Greedy Introduction


Greedy algorithms are simple and straightforward. They are shortsighted in their approach in the sense that they take decisions on the basis of information at hand without worrying about the effect these decisions may have in the future. They are easy to invent, easy to implement and most of the time quite efficient. Many problems cannot be solved correctly by greedy approach. Greedy algorithms are used to solve optimization problems

Greedy Approach

Greedy Algorithm works by making the decision that seems most promising at any moment; it never reconsiders this decision, whatever situation may arise later.

As an example consider the problem of "Making Change".

Coins available are:


Problem    Make a change of a given amount using the smallest possible number of coins.


Informal Algorithm


Formal Algorithm

Make change for n units using the least possible number of coins.

{100, 25, 10, 5, 1}     // constant.
{};                         // set that will hold the solution set.
0 sum of item in solution set
        WHILE sum not = n
            x = largest item in set C such that sum + x ≤ n
            IF no such item THEN
                RETURN    "No Solution"
S {value of x}
sum + x
        RETURN S


Example     Make a change for 2.89 (289 cents) here n = 2.89 and the solution contains 2 dollars, 3 quarters, 1 dime and 4 pennies. The algorithm is greedy because at every stage it chooses the largest coin without worrying about the consequences. Moreover, it never changes its mind in the sense that once a coin has been included in the solution set, it remains there.


Characteristics and Features of Problems solved by Greedy Algorithms

To construct the solution in an optimal way. Algorithm maintains two sets. One contains chosen items and the other contains rejected items.

The greedy algorithm consists of four (4) function.

  1. A function that checks whether chosen set of items provide a solution.
  2. A function that checks the feasibility of a set.
  3. The selection function tells which of the candidates is the most promising.
  4. An objective function, which does not appear explicitly, gives the value of a solution.


Structure Greedy Algorithm


Definitions of feasibility

A feasible set (of candidates) is promising if it can be extended to produce not merely a solution, but an optimal solution to the problem. In particular, the empty set is always promising why? (because an optimal solution always exists)

Unlike Dynamic Programming, which solves the subproblems bottom-up, a greedy strategy usually progresses in a top-down fashion, making one greedy choice after another, reducing each problem to a smaller one.

Greedy-Choice Property

The "greedy-choice property" and "optimal substructure" are two ingredients in the problem that lend to a greedy strategy.

Greedy-Choice Property

It says that a globally optimal solution can be arrived at by making a locally optimal choice.