Lecture 7: Fast sparse solvers

Sparse matrix

DEF: Sparse matrix is a matrix that contains $\mathcal{O}(n)$ nonzero elements.

Sparse matrices are ubiquitous in PDEs

Consider for example a 3D Poisson equation:

$$\Delta T = \frac{\partial^2T}{\partial x^2}+\frac{\partial^2T}{\partial y^2}+\frac{\partial^2T}{\partial z^2}=f.$$

After discretization we obtain five diagonal matrix A:



In [25]:

import matplotlib.pyplot as plt
import numpy as np
import scipy as sp
import matplotlib.cm as cm
%matplotlib inline
N = 3
B = np.diag(2*np.ones(N))  + np.diag((-1)*np.ones(N-1),k=-1)+ np.diag((-1)*np.ones(N-1),k = 1)
Id = np.diag(np.ones(N));
# Assembling a 3D operator:
A = np.kron(Id,np.kron(Id,B)) + np.kron(Id,np.kron(B,Id)) +np.kron(B,np.kron(Id,Id))




In [10]:

plt.spy(A,markersize=34/N**2)




Out[10]:



this matrix has $\mathcal{O}(1)$ elements in a row, therefore it is sparse.

Finite elements method is also likely to give you a system with a sparse matrix.

How to store a sparse matrix

Coordinate format (coo)

(i, j, value)

i.e. store two integer arrays and one real array. Easy to add elements. But how to multiply a matrix by a vector?

CSR format

A matrix is stored as 3 different arrays:

sa, ja, ia

where:

• nnz is the total number of non-zeros for the matrix
• sa is an real-value array of non-zeros for the matrix (length nnz)
• ja is an integer array of column number of the non-zeros (length nnz)
• ia is an integer array of locations of the first non-zero element in each row (length n+1)

(Blackboard figure)

Idea behind CSR

• For each row i we store the column number of the non-zeros (and their) values
• We stack this all together into ja and sa arrays
• We save the location of the first non-zero element in each row

CSR helps for matrix-by-vector product as well

   for i in xrange(n):
for k in xrange(ia(i):ia(i+1)-1):
y(i) += sa(k) * x(ja(k))

Let us do a short timing test



In [26]:

import numpy as np
import scipy as sp
import scipy.sparse
import scipy.sparse.linalg
from scipy.sparse import csc_matrix, csr_matrix, coo_matrix, lil_matrix

A = csr_matrix([10,10])
B = lil_matrix([10,10])
A[0,0] = 1
#print A
B[0,0] = 1
#print B




In [44]:

import numpy as np
import scipy as sp
import scipy.sparse
import scipy.sparse.linalg
from scipy.sparse import csc_matrix, csr_matrix, coo_matrix
import matplotlib.pyplot as plt
import time
%matplotlib inline
n = 1000
ex = np.ones(n);
lp1 = sp.sparse.spdiags(np.vstack((ex,  -2*ex, ex)), [-1, 0, 1], n, n, 'csr');
e = sp.sparse.eye(n)
A = sp.sparse.kron(lp1, e) + sp.sparse.kron(e, lp1)
A = csr_matrix(A)
rhs = np.ones(n * n)
B = coo_matrix(A)
#t0 = time.time()
%timeit A.dot(rhs)
#print time.time() - t0
#t0 = time.time()
%timeit B.dot(rhs)
#print time.time() - t0




100 loops, best of 3: 10.5 ms per loop
100 loops, best of 3: 14 ms per loop



As you see, CSR is faster, and for more unstructured patterns the gain will be larger.

CSR format has difficulties with adding new elements.

How to solve linear systems?

Direct solvers

The direct methods use sparse Gaussian elimination, i.e. they eliminate variables while trying to keep the matrix as sparse as possible.

And often, the inverse of a sparse matrix is not sparse: (it corresponds to some integral operator, so it has block low-rank structure. Details will be later in this course)



In [63]:

N = n = 100
ex = np.ones(n);
a = sp.sparse.spdiags(np.vstack((ex,  -2*ex, ex)), [-1, 0, 1], n, n, 'csr');
a = a.todense()
b = np.array(np.linalg.inv(a))
fig,axes = plt.subplots(1, 2)
axes[0].spy(a)
axes[1].spy(b,markersize=2)




Out[63]:

<matplotlib.lines.Line2D at 0x10fd4eed0>



Looks woefully.



