Backtracking is a general algorithmic technique used in computer science and mathematics to solve problems by incrementally constructing a solution and abandoning it when it is found to be not feasible. It is often used to find all possible solutions to a problem by systematically exploring all possibilities.
Backtracking algorithms start with a partially filled solution and try to find a complete solution by making one move at a time. If the move leads to a feasible solution, the algorithm continues with the next move. If the move leads to an infeasible solution, the algorithm backtracks to the previous move and tries a different approach. This process is repeated until a complete solution is found or it is proven that there is no solution.
Backtracking algorithms are used to solve problems such as the N-Queens problem, the traveling salesman problem, and the knapsack problem. They are often used when the problem size is too large to be solved using exhaustive search or when the problem has a large number of constraints that need to be considered.
Backtracking algorithms have exponential time complexity, so they are not suitable for solving problems with large inputs. However, they are often used as a prototyping tool and are later optimized using dynamic programming or other optimization techniques.