the
function with next index and number of vertices.
If any recursive function returns true break the loop and return true
If no recusive function returns true then return false.
Backtracking:
1.
2.
3.
4.
5.
6.
SOME EXAMPLES
APPLICATION
Map Colouring
Sudoku
Register Allocation
Time Table Scheduling
Mobile Radio Frequency
MAP COLOURING
Four colors are sufficient
to color any map
We place a vertex in each
region
4-coloring thereom
Fill in the blank
cells so that each
row, column and
box has the
characters 1 to 9
exactly once
SUDOKU
How do we schedule the
exam so that no two exams
with a common student
are scheduled at same
time?
How many minimum time
slots are needed to
schedule all exams?
TIMETABLE MAKING
CNDP is an np-hard problem
NP-hard are still under research,
so until these are found one will
have to use the algorithm with
exponential time complexity
(naive/backtracking)
CONCLUSION
REFERENCES
Proof: http://cs.bme.hu/thalg/3sat-to-3col.pdf
https://www.kodnest.com/free-online-courses/algorithm-
2/lessons/graph-coloring/
https://www.youtube.com/watch?v=e2cF8a5aAhE
QUESTIONS
Chromatic Number of a Graph (Graph Colouring)

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Chromatic Number of a Graph (Graph Colouring)

  • 1. DAA PRESENTATION BY YASH BRID 2019130008 ABHISHEK CHOPRA 2019130009 ADWAIT HEGDE 2019130019
  • 2. DECISION PROBLEM CHROMATIC NUMBER CHROMATIC NUMBER IS THE MINIMUM NUMBER OF COLORS REQUIRED TO COLOR ANY GRAPH SUCH THAT NO TWO ADJACENT VERTICES OF IT ARE ASSIGNED THE SAME COLOR. PROBLEM STATEMENT
  • 3. What are NP Problems? NP is set of decision problems whose solutions are hard to find but easy to verify and can be solved by a Non-deterministic Turing Machine in Polynomial time. A Non-deterministic Turing Machine is a theoretical model of computation whose governing rules specify more than one possible action when in some given situations i.e. the set of rules of a Non-deterministic Turing Machine may prescribe more than one action to be performed for any given situation. A problem X is NP-Complete if there is an NP problem Y, such that Y is reducible to X in polynomial time. NP-Complete problems can be solved by a Non-deterministic Turing Machine in polynomial time. What is a Non-deterministic Turing Machine? What is a NP-Complete problem? PROBLEM DEFINITION
  • 4. What is a NP-Hard Problem? Satisfiability with at most 3 literals per clause. Reduction to NP-Hard problem is possible It is a reduction from 3-SAT. The complexity class of decision problems that are intrinsically harder than those that can be solved by a nondeterministic Turing machine in polynomial time. When a decision version of a combinatorial optimization problem is proved to belong to the class of NP- complete problems, then the optimization version is NP-hard. Therefore, it is a NP-Hard Problem. Why is CNDP a NP-Hard Problem?
  • 5. Graph Colouring: Graph Coloring is the process of assigning colors to the vertices of a graph such that no two adjacent vertices of it are assigned the same color. THEORY Chromatic number: Chromatic Number is the minimum number of colors required to color any graph such that no two adjacent vertices of it are assigned the same color. Example:
  • 6. ALGORITHM Create a recursive function that takes 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. If the conditions are met, print the configuration and break. Assign color to a vertex (1 to m). For every assigned color recursively call the function with next index and number of vertices. If any recursive function returns true break the loop and return true. There are quite a few ways in which we can solve the Chromatic Number Decision Problem (CNDP), backtracking and naive are two of them. Naive: 1. 2. 3. 4. 5. 6.
  • 7. Create a recursive function that takes the graph, current index, number of vertices and output color array. If the current index is equal to number of vertices. Print the color configuration in output array. Assign color to a vertex (1 to m). For every assigned color, check if the configuration is safe, recursively call the function with next index and number of vertices. If any recursive function returns true break the loop and return true If no recusive function returns true then return false. Backtracking: 1. 2. 3. 4. 5. 6.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 14. APPLICATION Map Colouring Sudoku Register Allocation Time Table Scheduling Mobile Radio Frequency
  • 15. MAP COLOURING Four colors are sufficient to color any map We place a vertex in each region 4-coloring thereom
  • 16. Fill in the blank cells so that each row, column and box has the characters 1 to 9 exactly once SUDOKU
  • 17. How do we schedule the exam so that no two exams with a common student are scheduled at same time? How many minimum time slots are needed to schedule all exams? TIMETABLE MAKING
  • 18. CNDP is an np-hard problem NP-hard are still under research, so until these are found one will have to use the algorithm with exponential time complexity (naive/backtracking) CONCLUSION