I\'m trying t开发者_开发问答o figure out the best way to solve a pentadiagonal matrix. Is there something faster than gaussian elimination?You should do an LU or Cholesky decomposition of the matrix,
I\'m looking for an a command or trick to convert two arrays to a sparse matrix. The two arrays contain x-values and y-values, which gives a coordinate in the cartesian coordinate开发者_StackOverflow中
I have a scipy.sparse.dok_matrix (dimensions m x n), wanting to add a flat numpy-array with length m.
So, I\'m doing some Kmeans classification using numpy arrays that are quite sparse-- lots and lots of zeroes.I figured that I\'d use scipy\'s \'sparse\' package to reduce the storage overhead, but I\'
I have two sparse matrices, m1 and m2: > m1 <- Matrix(data=0,nrow=2, ncol=1, sparse=TRUE, dimnames=list(c(\"b\",\"d\"),NULL))
Related to my other question, I have now modified the sparse matrix solver to use the SOR (Successive Over-Relaxation) method. The code is now as follows:
I\'m writing a sparse matrix solver using the Gauss-Seidel method. By profiling, I\'ve determined that about half of my program\'s time is spent inside the solver. The performance-critical part is as
In Numpy, ix_() is used to grab rows and columns of a matrix, but it doesn\'t seem to work with sparse matrices. For instance, this code works because it uses a dense matrix:
For solving spare matrices, in general, how big does the matrix have t开发者_JAVA技巧o be (as a rule of thumb)
What are so开发者_C百科me of the better libraries for large sparse iterative (conjugate gradient, MINRES, GMRES, etc.) linear algebra system solving?I\'ve often coded my own routines, but I\'m interes
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