sparse.linalg.isolve.lsqr

Sparse Equations and Least Squares.

The original Fortran code was written by C. C. Paige and M. A. Saunders as described in

C. C. Paige and M. A. Saunders, LSQR: An algorithm for sparse linear equations and sparse least squares, TOMS 8(1), 43–71 (1982).

C. C. Paige and M. A. Saunders, Algorithm 583; LSQR: Sparse linear equations and least-squares problems, TOMS 8(2), 195–209 (1982).

It is licensed under the following BSD license:

Copyright (c) 2006, Systems Optimization Laboratory All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

The Fortran code was translated to Python for use in CVXOPT by Jeffery Kline with contributions by Mridul Aanjaneya and Bob Myhill.

Adapted for SciPy by Stefan van der Walt.

Module Contents

Functions

_sym_ortho(a,b) Stable implementation of Givens rotation.
lsqr(A,b,damp=0.0,atol=1e-08,btol=1e-08,conlim=100000000.0,iter_lim=None,show=False,calc_var=False,x0=None) Find the least-squares solution to a large, sparse, linear system
_sym_ortho(a, b)

Stable implementation of Givens rotation.

The routine ‘SymOrtho’ was added for numerical stability. This is recommended by S.-C. Choi in [1]_. It removes the unpleasant potential of 1/eps in some important places (see, for example text following “Compute the next plane rotation Qk” in minres.py).

[1]S.-C. Choi, “Iterative Methods for Singular Linear Equations and Least-Squares Problems”, Dissertation, http://www.stanford.edu/group/SOL/dissertations/sou-cheng-choi-thesis.pdf
lsqr(A, b, damp=0.0, atol=1e-08, btol=1e-08, conlim=100000000.0, iter_lim=None, show=False, calc_var=False, x0=None)

Find the least-squares solution to a large, sparse, linear system of equations.

The function solves Ax = b or min ||b - Ax||^2 or min ||Ax - b||^2 + d^2 ||x||^2.

The matrix A may be square or rectangular (over-determined or under-determined), and may have any rank.

1. Unsymmetric equations --    solve  A*x = b

2. Linear least squares  --    solve  A*x = b
                               in the least-squares sense

3. Damped least squares  --    solve  (   A    )*x = ( b )
                                      ( damp*I )     ( 0 )
                               in the least-squares sense
A : {sparse matrix, ndarray, LinearOperator}
Representation of an m-by-n matrix. It is required that the linear operator can produce Ax and A^T x.
b : array_like, shape (m,)
Right-hand side vector b.
damp : float
Damping coefficient.
atol, btol : float, optional
Stopping tolerances. If both are 1.0e-9 (say), the final residual norm should be accurate to about 9 digits. (The final x will usually have fewer correct digits, depending on cond(A) and the size of damp.)
conlim : float, optional
Another stopping tolerance. lsqr terminates if an estimate of cond(A) exceeds conlim. For compatible systems Ax = b, conlim could be as large as 1.0e+12 (say). For least-squares problems, conlim should be less than 1.0e+8. Maximum precision can be obtained by setting atol = btol = conlim = zero, but the number of iterations may then be excessive.
iter_lim : int, optional
Explicit limitation on number of iterations (for safety).
show : bool, optional
Display an iteration log.
calc_var : bool, optional
Whether to estimate diagonals of (A'A + damp^2*I)^{-1}.
x0 : array_like, shape (n,), optional

Initial guess of x, if None zeros are used.

New in version 1.0.0.

x : ndarray of float
The final solution.
istop : int
Gives the reason for termination. 1 means x is an approximate solution to Ax = b. 2 means x approximately solves the least-squares problem.
itn : int
Iteration number upon termination.
r1norm : float
norm(r), where r = b - Ax.
r2norm : float
sqrt( norm(r)^2  +  damp^2 * norm(x)^2 ). Equal to r1norm if damp == 0.
anorm : float
Estimate of Frobenius norm of Abar = [[A]; [damp*I]].
acond : float
Estimate of cond(Abar).
arnorm : float
Estimate of norm(A'*r - damp^2*x).
xnorm : float
norm(x)
var : ndarray of float
If calc_var is True, estimates all diagonals of (A'A)^{-1} (if damp == 0) or more generally (A'A + damp^2*I)^{-1}. This is well defined if A has full column rank or damp > 0. (Not sure what var means if rank(A) < n and damp = 0.)

LSQR uses an iterative method to approximate the solution. The number of iterations required to reach a certain accuracy depends strongly on the scaling of the problem. Poor scaling of the rows or columns of A should therefore be avoided where possible.