In [65]:

N = n = 5
ex = np.ones(n);
A = sp.sparse.spdiags(np.vstack((ex,  -2*ex, ex)), [-1, 0, 1], n, n, 'csr');
A = A.todense()
B = np.array(np.linalg.inv(A))

print B




[[-0.83333333 -0.66666667 -0.5        -0.33333333 -0.16666667]
[-0.66666667 -1.33333333 -1.         -0.66666667 -0.33333333]
[-0.5        -1.         -1.5        -1.         -0.5       ]
[-0.33333333 -0.66666667 -1.         -1.33333333 -0.66666667]
[-0.16666667 -0.33333333 -0.5        -0.66666667 -0.83333333]]



But occasionally L and U factors can be sparse.



In [57]:

p, l, u = scipy.linalg.lu(a)
fig,axes = plt.subplots(1, 2)
axes[0].spy(l)
axes[1].spy(u)




Out[57]:

<matplotlib.image.AxesImage at 0x10d719350>



In 1D factors L and U are bidiagonal.

In 2D factors L and U looks less optimistic, but still ok.)



In [71]:

n = 3

ex = np.ones(n);
lp1 = sp.sparse.spdiags(np.vstack((ex,  -2*ex, ex)), [-1, 0, 1], n, n, 'csr');
e = sp.sparse.eye(n)
A = sp.sparse.kron(lp1, e) + sp.sparse.kron(e, lp1)
A = csc_matrix(A)
T = scipy.sparse.linalg.splu(A)
fig,axes = plt.subplots(1, 2)
axes[0].spy(a,  markersize=1)
axes[1].spy(T.L, marker='.', markersize=0.4)




Out[71]:

<matplotlib.lines.Line2D at 0x110e0a110>



Sparse matrices and graph ordering

The number of non-zeros in the LU decomposition has a deep connection to the graph theory.
(I.e., there is an edge between $(i, j)$ if $a_{ij} \ne 0$.



In [36]:

import networkx as nx
n = 13
ex = np.ones(n);
lp1 = sp.sparse.spdiags(np.vstack((ex,  -2*ex, ex)), [-1, 0, 1], n, n, 'csr');
e = sp.sparse.eye(n)
A = sp.sparse.kron(lp1, e) + sp.sparse.kron(e, lp1)
A = csc_matrix(A)
G = nx.Graph(A)
nx.draw(G, pos=nx.spring_layout(G), node_size=10)






Strategies for elimination

The reordering that minimizes the fill-in is important, so we can use graph theory to find one.

• Minimum degree ordering - order by the degree of the vertex
• Cuthill–McKee algorithm (and reverse Cuthill-McKee) -- order for a small bandwidth
• Nested dissection: split the graph into two with minimal number of vertices on the separator


In [38]:

import networkx as nx
from networkx.utils import reverse_cuthill_mckee_ordering, cuthill_mckee_ordering
n = 13
ex = np.ones(n);
lp1 = sp.sparse.spdiags(np.vstack((ex,  -2*ex, ex)), [-1, 0, 1], n, n, 'csr');
e = sp.sparse.eye(n)
A = sp.sparse.kron(lp1, e) + sp.sparse.kron(e, lp1)
A = csc_matrix(A)
G = nx.Graph(A)
#rcm = list(reverse_cuthill_mckee_ordering(G))
rcm = list(reverse_cuthill_mckee_ordering(G))
A1 = A[rcm, :][:, rcm]
plt.spy(A1, marker='.', markersize=3)
#p, L, U = scipy.linalg.lu(A1.todense())
#plt.spy(L, marker='.', markersize=0.8)
#nx.draw(G, pos=nx.spring_layout(G), node_size=10)




Out[38]:

<matplotlib.lines.Line2D at 0x10fa37f10>



Florida sparse matrix collection

Florida sparse matrix collection which contains all sorts of matrices for different applications. It also allows for finding test matrices as well! Let's have a look.



In [1]:

from IPython.display import HTML
HTML('<iframe src=http://yifanhu.net/GALLERY/GRAPHS/search.html width=700 height=450></iframe>')




Out[1]:



Test some

Let us check some sparse matrix (and its LU).



