# Greedy Method

The mathematical process that searches for a simple but easy-to-implement solution up to complex, and multi-step problems by choosing the next step that will offer the more obvious benefit is known as the greedy algorithm. The reason it is referred to as “greedy” is because in smaller instances the optimal solution provides an immediate output, but the algorithm itself doesn’t recognize the larger problem overall. After a decision has been decided, reconsideration is never given.

Recursive construction of a group of objects derived from the most minor constituent parts is how the greedy algorithms work. The approach to a solving a problem where the solution is dependent upon solutions of the same problems smaller instance is known as Recursion. The primary advantage here is that the solution to the smaller instance can be easier to understand and more straightforward using the greedy algorithm. Shorter term solutions may create a worse but longer-termed outcome and that is the disadvantage.

Within mobile networking, the greedy algorithm is used in ad hoc so that route packets with the shortest delay and fewest number of hops are efficiently sent. This is also useful in machine learning, programming, artificial intelligence, and business intelligence.When it comes to optimization of a problem the greedy algorithm is one of the simplest approaches. This is where it is determined what the global optimum of a particular function through a sequence of steps where the stages allow for a choice among a class of probable decisions.

The optimal decision using the greedy method is the choice of the information on hand without consideration of the effect that a particular decision will have on the future. It is easy to invent a greedy algorithm as well as the ease of implementation and it is more efficient the majority of the time. There are a variety of problems that can’t readily be solved through the greedy approach. A common example is the problem of making change of a particular amount of monetary coins. A variety of values can be used to come to a particular end. Therefore with the greedy algorithm the highest amount of coins in any given value will start from the highest 100 cents.

In this type of setting it is easy to see that the greedy algorithm is the most optimal for proof that it suffices to use the principle of induction that will operate well because every part of the procedure has come to an end or there is a minimum of one coin where the actual value can be used. This means that a certain optimal substructure of a problem makes it effective in the greedy algorithm.