The knapsack problem or rucksack problem is a problem in combinatorial optimization: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible.
It derives its name from the problem faced by someone who is constrained by a fixed-size knapsack and must fill it with the most valuable items.
Example of a one-dimensional (constraint) knapsack problem: which boxes should be chosen to maximize the amount of money while still keeping the overall weight under or equal to 15 kg?
The most common problem being solved is the 0/1 knapsack problem,
which restricts the number xi of copies of each kind of item to zero or one.
Given a set of n items numbered from 1 up to n, each with a
weight wi and a value vi, along with a maximum weight
capacity W,
maximize
subject to 
Here xi represents the number of instances of item i to
include in the knapsack. Informally, the problem is to maximize
the sum of the values of the items in the knapsack so that the
sum of the weights is less than or equal to the knapsack's
capacity.
The bounded knapsack problem (BKP) removes the restriction
that there is only one of each item, but restricts the number
xi of copies of each kind of item to a maximum non-negative
integer value c:
maximize
subject to
The unbounded knapsack problem (UKP) places no upper bound
on the number of copies of each kind of item and can be
formulated as above except for that the only restriction
on xi is that it is a non-negative integer.
maximize
subject to
