## Naive algorithm

Take for example a backpack problem. The naive algorithm would consist in taking the objects of the smallest size first until no new object could be put in the bag.

foriof1Tonotifw [i] + w_conso ≤ Wsox [i]: = 1 w_conso: = w_conso + w [i]otherwisex [i]: = 0end ifend for

## Greedy algorithm (intuitive method)

Intuitively, we can say to ourselves that to maximize the utility in the backpack problem, it suffices to put the element having the greatest utility (with a size compatible with the remaining size in the bag) until no more have enough room in the bag.

For this we will consider **x** and **w** the vectors of the choice of the object and of its weight sorted in decreasing order of efficiency (*efficiency = utility / weight*). Then the algorithm works like the naive method.

**1**and utility

**2**, and an object of size and utility equal to the size of the bag. Under these conditions the first object with the greatest utility, it will be chosen first. There is no more room to place the second object. The optimal solution would be to choose only the second object in the bag.

## Brute force or enumeration

Usually, the solution tree is represented as the name suggests as a tree. Let's take the example of the backpack problem with four objects with the following parameters and a bag size of 30:

object | 1 | 2 | 3 | 4 |

u_i | 7 | 3 | 4 | 3 |

w_i | 13 | 8 | 12 | 10 |

The tree is built by iteration: at each iteration, the new leaves represent whether or not to take the next object. We get the following binary tree:

The tops in red exceed the maximum bag size, the top in purple is the optimal solution. As you can see, the number of leaf is equal to 2^{(n-1)} for the object **not**. The number of nodes generated up to the object n is therefore 2^{not} -1.