sparse-matrix

I am looking for a good (in the best case actively maintained) C++ matrix library. Thereby it should be templated, because I want to use a complex of rationals as numerical type. The matrices what I am dealing with are mainly sparse and unitary. Can you please suggest libraries and also give a small explaination why to use them, because I know how to find them, but I cannot really decide what is suitable for me because I am missing the experience with them. EDIT: The main operations I am dealing with are matrix multiplication , scalar multiplication with a vector and kronecker product . The

The SciPy Sparse Matrix tutorial is very good -- but it actually leaves the section on slicing un(der)developed (still in outline form -- see section: "Handling Sparse Matrices"). I will try and update the tutorial, once this question is answered. I have a large sparse matrix -- currently in dok_matrix format. import numpy as np from scipy import sparse M = sparse.dok_matrix((10**6, 10**6)) For various methods I want to be able to slice columns and for others I want to slice rows. Ideally I would use advanced-indexing (i.e. a boolean vector, bool_vect ) with which to slice a sparse matrix M --

I'm working with some rather large sparse matrices (from 5000x5000 to 20000x20000) and need to find an efficient way to concatenate matrices in a flexible way in order to construct a stochastic matrix from separate parts. Right now I'm using the following way to concatenate four matrices, but it's horribly inefficient. Is there any better way to do this that doesn't involve converting to a dense matrix? rmat[0:m1.shape[0],0:m1.shape[1]] = m1 rmat[m1.shape[0]:rmat.shape[0],m1.shape[1]:rmat.shape[1]] = m2 rmat[0:m1.shape[0],m1.shape[1]:rmat.shape[1]] = bridge rmat[m1.shape[0]:rmat.shape[0],0:m1

What would be the most efficient way to concatenate sparse matrices in Python using SciPy/Numpy? Here I used the following: >>> np.hstack((X, X2)) array([ <49998x70000 sparse matrix of type '<class 'numpy.float64'>' with 1135520 stored elements in Compressed Sparse Row format>, <49998x70000 sparse matrix of type '<class 'numpy.int64'>' with 1135520 stored elements in Compressed Sparse Row format>], dtype=object) I would like to use both predictors in a regression, but the current format is obviously not what I'm looking for. Would it be possible to get the following: <49998x1400000 sparse

For SciPy sparse matrix, one can use todense() or toarray() to transform to NumPy matrix or array. What are the functions to do the inverse? I searched, but got no idea what keywords should be the right hit. You can pass a numpy array or matrix as an argument when initializing a sparse matrix. For a CSR matrix, for example, you can do the following. >>> import numpy as np >>> from scipy import sparse >>> A = np.array([[1,2,0],[0,0,3],[1,0,4]]) >>> B = np.matrix([[1,2,0],[0,0,3],[1,0,4]]) >>> A array([[1, 2, 0], [0, 0, 3], [1, 0, 4]]) >>> sA = sparse.csr_matrix(A) # Here's the initialization of