In [40]:

fname = 'crystm02.mat'
!wget http://www.cise.ufl.edu/research/sparse/mat/Boeing/$fname   --2015-04-13 17:45:26-- http://www.cise.ufl.edu/research/sparse/mat/Boeing/crystm02.mat Resolving www.cise.ufl.edu... 128.227.248.40 Connecting to www.cise.ufl.edu|128.227.248.40|:80... connected. HTTP request sent, awaiting response... 200 OK Length: 797624 (779K) [text/plain] Saving to: 'crystm02.mat.3' crystm02.mat.3 100%[=====================>] 778.93K 464KB/s in 1.7s 2015-04-13 17:45:29 (464 KB/s) - 'crystm02.mat.3' saved [797624/797624]   In [41]: from scipy.io import loadmat import scipy.sparse q = loadmat(fname) #print q mat = q['Problem']['A'][0, 0] T = scipy.sparse.linalg.splu(mat)   In [42]: #Compute its LU %matplotlib inline import matplotlib.pyplot as plt plt.spy(T.L, markersize=0.1)   Out[42]: <matplotlib.lines.Line2D at 0x10efc0c90>  Iterative solvers The main disadvantage of factorization methods is there computational complexity. A more efficient solution of linear systems can be obtained by iterative methods. This requires a high convergence rate of the iterative process and low arithmetic cost of each iteration. Modern iterative methods are mainly based on the idea of iteration on Krylov subspace. $$\mathcal{K}_i = span\{b,~Ab,~A^2b,~ ..,~ A^{i-1}b\}, ~~ i = 1,2,..$$$$x_i = argmin\{ \|b-Ax\|_{\text{some norm}}:x\in \mathcal{K}_i\}$$ In fact, to apply iterative solver to a system with matrix$~A$all you need to know is • how to multiply matrix by vector • how to apply preconditioner Preconditioners If A is ill conditioned then iterative methods give you a lot of iterations. You can reduce number of iterations if you find matrix$~B$(called preconditioner), such that$~AB$or$~BA$matrices has less conditional number. $$Ax=y \Rightarrow BAx= By$$$$ABz= y, x= Bz.$$ To be a good preconditioner matrix$~B$must be somehow close to inverse matrix of$~A$$$B \approx A^{-1}.$$ Note that$B = A^{-1}$is a perfect preconditioner and gives you 1 iteration to converge. But building this preconditioner requires as much operations as the direct solution requires. Building a preconditioner requires some compromise between time for building it and iterations time. Two basic strategies for building preconditioner: • Use information about elements of matrix$A$• Use additional information about problem. The first strategy, where we use information about elements of matrix$A$For sparse matrices we use only non-zero elements. Good example is a method of Incomplete matrix factorization The main idea here is to avoid full factorization by dropping some elements in the factorization. Drop rules specify type of incomplete factorization and type of preconditioner. Standard ILU preconditioners: The second strategy, where we use additional information about a problem Here we use additional information about where the matrix came from. For example, Multigrid and Domain Decomposition methods (see next lecture for multigrid)  In [2]: from IPython.core.display import HTML def css_styling(): styles = open("./styles/custom.css", "r").read() return HTML(styles) css_styling()   Out[2]: @font-face { font-family: "Computer Modern"; src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf'); } div.cell{ /*width:80%;*/ /*margin-left:auto !important; margin-right:auto;*/ } h1 { font-family: 'Alegreya Sans', sans-serif; } h2 { font-family: 'Fenix', serif; } h3{ font-family: 'Fenix', serif; margin-top:12px; margin-bottom: 3px; } h4{ font-family: 'Fenix', serif; } h5 { font-family: 'Alegreya Sans', sans-serif; } div.text_cell_render{ font-family: 'Alegreya Sans',Computer Modern, "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif; line-height: 1.2; font-size: 160%; /*width:70%;*/ /*margin-left:auto;*/ margin-right:auto; } .CodeMirror{ font-family: "Source Code Pro"; font-size: 90%; } /* .prompt{ display: None; }*/ .text_cell_render h1 { font-weight: 200; font-size: 50pt; line-height: 110%; color:#CD2305; margin-bottom: 0.5em; margin-top: 0.5em; display: block; } .text_cell_render h5 { font-weight: 300; font-size: 16pt; color: #CD2305; font-style: italic; margin-bottom: .5em; margin-top: 0.5em; display: block; } li { line-height: 110%; } .warning{ color: rgb( 240, 20, 20 ) } MathJax.Hub.Config({ TeX: { extensions: ["AMSmath.js"] }, tex2jax: { inlineMath: [ ['$','\$'], ["\$","\$"] ],
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