Backtracking Algorithm

Backtracking is a technique used in computer programming to find all possible solutions to a problem by systematically searching through a large set of potential solutions. It is particularly useful for problems where the solution needs to satisfy a set of constraints and the constraints become increasingly complex as the solution is built.

Backtracking algorithms are commonly used in a variety of fields, such as computer science, engineering, mathematics, and artificial intelligence. Some examples of problems that can be solved using backtracking algorithms include:

• The n-queens problem, which involves placing n queens on an n x n chessboard such that no two queens can attack each other
• The Sudoku puzzle, which involves filling a 9 x 9 grid with numbers such that each row, column, and 3 x 3 subgrid contains all the digits from 1 to 9
• The traveling salesman problem, which involves finding the shortest possible route that visits every city in a given set
• The graph coloring problem, which involves coloring the nodes of a graph such that no two adjacent nodes have the same color
• The knapsack problem, which involves packing a knapsack with items of different weights and values such that the knapsack has maximum value without exceeding its weight capacity.

By using backtracking algorithms, programmers can efficiently find solutions to complex problems that would be otherwise difficult or impossible to solve.

Let’s see how can we use Backtracking to solve n-queen problem:

def solve_n_queens(n):
    """
    Returns a list of all possible solutions to the n-queens problem.
    """
    # Initialize empty solution list
    solutions = []

    def backtrack(queens, xy_sum, xy_diff):
        """
        Recursive function that generates all possible solutions.
        """
        row = len(queens)
        if row == n:
            # Base case: a valid solution has been found
            solutions.append(queens)
            return

        for col in range(n):
            # Check whether the current position is valid
            if col not in queens and row + col not in xy_sum and row - col not in xy_diff:
                # If it is, add the queen and continue searching
                backtrack(queens + [col], xy_sum + [row + col], xy_diff + [row - col])

    backtrack([], [], [])
    return solutions

This function takes an integer n as input and returns a list of all possible solutions to the n-queens problem. The backtrack() function is a recursive function that generates all possible solutions. At each level of recursion, it tries to place a queen in each column of the current row, and then recursively calls itself to try to place queens in the remaining rows. If a valid solution is found, it is added to the list of solutions.

This implementation uses three lists to keep track of the columns, sums, and differences of the diagonals that have already been used by the queens. This allows the algorithm to quickly determine whether a given position is valid or not.

How does Backtracking Algorithm works?

Backtracking algorithms work by maintaining a partial solution to the problem, which is progressively built upon until a complete solution is found. At each step, the algorithm checks whether the current partial solution satisfies the constraints of the problem. If it does, the algorithm continues building the solution. If it does not, the algorithm backtracks to the previous step and tries a different option.

Imagine you are trying to solve a puzzle, but you’re not sure how to proceed. You might try a certain approach, but soon realize that it doesn’t work. So, you backtrack to your last move and try a different approach. This is essentially how backtracking algorithms work.

Backtracking algorithms start by taking a partial solution to a problem and incrementally building upon it until a complete solution is found. At each step, the algorithm checks whether the current partial solution satisfies the constraints of the problem. If it does, the algorithm continues building the solution. If it does not, the algorithm backtracks to the previous step and tries a different option.

Here’s an example to illustrate how backtracking works:

Imagine you are playing a game of Sudoku and you need to fill in the numbers for an empty cell. You start by guessing a number and checking whether it violates any of the rules of the game. If it doesn’t, you move on to the next cell and repeat the process. If you get to a point where there are no valid numbers for a cell, you backtrack to the previous cell and try a different number.

Let’s say you’ve filled in some numbers and you’re stuck on a certain cell:

		3
1		4

You guess 2 for the middle cell and check whether it’s a valid choice. You check the row and column and see that 2 doesn’t appear, so it’s a valid choice. You move on to the next cell and guess 3 for the bottom left cell. However, you soon realize that you cannot fill in the bottom right cell because all the valid numbers have already been used in the row and column. So, you backtrack to the previous cell and try a different number. You try 2 for the bottom left cell and find that it’s a valid choice. You can then continue filling in the remaining cells using this same approach.

