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Introduction
 It is one of the very important types of algorithms.
Definition
 Backtracking is a general algorithmic technique that is used to solve a wide range of problems, including combinatorial optimization problems, constraint satisfaction problems, and other problems that involve finding a solution among a large set of possible solutions.
Characteristics
 The backtracking algorithm can be implemented using recursion or iteration, depending on the problem and the programming language.
 In general, backtracking is a powerful technique that can solve many complex problems, but it can be inefficient for problems with a large solution space.
Working Principle
 The basic idea of backtracking is to explore all possible solutions by incrementally building up a potential solution and checking if it satisfies the constraints. If the current solution violates any constraint, the algorithm backtracks to the previous step and tries another solution.
 In other words, backtracking builds a solution incrementally, one step at a time, while simultaneously checking if the partial solution can be extended to a complete solution. If at any point it is discovered that the current partial solution cannot be extended to a complete solution, then the algorithm “backtracks” to the previous step, and tries a different path.
 There are the following general steps involved in the backtracking algorithm:

Define the problem: First, identify the constraints and the goal of the problem that we want to solve.

Choose a starting point: Start with an initial configuration of the problem, which can be an empty solution or a partial solution.

Construct a candidate solution: Generate the next possible solution based on the current state.

Check if the solution satisfies the constraints: Evaluate the candidate solution to see if it satisfies the problem’s constraints. If it does not, discard it and backtrack to the previous step.

If the solution satisfies the constraints, check if it is the goal: If the solution satisfies all the constraints and is also the goal of the problem, then accept it as the solution and stop. Otherwise, move to the next step.

Repeat the process: If the solution is not the goal, repeat the process from step 3 by generating the next candidate solution.

Backtrack: If there are no more candidate solutions to explore, backtrack to the previous step and try another candidate solution.

Stop: If all candidate solutions have been explored and none of them satisfy the constraints or the goal, then stop and report that there is no solution to the problem.

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