I am implementing a sparse matrix class in compressed row format. This means i have a fixed number of rows and each row consists of a number of elements (this number can be different for different rows but will not change after the matrix has been initialised.
Is it suitable to implement this via vectors of vectors or will this somehow fragment the memory?
How whould i implement the allocation of this so i will have one big chunk of memory?
Thanks for sharing your wisdom! Dirk
You can use existing libraries, like boost sparse matrix (http://www.boost.org/doc/libs/1_43_0/libs/numeric/ublas/doc/matrix_sparse.htm)
I've implemented the compressed column format with 3 std::vector<int> (2 for row & column indices and one for the value). Why do you need std::vector<std::vector<int>>?
Are you sure you're implementing the right format? A description of the compressed column format (and code for matrix-vector multiplication) can be found here.
You won't get 'one big chunk' with vector-of-vectors. Each row will be a contiguous chunk, but each row's data could be located at a different place on the heap.
Keep in mind that this might be a good thing if you're going to be dealing with huge matrices. The larger the matrix, the less likely you'll be able to acquire a contiguous chunk to fit the whole thing.
The general rule with sparse matrices is that you pick a structure that best fits the location of non-zeros in matrix; so maybe you could write a bit more about the matrix structure, and also what (sort of) algorithms will be invoked on it.
About the memory -- if this matrix is not too big, it is better to alloc it as a one big chunk and make some index magic; this may result in a better cache use.
If it is huge, then the chunked version should be used since it will better fit in memory.