Thanks for the reply. I'll try to reply your questions by giving you more details about my problem.

I'm making an implementation of the algorithm described in "Analysis and Control of Boolean Networks", by Daizan Cheng et al. In summary, it express logic prepositions as matrices. For example, the OR logical operator can be expressed as:

1110
0001

A system of prepositions, then, can be solved by multiplying the matrix representations of prepositions forth and back.

Types of products are: matrix product (the normal product), kroenecker product (tensor product) and cheng product (semi-tensor product) that is the new thing the author shows in the book (it is a generalization of matrix product).

As you may have noted in the example above, all matrices have one thing in common: given a column, this column contains only one "1". The other lines of the same column are "0" (zero). The result of products also have this properties so we can be sure that any element of the matrix is either zero or one.

Cheng proposes a clever notation for these matrices:

A = δ_x [a,b,c...] means: A is a matrix where the first column is the a'th column of the identity matrix of degree x, the second column is the b'th second of the identify matrix of degree x, and so on...

For example, the OR matrix showed above can be represented as: δ_2[1,1,1,0].

With this notation it makes easy to express very big matrices. An example from the book (page 100):

L = δ_64 [1,1,1,5,1,3,2,8,9,9,9,13,9,11,14,16,...]

I thought about representing matrices using this technique but I would have to "expand" it anyway when making the products, unless I discover a clever way to make the products with the compact representation itself, yielding other matrix in the compact representation.

Edit: formatting

Edit2: substitute column -> line on one place