Greedy Approach
In greedy approach, the decision is taken on the basis of current available information
without worrying about the effect of the current decision in future.
Greedy algorithms build a solution part by part, choosing the next part in such a way, that it
gives an immediate benefit. This approach never reconsiders the choices taken previously.
This approach is mainly used to solve optimization problems. Greedy method is easy to
implement and quite efficient in most of the cases. Hence, we can say that Greedy algorithm
is an algorithmic paradigm based on heuristic that follows local optimal choice at each step
with the hope of finding global optimal solution.
In many problems, it does not produce an optimal solution though it gives an approximate
(near optimal) solution in a reasonable time.
Greedy Method finds out of many options, but you have to choose the best option."
In this method, we have to find out the best method/option out of many present ways.
In this approach/method we focus on the first stage and decide the output, don't think about
the future.
This method may or may not give the best output.
Greedy Algorithm solves problems by making the best choice that seems best at the particular
moment. Many optimization problems can be determined using a greedy algorithm. Some
issues have no efficient solution, but a greedy algorithm may provide a solution that is close
to optimal. A greedy algorithm works if a problem exhibits the following two properties:
1. Greedy Choice Property:+A globally optimal solution can be reached at by creating
a locally optimal solution. In other words, an optimal solution can be obtained by
creating "greedy" choices.
2. Optimal substructure:+Optimal solutions contain optimal subsolutions. In other
words, answers to subproblems of an optimal solution are optimal.
Example:
1. machine scheduling
