337 research outputs found
Spectral behavior of preconditioned non-Hermitian multilevel block Toeplitz matrices with matrix-valued symbol
This note is devoted to preconditioning strategies for non-Hermitian
multilevel block Toeplitz linear systems associated with a multivariate
Lebesgue integrable matrix-valued symbol. In particular, we consider special
preconditioned matrices, where the preconditioner has a band multilevel block
Toeplitz structure, and we complement known results on the localization of the
spectrum with global distribution results for the eigenvalues of the
preconditioned matrices. In this respect, our main result is as follows. Let
, let be the linear space of complex matrices, and let be functions whose components
belong to .
Consider the matrices , where varies
in and are the multilevel block Toeplitz matrices
of size generated by . Then
, i.e. the family
of matrices has a global (asymptotic)
spectral distribution described by the function , provided
possesses certain properties (which ensure in particular the invertibility of
for all ) and the following topological conditions are met:
the essential range of , defined as the union of the essential ranges
of the eigenvalue functions , does not
disconnect the complex plane and has empty interior. This result generalizes
the one obtained by Donatelli, Neytcheva, Serra-Capizzano in a previous work,
concerning the non-preconditioned case . The last part of this note is
devoted to numerical experiments, which confirm the theoretical analysis and
suggest the choice of optimal GMRES preconditioning techniques to be used for
the considered linear systems.Comment: 18 pages, 26 figure
An Alternating Trust Region Algorithm for Distributed Linearly Constrained Nonlinear Programs, Application to the AC Optimal Power Flow
A novel trust region method for solving linearly constrained nonlinear
programs is presented. The proposed technique is amenable to a distributed
implementation, as its salient ingredient is an alternating projected gradient
sweep in place of the Cauchy point computation. It is proven that the algorithm
yields a sequence that globally converges to a critical point. As a result of
some changes to the standard trust region method, namely a proximal
regularisation of the trust region subproblem, it is shown that the local
convergence rate is linear with an arbitrarily small ratio. Thus, convergence
is locally almost superlinear, under standard regularity assumptions. The
proposed method is successfully applied to compute local solutions to
alternating current optimal power flow problems in transmission and
distribution networks. Moreover, the new mechanism for computing a Cauchy point
compares favourably against the standard projected search as for its activity
detection properties
BTTB preconditioners for BTTB least squares problems
AbstractIn this paper, we consider solving the least squares problem minx‖b-Tx‖2 by using preconditioned conjugate gradient (PCG) methods, where T is a large rectangular matrix which consists of several square block-Toeplitz–Toeplitz-block (BTTB) matrices and b is a column vector. We propose a BTTB preconditioner to speed up the PCG method and prove that the BTTB preconditioner is a good preconditioner. We then discuss the construction of the BTTB preconditioner. Numerical examples, including image restoration problems, are given to illustrate the efficiency of our BTTB preconditioner. Numerical results show that our BTTB preconditioner is more efficient than the well-known Level-1 and Level-2 circulant preconditioners
A preconditioned MINRES method for nonsymmetric Toeplitz matrices
Circulant preconditioning for symmetric Toeplitz linear systems is well established; theoretical guarantees of fast convergence for the conjugate gradient method are descriptive of the convergence seen in computations. This has led to robust and highly efficient solvers based on use of the fast Fourier transform exactly as originally envisaged in [G. Strang, Stud. Appl. Math., 74 (1986), pp. 171--176]. For nonsymmetric systems, the lack of generally descriptive convergence theory for most iterative methods of Krylov type has provided a barrier to such a comprehensive guarantee, though several methods have been proposed and some analysis of performance with the normal equations is available. In this paper, by the simple device of reordering, we rigorously establish a circulant preconditioned short recurrence Krylov subspace iterative method of minimum residual type for nonsymmetric (and possibly highly nonnormal) Toeplitz systems. Convergence estimates similar to those in the symmetric case are established
Quantum Circulant Preconditioner for Linear System of Equations
We consider the quantum linear solver for with the circulant
preconditioner . The main technique is the singular value estimation (SVE)
introduced in [I. Kerenidis and A. Prakash, Quantum recommendation system, in
ITCS 2017]. However, some modifications of SVE should be made to solve the
preconditioned linear system . Moreover, different from
the preconditioned linear system considered in [B. D. Clader, B. C. Jacobs, C.
R. Sprouse, Preconditioned quantum linear system algorithm, Phys. Rev. Lett.,
2013], the circulant preconditioner is easy to construct and can be directly
applied to general dense non-Hermitian cases. The time complexity depends on
the condition numbers of and , as well as the Frobenius norm
Improving Performance of Iterative Methods by Lossy Checkponting
Iterative methods are commonly used approaches to solve large, sparse linear
systems, which are fundamental operations for many modern scientific
simulations. When the large-scale iterative methods are running with a large
number of ranks in parallel, they have to checkpoint the dynamic variables
periodically in case of unavoidable fail-stop errors, requiring fast I/O
systems and large storage space. To this end, significantly reducing the
checkpointing overhead is critical to improving the overall performance of
iterative methods. Our contribution is fourfold. (1) We propose a novel lossy
checkpointing scheme that can significantly improve the checkpointing
performance of iterative methods by leveraging lossy compressors. (2) We
formulate a lossy checkpointing performance model and derive theoretically an
upper bound for the extra number of iterations caused by the distortion of data
in lossy checkpoints, in order to guarantee the performance improvement under
the lossy checkpointing scheme. (3) We analyze the impact of lossy
checkpointing (i.e., extra number of iterations caused by lossy checkpointing
files) for multiple types of iterative methods. (4)We evaluate the lossy
checkpointing scheme with optimal checkpointing intervals on a high-performance
computing environment with 2,048 cores, using a well-known scientific
computation package PETSc and a state-of-the-art checkpoint/restart toolkit.
Experiments show that our optimized lossy checkpointing scheme can
significantly reduce the fault tolerance overhead for iterative methods by
23%~70% compared with traditional checkpointing and 20%~58% compared with
lossless-compressed checkpointing, in the presence of system failures.Comment: 14 pages, 10 figures, HPDC'1
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