Approximation algorithms are techniques used to find near-optimal solutions for complex problems, especially when finding the exact solution is computationally expensive or infeasible. These algorithms provide solutions that are close to the best possible answer, often within a guaranteed ratio of the optimal solution. They are particularly useful in fields like computer science and operations research. These algorithms are commonly applied to NP-hard problems, such as the Traveling Salesman Problem or Knapsack Problem, where exact solutions may require excessive time. By using approximation algorithms, practitioners can achieve satisfactory results more efficiently, making them valuable tools in both theoretical and practical applications.