Which type of algorithm makes the best choice at each step to solve optimization problems?

The greedy algorithm is recognized for its strategy of making the best local choice at each stage with the hope of finding a global optimum. This approach works well for certain optimization problems where local optimal choices lead to a globally optimal solution.

By focusing on immediate benefits without regard to the larger problem, the greedy algorithm can efficiently process problems such as finding the minimum spanning tree or performing coin change for specific denominations. It is essential to note that greedy algorithms do not always yield the best solution for every optimization problem but are highly effective in scenarios where conditions align with their approach.

In contrast, other algorithm types serve different purposes. Search algorithms are more focused on exploring data structures to locate specific values, while recursive algorithms break problems into smaller subproblems but do not inherently optimize decisions at each step. Dynamic programming instead involves storing results of subproblems to avoid redundant calculations, which is generally more effective when the problem exhibits overlapping subproblems and optimal substructure, rather than taking a straightforward greedy approach.