For example, in problem 1 the solution is unaltered by row-scaling. If a row of A is very small or large compared to the other rows of A, the corresponding row of ( A b ) should be scaled up or down.

In problems 1 and 2, the solution x is easily recovered following column-scaling. Unless better information is known, the nonzero columns of A should be scaled so that they all have the same Euclidean norm (e.g., 1.0).

In problem 3, there is no freedom to re-scale if damp is nonzero. However, the value of damp should be assigned only after attention has been paid to the scaling of A.

The parameter damp is intended to help regularize ill-conditioned systems, by preventing the true solution from being very large. Another aid to regularization is provided by the parameter acond, which may be used to terminate iterations before the computed solution becomes very large.

If some initial estimate x0 is known and if damp == 0, one could proceed as follows:

  1. Compute a residual vector r0 = b - A*x0.
  2. Use LSQR to solve the system A*dx = r0.
  3. Add the correction dx to obtain a final solution x = x0 + dx.

This requires that x0 be available before and after the call to LSQR. To judge the benefits, suppose LSQR takes k1 iterations to solve A*x = b and k2 iterations to solve A*dx = r0. If x0 is “good”, norm(r0) will be smaller than norm(b). If the same stopping tolerances atol and btol are used for each system, k1 and k2 will be similar, but the final solution x0 + dx should be more accurate. The only way to reduce the total work is to use a larger stopping tolerance for the second system. If some value btol is suitable for A*x = b, the larger value btol*norm(b)/norm(r0) should be suitable for A*dx = r0.

Preconditioning is another way to reduce the number of iterations. If it is possible to solve a related system M*x = b efficiently, where M approximates A in some helpful way (e.g. M - A has low rank or its elements are small relative to those of A), LSQR may converge more rapidly on the system A*M(inverse)*z = b, after which x can be recovered by solving M*x = z.

If A is symmetric, LSQR should not be used!

Alternatives are the symmetric conjugate-gradient method (cg) and/or SYMMLQ. SYMMLQ is an implementation of symmetric cg that applies to any symmetric A and will converge more rapidly than LSQR. If A is positive definite, there are other implementations of symmetric cg that require slightly less work per iteration than SYMMLQ (but will take the same number of iterations).

[1]C. C. Paige and M. A. Saunders (1982a). “LSQR: An algorithm for sparse linear equations and sparse least squares”, ACM TOMS 8(1), 43-71.
[2]C. C. Paige and M. A. Saunders (1982b). “Algorithm 583. LSQR: Sparse linear equations and least squares problems”, ACM TOMS 8(2), 195-209.
[3]M. A. Saunders (1995). “Solution of sparse rectangular systems using LSQR and CRAIG”, BIT 35, 588-604.
>>> from scipy.sparse import csc_matrix
>>> from scipy.sparse.linalg import lsqr
>>> A = csc_matrix([[1., 0.], [1., 1.], [0., 1.]], dtype=float)

The first example has the trivial solution [0, 0]

>>> b = np.array([0., 0., 0.], dtype=float)
>>> x, istop, itn, normr = lsqr(A, b)[:4]
The exact solution is  x = 0
>>> istop
0
>>> x
array([ 0.,  0.])

The stopping code istop=0 returned indicates that a vector of zeros was found as a solution. The returned solution x indeed contains [0., 0.]. The next example has a non-trivial solution:

>>> b = np.array([1., 0., -1.], dtype=float)
>>> x, istop, itn, r1norm = lsqr(A, b)[:4]
>>> istop
1
>>> x
array([ 1., -1.])
>>> itn
1
>>> r1norm
4.440892098500627e-16

As indicated by istop=1, lsqr found a solution obeying the tolerance limits. The given solution [1., -1.] obviously solves the equation. The remaining return values include information about the number of iterations (itn=1) and the remaining difference of left and right side of the solved equation. The final example demonstrates the behavior in the case where there is no solution for the equation:

>>> b = np.array([1., 0.01, -1.], dtype=float)
>>> x, istop, itn, r1norm = lsqr(A, b)[:4]
>>> istop
2
>>> x
array([ 1.00333333, -0.99666667])
>>> A.dot(x)-b
array([ 0.00333333, -0.00333333,  0.00333333])
>>> r1norm
0.005773502691896255

istop indicates that the system is inconsistent and thus x is rather an approximate solution to the corresponding least-squares problem. r1norm contains the norm of the minimal residual that was found.