Editing Backtracking

'''Backtracking''' is an algorithmic technique for solving problems that incrementally builds candidates to the solution(s) and abandons a candidate ("backtracks") as soon as it determines that the candidate cannot be extended to a valid solution.

The concept of backtracking is based on the idea of exploring all possible solutions until a valid solution is found. Backtracking is commonly used in problems involving decision trees or state space trees, such as searching through graphs, constraint satisfaction problems, and combinatorial optimization problems.


== Understand the Problem ==
Before implementing backtracking, it's essential to have a deep understanding of the problem statement, input constraints, and desired output. Identify the decisions to be made, the constraints to satisfy, and the goal to achieve.

== Identify the Decision Space ==
Determine the decision space of the problem, which is the set of choices that can be made at each step of the solution-building process. The decision space is often represented as a tree or a graph.

== Implement a Recursive Solution ==
Backtracking is typically implemented using recursion. The recursive function should include the following components:

- '''Base case:''' Identify the conditions under which a valid solution has been found or the search must be terminated. Return an appropriate value or result.
- '''Recursive case:''' Iterate over the decision space, making a choice, and then calling the recursive function with updated parameters. If a valid solution is found, return it or store it as needed.
- '''Backtrack:''' If the current choice does not lead to a valid solution, undo the choice (i.e., restore the previous state) and continue with the next option in the decision space.

== Pruning ==
In many problems, it's possible to identify situations where further exploration of the decision space is unnecessary or unproductive. Pruning techniques can be employed to eliminate such branches, improving the algorithm's efficiency.

== Analyze Time and Space Complexity ==
Analyze the time and space complexity of the backtracking algorithm. This analysis will help in understanding the performance of the algorithm and the feasibility of using it to solve large-scale problems.

== Examples of Backtracking Problems ==

* N-Queens problem
* Sudoku solver
* Traveling Salesman Problem (TSP) using Branch and Bound
* Hamiltonian Cycle
* Graph coloring problem
* Subset sum problem

[[category:Backtracking]]