sparse-matrix

I am filling a sparse matrix P (230k,290k) with values coming from a text file which I read line by line, here is the (simplified) code while ... C = textscan(text_line,'%d','delimiter',',','EmptyValue', 0); line_number = line_number+1; P(line_number,:)=C{1}; end the problem I have is that while at the beginning the P(line_number,:)=C{1}; statement is fast, after a few thousands lines become exterely slow, I guess because Matlab need to find the memory space to allocate every time. Is there a way to pre-allocate memory with sparse matrixes? I don't think so but maybe I am missing something.

I have a working LAPACK implementation and that, as far as I read, contains BLAS. I want to use SPARSE BLAS and as far as I understand this website , SPARSE BLAS is part of BLAS. But when I tried to run the code below from the sparse blas manual using g++ -o sparse.x sparse_blas_example.c -L/usr/local/lib -lblas && ./sparse_ex.x the compiler (or linker?) asked for blas_sparse.h. When I put that file in the working directory I got: ludi@ludi-M17xR4:~/Desktop/tests$ g++ -o sparse.x sparse_blas_example.c -L/usr/local/lib -lblas && ./sparse_ex.x In file included from sparse_blas_example.c:3:0:

Problem I have a set of equations with variables denoted with lowercase variables and constants with uppercase variables as such A = a + b B = c + d C = a + b + c + d + e I'm provided the information as to the structure of these equations in a pandas DataFrame with two columns: Constants and Variables E.g. df = pd.DataFrame([['A','a'],['A','b'],['B','c'],['B','d'],['C','a'],['C','b'], ['C','c'],['C','d'],['C','e']],columns=['Constants','Variables']) I then convert this to a sparse CSC matrix by using NetworkX table = nx.bipartite.biadjacency_matrix(nx.from_pandas_dataframe(df,'Constants',

Does Matlab support efficient operations on large sparse tensors? More specifically: Is there an elegant way, similar to sparse , of loading and storing a sparse tensor? As far as I can understand, sparse can only load matrices. Are operations like tensor product implemented efficiently over sparse tensors? I realize I can always store a tensor as a combination of cell arrays of matrices, but that would require using loops, and I'm hoping to avoid that. Since the data I'm working with is very large, I cannot consider a non-sparse representation. Out of the box, I believe MATLAB only handles

I'm not sure what I am doing wrong but it seems that row indexing a scipy csr_matrix is approximately 2 folds slower compared to numpy arrays (see code below). Shouldn't row indexing of csr matrices be faster than dense matrices because only few non-zero elements are extracted like in the case below ? Are there tricks to make row indexing faster for scipy csr matrices ? import numpy as np import timeit from scipy.sparse import csr_matrix # Generate random matrix A = np.random.rand(5000, 1000) # Make A sparse A[:, 4:] =0 # Create sparse matrix A_sparse = csr_matrix(A) # Create row indexing

I have the network representation in a 2D coordinate space. I have an adjacency matrix Adj (which is sparse) and a coordinate matrix with the x,y values of all the points/nodes/vertices in the graph which are drawn. I would like to compute as efficiently as possible the distance between these points. I would like to avoid cycling through the entries in the matrix and computing the pairwise distances one by one. [n, d] = size(coordinate); assert(d == 2); resi = sparse(Adj * diag(1:n)); resj = sparse(diag(1:n) * Adj); res = sparse(zeros(n)); f = find(Adj) res(f) = sqrt((coordinate(resi(f), 1) -

When creating a sparse matrix in Matlab it seems that you can create a sparse matrix either filled with logicals or double valued numbers. While reading around I understood that Matlab does not have support for other type of sparse matrices, i.e. uint8 or other integers. In my application I know that max(values)==16 , and the memory is a crucial thing, therefore I would like to have uint8 sparse matrices. Is there a way of creating a unit8 sparse matrix? If not (most likely), is there any apparent reason of why Matlab has not implemented uint8 sparse matrices? I can see how using uint8 instead