When is backtracking used?

Backtracking algorithms are leveraged for solving problems that necessitate meeting multiple constraints, which become more intricate as we develop the solution. They are particularly advantageous when there is no straightforward approach to determine the best decision, and we need to assess every potential solution until we arrive at the optimal one.

Here are some examples of problems where backtracking algorithms are commonly used:

Constraint Satisfaction Problems (CSPs)

CSPs are a class of problems that involve finding a solution that satisfies a set of constraints. One of the most famous CSPs is the n-queens problem, where we need to place n queens on an n x n chessboard such that no two queens can attack each other. Another example is the Sudoku puzzle, where we need to fill in a 9 x 9 grid with numbers such that each row, column, and 3 x 3 subgrid contains all the digits from 1 to 9.

To solve CSPs using backtracking, we start by making an initial guess and checking whether it satisfies the constraints of the problem. If it does, we continue building the solution. If it doesn’t, we backtrack to the previous step and try a different guess.

Permutation and Combination Problems

Permutation and combination problems involve finding all possible permutations or combinations of a set of elements. For example, we might need to generate all permutations of a string, or find all possible combinations of a set of integers that sum up to a given target value.

To solve permutation and combination problems using backtracking, we start by making an initial choice and building the solution incrementally. At each step, we check whether the current solution satisfies the constraints of the problem. If it does, we continue building the solution. If it doesn’t, we backtrack to the previous step and try a different choice.

Graph Problems

Backtracking algorithms can be used to solve many graph problems, such as finding all possible paths between two nodes in a graph, finding a Hamiltonian path or cycle in a graph, or finding the minimum cut in a graph. These problems are often solved using depth-first search (DFS) with backtracking.

To solve graph problems using backtracking, we start by making an initial choice and building the solution incrementally. At each step, we traverse the graph to the next node and check whether the current solution satisfies the constraints of the problem. If it does, we continue building the solution. If it doesn’t, we backtrack to the previous node and try a different path.

Tree Traversal

Backtracking algorithms are often used to traverse trees, such as finding all possible paths from the root to a leaf node, or finding the lowest common ancestor of two nodes. These problems are often solved using depth-first search (DFS) with backtracking.

To solve tree traversal problems using backtracking, we start at the root node and make an initial choice. We then traverse the tree to the next node and check whether the current solution satisfies the constraints of the problem. If it does, we continue building the solution. If it doesn’t, we backtrack to the previous node and try a different path.

Game Theory

Backtracking algorithms can be used in game theory to find optimal strategies for two-player games such as chess or tic-tac-toe. These problems are often solved using minimax search with backtracking.

To solve game theory problems using backtracking, we start by making an initial move and building the solution incrementally. We then simulate the opponent’s moves and check whether the current solution satisfies the constraints of the problem. If it does, we continue building the solution. If it doesn’t, we backtrack to the previous move and try a different strategy.

Advantages and Disadvantages of Backtracking Algorithm

Backtracking algorithms have several advantages and disadvantages:

Advantages:

1. Backtracking algorithms can be used to solve a wide range of problems, including problems with complex constraints or where other algorithms fail.
2. Backtracking algorithms are relatively easy to implement, and they can often find the optimal solution to a problem.
3. Backtracking algorithms can be used to find multiple solutions to a problem, rather than just one.

Disadvantages:

1. Backtracking algorithms can be computationally expensive, especially for large problems. This is because they need to explore every possible solution to find the optimal one.
2. Backtracking algorithms can require a lot of memory, as they need to maintain the state of the search space.
3. Backtracking algorithms can be inefficient if the search space is very large or if there are many invalid solutions to the problem. In these cases, other algorithms, such as dynamic programming or branch-and-bound, may be more effective.