Scipy稀疏dia_matrix解算器 [英] Scipy sparse dia_matrix solver

问题描述

在一个scipy程序中，我正在创建一个带有5个对角线的dia_matrix(稀疏矩阵类型).中心对角线+1& -1对角线和+4& -4对角线(通常>> 4，但是原理相同)，即我具有以下形式的典型PDE系统矩阵:

In a scipy program I'm creating a dia_matrix (sparse matrix type) with 5 diagonals. The centre diagonal the +1 & -1 diagonals and the +4 & -4 diagonals (usually >> 4, but the principle is the same), i.e. I have a typical PDE system matrix of the form:

[ a0  b0  0   0   0   d0  0   0   0  ... 0.0 ]
[ c1  a1  b1  0   0   0   d1  0   0  ... 0.0 ]
[ 0   c2  a2  b2  0   0   0   d2  0  ... 0.0 ]
[ 0   0   c3  a3  b3  0   0   0   d3 ... 0.0 ]
[ 0   0   0   c4  a4  b4  0   0   0  ... 0.0 ]
[ e5  0   0   0   c5  a5  b5  0   0  ... 0.0 ]
[ :   :   :   :   :   :   :   :   :   :  :   ]
[ 0   0   0   0   0   0   0   0   0  ... aN  ]

当我使用scipy.linalg.dsolve.spsolve()求解矩阵方程时，它可以工作，但我收到以下报告给我

When I use scipy.linalg.dsolve.spsolve() to solve the matrix equation it works but I get the following reported back to me

>>> SparseEfficiencyWarning: spsolve requires CSC or CSR matrix format
    warn('spsolve requires CSC or CSR matrix format', SparseEfficiencyWarning)

如果spsolve()对于解决稀疏矩阵类型dia_matrix的效率不高，那么我应该使用什么?

If spsolve() is not efficient for solving the sparse matrix type dia_matrix's then what should I be using?

推荐答案

这个答案有点迟了，但我希望您发现添加了:

I'm a bit late with this answer, but I hope you found that adding:

from scipy.linalg import solve_banded

允许您使用DIA矩阵，而不必求助于CSR或CSC.

Allows you to use a DIA matrix rather than having to resort to CSR or CSC.

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