BackTracking Algorithm: Technique and Examples

Backtracking:
Technique & Examples
By,
Fahim Ferdous
Back
Track
Yes Solution
No Solution

Backtracking
History
• ‘Backtrack’ the Word was first introduced by Dr. D.H.
Lehmer in 1950s.
• R.J Walker Was the First man who gave algorithmic
description in 1960.
• Later developed by S. Golamb and L. Baumert.

Backtracking
• What is Backtracking?
----- Backtracking is nothing but the modified process of the brute
force approach. where the technique systematically searches for a
solution to a problem among all available options. It does so by
assuming that the solutions are represented by vectors (v1, ..., in) of
values and by traversing through the domains of the vectors until
the solutions is found.

Backtracking
• The Algorithmic Approach
– Backtracking systematically try and search possibilities to find the
solution. Also it is an important process for solving constraint
satisfaction problem like crossword, Sudoku and many other puzzles. It
can be more continent technique for parsing other combinatorial
optimization problem.
– Basically the process is used when the problem has a number of option
and just one solution have to be selected. After having a new option set
means recursion, the procedure is repeated over and over until final
stage.

Backtracking
Algorithm Backtrack (v1,Vi)
If (V1,……., Vi) is a Solution Then
Return (V1,…, Vi)
For each v DO
If (V1,…….,Vi) is acceptable vector THEN
Sol = try (V1,…,Vi, V)
If sol != () Then
RETURN sol
End
End
Return ( )

Backtracking
• Advantages
– Comparison with the Dynamic Programming, Backtracking Approach is more effective in some cases.
– Backtracking Algorithm is the best option for solving tactical problem.
– Also Backtracking is effective for constraint satisfaction problem.
– In greedy Algorithm, getting the Global Optimal Solution is a long procedure and depends on user
statements but in Backtracking It Can Easily getable.
– Backtracking technique is simple to implement and easy to code.
– Different states are stored into stack so that the data or Info can be usable anytime.
– The accuracy is granted.

Backtracking
• Disadvantages
– Backtracking Approach is not efficient for solving strategic Problem.
– The overall runtime of Backtracking Algorithm is normally slow
– To solve Large Problem Sometime it needs to take the help of other
techniques like Branch and bound.
– Need Large amount of memory space for storing different state function
in the stack for big problem.
– Thrashing is one of the main problem of Backtracking.
– The Basic Approach Detects the conflicts too late.

Backtracking
• Application of Backtracking
• Optimization and tactical problems
• Constraints Satisfaction Problem
• Electrical Engineering
• Robotics
• Artificial Intelligence
• Genetic and bioinformatics Algorithm
• Materials Engineering
• Network Communication
• Solving puzzles and path

Backtracking
• Some Problem Solved with Backtracking Technique
– N- Queens Problem
– Sum of Subset
– Sudoku Puzzle
– Maze Generation
– Hamiltonian Cycle

N-Queens Problem
• History:
First Introduced in 1848 which was known as 8- queens Puzzle. Surprisingly,
The First Solution was created in 1950 by Franz Nauck. Nauck made an 8X8
Chessboard to find the first Feasible Solution.

N-Queens Problem
• Problem Description
In a NxN square board N – number of queens need to be
placed considering three Condition ---
• No two Queens can be placed in same row.
• No two Queens Can be places in same Column
• No two queens Can be placed in same Diagonal.

N-Queens Problem
• Backtracking approach for solution
- - The Algorithm will check each position [i , j] for each queens . If any Suitable places found , It will
place a queen on that position. If not Algorithm will try same approach for next position.
Algorithm Nqueen(K,n){
For i= 1 to n {
If Place(K,i){
X[k] = I;
If(k = n) then
Write x[1:n];
Else
Nqueen(k+1, n) ;
}
}
}
Place(k,i){
For j=1 to k-1{
If((x[j] = i) or abs(x[j] – 1 = abs(j-k)))
Return false;
}
Return true;
}

Maze Generation
Problem Description
A N X N board is made in a way So that 1 or 0 can be placed in
each box where 1 is for valid path for moving towards exit and 0 is
the Closed path. Now, by using Backtracking Algorithm The path to
the exit need to be find.

Maze Generation
• -- Algorithm will try to follow the box which has the value 1 and
generate a path. Again, it will check for 1 at the next box. If not
found it will go through next box. If entire row is complete, then it will
move to the next row and look for 1.

