What is the difference between dynamic programming and backtracking?

What is the difference between dynamic programming and backtracking?

Dynamic programming is an optimization technique used to solve problems by breaking them down into simpler subproblems, storing the results to avoid redundant calculations. Backtracking, on the other hand, is a method for solving problems incrementally, trying out different possibilities and abandoning those that fail to satisfy the conditions.

Can you give me an example of a problem suited for dynamic programming?

Sure! A classic example is the Fibonacci sequence. Instead of recalculating Fibonacci numbers multiple times, dynamic programming stores the results of previous calculations to efficiently compute the next numbers.

What about backtracking? Any examples?

Yes! A common example of backtracking is the N-Queens problem, where you need to place N queens on an N×N chessboard so that no two queens threaten each other. Backtracking explores all possible placements and backtracks when it finds a conflict.

When should I use dynamic programming over backtracking?

Use dynamic programming when the problem has overlapping subproblems and optimal substructure, while backtracking is better for problems that require exploring all possibilities, like puzzles or combinatorial problems.