srch

numeric or logical; if missing, a unit diagonal n x n matrix is created. uplo. for .symDiagonal ( .trDiagonal ), the resulting sparse ...

Sparse Matrix diagonals Description Extract or replace the diagonal of a matrix, or construct a diagonal matrix. Usage # diag (x) diag (x=1, nrow, ncol) diag (x) <- value diag.spam (x=1, nrow, ncol) diag.spam (x) <- value Arguments Details Using diag (x) can have unexpected effects if x is a vector that could be of length one.

Construct a sparse matrix from diagonals. Parameters diagonalssequence of array_like Sequence of arrays containing the matrix diagonals, corresponding to offsets. offsetssequence of int or an int, optional Diagonals to set: k = 0 the main diagonal (default) k > 0 the kth upper diagonal k < 0 the kth lower diagonal shapetuple of int, optional

diagonals in dense NumPy array of shape (n_diag, length). fixed length -> waste space a bit when far from main diagonal; subclass of _data_matrix (sparse matrix ...

A very efficient structure for an extreme case of band matrices, the diagonal matrix, is to store just the entries in the main diagonal as ...

It is a tridiagonal matrix with -2s on the diagonal and 1s on the super- and subdiagonal. There are many ways to generate it—here's one possibility. n = 5; D = sparse(1:n,1:n,-2*ones(1,n),n,n); E = sparse(2:n,1:n-1,ones(1,n-1),n,n); S = E+D+E'

Creating sparse matrices based on their diagonal elements is a common operation, so the function spdiags handles this task. Its syntax is S = spdiags (B,d,m,n) To create an output matrix S of size m -by- n with elements on p diagonals: B is a matrix of size min (m,n) -by- p. The columns of B are the values to populate the diagonals of S.

The storage mode used for square sparse matrices stored in compressed-diagonal storage mode has two variations, depending on whether the matrix is a general ...

S = spdiags (Bin,d,m,n) creates an m -by- n sparse matrix S by taking the columns of Bin and placing them along the diagonals specified by d. example S = spdiags (Bin,d,A) replaces the diagonals in A specified by d with the columns of Bin. Examples collapse all Create Tridiagonal Matrix Try This Example Copy Command

It is a tridiagonal matrix with -2s on the diagonal and 1s on the super- and subdiagonal.

Jan 19, 2016 · A sparse matrix has a diagonal method: M.diagonal() http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csc_matrix.diagonal.html. The numpy diagonal is a little more powerful, allowing you to specify an off diagonal. M.A.diagonal(2)

This function differs from spdiags in the way it handles off-diagonals.,Repeated diagonal offsets are disallowed.,Construct a sparse matrix ...

Construct a sparse matrix from diagonals. Parameters diagonals sequence of array_like. Sequence of arrays containing the matrix diagonals, corresponding to offsets. offsets sequence of int or an int, optional Diagonals to set: k = 0 the main diagonal (default) k > 0 the kth upper diagonal. k < 0 the kth lower diagonal. shape tuple of int, optional

18.01.2016 · I want equivalent of np.diagonal(), but for sparse matrix. python python-2.7 scipy sparse-matrix. Share. Improve this question. Follow edited Apr 23 '19 at 16:38. nbro. 13.3k 23 23 gold badges 93 93 silver badges 178 178 bronze badges. asked …

In a Lower triangular sparse matrix, all elements above the main diagonal have a zero value. This type of sparse matrix is also known as a lower triangular matrix. If you see its pictorial representation, then you find that all the elements having non-zero value are appear below the diagonal. In a lower triangular matrix, Arr i,j=0 where i<j.

scipy.sparse.csr_matrix.diagonal¶ csr_matrix.diagonal(k=0)[source]¶ Returns the kth diagonal of the matrix. Parameters kint, optional Which diagonal to get, corresponding to elements a[i, i+k]. Default: 0 (the main diagonal). New in version 1.0. See also numpy.diagonal Equivalent numpy function. Examples

The functions A=spline matrix(g) and [a,b,c]=spline sparse matrix(g) return dense and sparse versions of this matrix. In order to manufacture a problem, we choose a simple solution vector x and compute f = Ax. However, for the sparse matrix version, we can call f=tridiag sparse mv(a,b,c,x) to compute this product.

Use scipy.sparse.spdiags (which does a lot, and so may be confusing, at first), scipy.sparse.dia_matrix and/or scipy.sparse.lil_diags .

Construct a sparse matrix from diagonals. ... This function differs from spdiags in the way it handles off-diagonals. ... Repeated diagonal offsets are disallowed.

scipy.sparse.csr_matrix.diagonal¶ csr_matrix. diagonal (k = 0) [source] ¶ Returns the kth diagonal of the matrix. Parameters k int, optional. Which diagonal to get, corresponding to elements a[i, i+k]. Default: 0 (the main diagonal).

In a Lower triangular sparse matrix, all elements above the main diagonal have a zero value. This type of sparse matrix is also known as a lower triangular matrix. If you see its pictorial representation, then you find that all the elements having non-zero value are appear below the diagonal.

Construct a sparse diagonal matrix from Pairs of vectors and diagonals. Each vector kv.second will be placed on the kv.first diagonal. By default, the matrix is square and its size is inferred from kv, but a non-square size m×n (padded with zeros as needed) can be specified by passing m,n as the first arguments. Examples

An important special type of sparse matrices is band matrix, defined as follows. The lower bandwidth of a matrix A is the smallest number p such that the entry ai,j vanishes whenever i > j + p. Similarly, the upper bandwidth is the smallest number p such that ai,j = 0 whenever i < j − p (Golub & Van Loan 1996, §1.2.1). For example, a tridiagonal matrixhas lower bandwidth 1 and upper bandwidth 1. As another example, the following sparse matrix has lower and upper bandw…