18,270 research outputs found
LSMR: An iterative algorithm for sparse least-squares problems
An iterative method LSMR is presented for solving linear systems and
least-squares problem \min \norm{Ax-b}_2, with being sparse or a fast
linear operator. LSMR is based on the Golub-Kahan bidiagonalization process. It
is analytically equivalent to the MINRES method applied to the normal equation
A\T Ax = A\T b, so that the quantities \norm{A\T r_k} are monotonically
decreasing (where is the residual for the current iterate
). In practice we observe that \norm{r_k} also decreases monotonically.
Compared to LSQR, for which only \norm{r_k} is monotonic, it is safer to
terminate LSMR early. Improvements for the new iterative method in the presence
of extra available memory are also explored.Comment: 21 page
Accuracy controlled data assimilation for parabolic problems
This paper is concerned with the recovery of (approximate) solutions to
parabolic problems from incomplete and possibly inconsistent observational
data, given on a time-space cylinder that is a strict subset of the
computational domain under consideration. Unlike previous approaches to this
and related problems our starting point is a regularized least squares
formulation in a continuous infinite-dimensional setting that is based on
stable variational time-space formulations of the parabolic PDE. This allows us
to derive a priori as well as a posteriori error bounds for the recovered
states with respect to a certain reference solution. In these bounds the
regularization parameter is disentangled from the underlying discretization. An
important ingredient for the derivation of a posteriori bounds is the
construction of suitable Fortin operators which allow us to control oscillation
errors stemming from the discretization of dual norms. Moreover, the
variational framework allows us to contrive preconditioners for the discrete
problems whose application can be performed in linear time, and for which the
condition numbers of the preconditioned systems are uniformly proportional to
that of the regularized continuous problem.
In particular, we provide suitable stopping criteria for the iterative
solvers based on the a posteriori error bounds. The presented numerical
experiments quantify the theoretical findings and demonstrate the performance
of the numerical scheme in relation with the underlying discretization and
regularization
A Gauss--Newton iteration for Total Least Squares problems
The Total Least Squares solution of an overdetermined, approximate linear
equation minimizes a nonlinear function which characterizes the
backward error. We show that a globally convergent variant of the Gauss--Newton
iteration can be tailored to compute that solution. At each iteration, the
proposed method requires the solution of an ordinary least squares problem
where the matrix is perturbed by a rank-one term.Comment: 14 pages, no figure
MINRES-QLP: a Krylov subspace method for indefinite or singular symmetric systems
CG, SYMMLQ, and MINRES are Krylov subspace methods for solving symmetric
systems of linear equations. When these methods are applied to an incompatible
system (that is, a singular symmetric least-squares problem), CG could break
down and SYMMLQ's solution could explode, while MINRES would give a
least-squares solution but not necessarily the minimum-length (pseudoinverse)
solution. This understanding motivates us to design a MINRES-like algorithm to
compute minimum-length solutions to singular symmetric systems.
MINRES uses QR factors of the tridiagonal matrix from the Lanczos process
(where R is upper-tridiagonal). MINRES-QLP uses a QLP decomposition (where
rotations on the right reduce R to lower-tridiagonal form). On ill-conditioned
systems (singular or not), MINRES-QLP can give more accurate solutions than
MINRES. We derive preconditioned MINRES-QLP, new stopping rules, and better
estimates of the solution and residual norms, the matrix norm, and the
condition number.Comment: 26 pages, 6 figure
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