Dynamic Programming vs Greedy Programming

Dynamic Programming

Dynamic Programming involves breaking down a complex problem into simpler subproblems, solving each of these subproblems just once, and storing their solutions to avoid redundant computations.

Example Problem for Dynamic Programming:

One example of a problem that can be solved using dynamic programming is the Fibonacci sequence. By storing the solutions to subproblems (like calculating Fibonacci numbers for smaller values), we can efficiently compute the Fibonacci sequence for larger values.

Greedy Programming

Greedy Programming involves making a sequence of choices that are locally optimal at each step with the hope of finding a global optimum solution.

Example Problem for Greedy Programming:

An example of a problem that can be solved using greedy programming is the coin change problem. By selecting the largest denomination coins first, the greedy approach can efficiently find the minimum number of coins needed to make a given amount of change.