sparse-matrix

I currently want to multiply a large sparse matrix(~1M x 200k) with its transpose. The values of the resulting matrix would be in float. I tried loading the matrix in scipy's sparse matrix and by multiplying each row of first matrix with the second matrix. The multiplication took ~2hrs to complete. What is the efficient way to achieve this multiplication? Because I see a pattern in the computation. The matrix being large and sparse . The multiplication of a matrix with its transpose. So, the resulting matrix would be symmetric. I would like to know what libraries can achieve the computation

I'm trying to create a "sparse" vector class in C++, like so: template<typename V, V Default> class SparseVector { ... } Internally, it will be represented by an std::map<int, V> (where V is the type of value stored). If an element is not present in the map, we will pretend that it is equal to the value Default from the template argument. However, I'm having trouble overloading the subscript operator, [] . I must overload the [] operator, because I'm passing objects from this class into a Boost function that expects [] to work correctly. The const version is simple enough: check whether the

I need to convert a scipy sparse matrix to cvxopt's sparse matrix format, spmatrix, and haven't come across anything yet (the matrix is too big to be converted to dense, of course). Any ideas how to do this? Mtap1 The more robust answer is a combination of hpaulj's answer and OferHelman's answer . def scipy_sparse_to_spmatrix(A): coo = A.tocoo() SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape) return SP Defining the shape variable preserves the dimensionality of A on SP. I found that any zero columns ending the scipy sparse matrix would be lost without this

Is there a built-in function in either slam package or Matrix package to convert a sparse matrix in simple triplet matrix form (from slam package) to a sparse matrix in dgTMatrix/dgCMatrix form (from Matrix package) ? And is there a built-in way to access non-zero entries from simple triplet matrix ? I'm working in R Actually, there is a built-in way: simple_triplet_matrix_sparse <- sparseMatrix(i=simple_triplet_matrix_sparse$i, j=simple_triplet_matrix_sparse$j, x=simple_triplet_matrix_sparse$v, dims=c(simple_triplet_matrix_sparse$nrow, simple_triplet_matrix_sparse$ncol)) From my own

I am using a python function called "incidence_matrix(G)", which returns the incident matrix of graph. It is from Networkx package. The problem that I am facing is the return type of this function is "Scipy Sparse Matrix". I need to have the Incident matrix in the format of numpy matrix or array. I was wondering if there is any easy way of doing that or not? Or is there any built-in function that can do this transformation for me or not? Thanks The scipy.sparse.*_matrix has several useful methods, for example, if a is e.g. scipy.sparse.csr_matrix : a.toarray() or a.A - Return a dense ndarray