Maze Generation
• Solution Using Backtracking Technique
Algorithm Maze_Gen(graph){
If(Maze_operator(graph, x,y, canvas)
== false)
Write (“No result”);
Return false;
Print_Result(canvas);
Return true;
}
Algorithm Maze_operator(graph, x,y, canvas){
If(x ==N-1 && y == N-1){
Canvas[x][y] = 1;
Return true;
}
If(IsSafe(graph, x,y) == true){
Canvas[x][y] = 1;
If(Maze_operator(graph, x+1,y, canvas))
Return true;
If(Maze_operator(graph, x,y+1, canvas))
Return true;
Canvas [x][y] = 0;
Return true;
}
Return false;
}

Hamiltonian Cycle
Hamiltonian Cycle is a graph theory problem where the graph cycle
through a graph can visit each node only once. The puzzle was first
devised by Sir William Rowan Hamilton and the Problem is named after
Him.
Condition: The Cycle Started with a Starting Node, and visit all the Nodes in the Graph
(Not necessary to visit in sequential Order and not creating edge that is not given) And
Stop at The Starting Point/Node.

Hamiltonian Cycle
• The Backtracking Approach
The Algorithm First Check the Starting Node, if there is any edge to the next node. If
yes, then the Algorithm will check that node for the edge to the next Node. It will Also
Check If any Node is visited twice by the previous Node. If there is any then the
Algorithm Will Ignore One and Choose the Optimal One for the Solution.
 The Important thing is the tour must finish at the starting point.

Hamiltonian Cycle
Algorithm Hamilton_Cycle(k){
Repeat{
NextVal(k);
If(x[k]==0) then
Return;
If (k ==n) then
Write (x[1:n]);
Else
Hamilton_Cycle(k+1);
}
Until (false);
}
AlgortihmNextVal(k){
Repeat{
X[k] = (x[k]+1) mod (n+1);
If(x[k]=0) then return;
If (G[x[k-1],x[k]] != 0 ) then{
For j = 1 to k-1 do
If(x[j]=x[k]) then
Break;
If(j = k) then
If ((k<n or k=n) && G[x[n],x[1]] != 0)
Then return;
}
}
Until (false);
}

Sudoku Puzzle
• The Word ‘Sudoku’ is a Japanese Word. But The Sudoku Puzzle Was First
Invented in Switzerland
• In a N X N Square Board, The Numbers 1 to N to be Placed in a way that:
– All the Number from 1 to N can be Placed in each row.
– All the Number from 1 to N can be Placed in each Column.
– All The Number from 1 to N Can be Placed in Sub Square boxes of the given box.

Sudoku Puzzle
• Backtracking Approach
The Algorithm Will Check Each Box’s value if the value is in the same
row, or same column or same sub-square box. If not,then it placed the value
in the box. And go to the next box for the next value, and check the Above
Condition. If there is any duplicate value, then The Algorithm check for the
next value.

Sudoku Puzzle
Algorithm Sudoku_Solver(canvas, row,
col){
If(anyEmptyLocation(canvas, row, col))
Return true;
For I = 1 to n do, {
If (find_location(canvas, row,col, i))
{
Canvas[row][col] = i;
If (Sudoku_Solver(canvas))
Return true;
Canvas [row][col] = -1;
}
}
Return false;
}

Sum of Subset
• The Sum of Subset Problem is, there will be a set of distinct positive
Numbers X and a given Number N. Now, we have to find all the
combination of numbers from X that sum up to N. Make a Set of
those Number.
W = {4,5,6,3}
M = 13

Sum of Subset
• Backtracking Approach
First, organize the numbers in non decreasing order. Then generating a tree, we
can find the solution by adding the weight of the nodes. Note that, here more than
necessary nodes can be generated. The algorithm will search for the weighted amount
of the nodes. If it goes beyond given Number N, then it stops generating nodes and
move to other parent nodes for finding solution.
Algorithm SumOfSubset(s,k,y){
X[k] = 1;
If(s+w[k] = m)
Write (x[1:n]);
Else if((s+w[k] + w[k+1]) <= m)
SumOfSubset(s+w[k], k+1, y-w[k]);
If ((s+ y-w[k]>=m) &&(s =w[k+1] <=m)) {
X[k] =0;
SumOfSubset(s,k+1,y-w[k]);
}
}

Sum of Subset
Here M = 53
W = {8,9,14,15,16,22}

References
• Chapter 6, Backtracking, Design and Analysis of Algorithms – V.V. Muniswami
• Chapter – 4, Non- Chronological Backtracking, Handbook on Constraint Programming – F.
Rossi, Van Beek
• web.cse.ohio-state.edu/~gurari/course/cis680/cis680Ch19.html
• www.classle.net/faq/comparisons-divide-and-conquergreedy-methoddynamic-
programmingbacktracking-and-branch-and-bound-
• Backtracking algorithms for constraint satisfaction problems; a survey
R. Dechter, D. Frost, in Constraints, International Journal, 1998.
• Bessiere C., Maestre A., Meseguer P., Distributed dynamic backtracking, in M.C. Silaghi, editor,
Proceedings of the IJCAI’01 workshop on Distributed Constraint Reasoning, Seattle WA,
2001, pp. 9–16.
• A First Look at Graph Theory – Jhon Clark, Allan Holton
• Principles and Practice of Constraint Programming- CP 2001: 7th International Conference,
CP 2001, Paphos, Cyprus, November 26 - December 1, 2001

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