Results 41 to 50 of about 641 (184)
A Preconditioned Majorization‐Minimization Method for ℓ2$$ {\ell}^2 $$‐ℓq$$ {\ell}^q $$ Minimization
Numerical Linear Algebra with Applications, Volume 33, Issue 3, June 2026.ABSTRACT The need to minimize a linear combination of an expression that involves an ℓq$$ {\ell}^q $$‐norm of a linear transformation of the computed solution and the ℓ2$$ {\ell}^2 $$‐norm of the residual error arises in image restoration as well as in statistics.
A. Buccini +3 more
wiley +1 more source
Extended block Hessenberg method for large-scale Sylvester differential matrix equations [PDF]
Journal of Mahani Mathematical ResearchIn this paper, we consider large-scale low-rank Sylvester differential matrix equations. We present two iterative methods for the approximate solution of such differential matrix equations. In the first method, exploiting the extended block Krylov method,
Azita Tajaddini
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A Method of Indefinite Krylov Subspace for Eigenvalue Problem [PDF]
Mathematical Problems in Engineering, 2018 We describe an indefinite state of Arnoldi’s method for solving the eigenvalues problems. In the following, we scrutinize the indefinite state of Lanczos’ method for solving the eigenvalue problems and we show that this method for the J-Hermitian matrices works much better than Arnoldi’s method.
M. Aliyari, M. Ghasemi Kamalvand
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An Augmented Lagrangian Preconditioner for Navier–Stokes Equations With Runge–Kutta in Time
Numerical Linear Algebra with Applications, Volume 33, Issue 3, June 2026.ABSTRACT We consider an implicit Runge–Kutta method for the numerical time integration of the nonstationary incompressible Navier–Stokes equations. This yields a sequence of nonlinear problems to be solved for the stages of the Runge–Kutta method. The resulting nonlinear system of differential equations is discretized using a finite element method.
Santolo Leveque +2 more
wiley +1 more source
Krylov Subspace Methods on Supercomputers [PDF]
SIAM Journal on Scientific and Statistical Computing, 1989 This paper presents a short survey of recent research on Krylov subspace methods with emphasis on implementation on vector and parallel computers. Conjugate gradient methods have proven very useful on traditional scalar computers, and their popularity is likely to increase as three-dimensional models gain importance.
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Gram Decay and Intrinsic Dimensions of Krylov Subspaces
Numerical Linear Algebra with Applications, Volume 33, Issue 3, June 2026.ABSTRACT Krylov subspace methods solve large sparse linear systems Ax=b$$ Ax=b $$ by building a sequence of polynomial approximations to A−1b$$ {A}^{-1}b $$ from successive matrix‐vector products. In finite precision, the number of numerically independent directions that can be extracted from this sequence is bounded by the intrinsic information ...
Stephen J. Thomas
wiley +1 more source
Performance of relaxed iterative methods for image deblurring problems
Journal of Algorithms & Computational Technology, 2019 In this paper, we consider performance of relaxation iterative methods for four types of image deblurring problems with different regularization terms.
Jae H Yun
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Simultaneous Inversion for Underactuated Mechanical Systems with Servo‐Constraints
Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.ABSTRACT The dynamic inversion of underactuated mechanical systems can be formulated in the servo‐constraint framework using a set of differential‐algebraic equations (DAEs). In case of a high differentiation index, the inversion‐based feedforward control design poses significant challenges.
Tengman Wang
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Iterative Krylov Subspace Methods for Linear Port‐Hamiltonian Systems
Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.ABSTRACT In this work, we present a structure‐preserving Krylov subspace iteration scheme for solving the equation systems that arise from the Gauss integration of linear energy‐conserving and dissipative differential systems (e.g., Poisson systems, gradient systems, and port‐Hamiltonian systems).
Stefan Maier, Nicole Marheineke
wiley +1 more source
Stochastic estimation of level density in nuclear shell-model calculations
EPJ Web of Conferences, 2016 An estimation method of the nuclear level density stochastically based on nuclear shell-model calculations is introduced. In order to count the number of the eigen-values of the shell-model Hamiltonian matrix, we perform the contour integral of the ...
Shimizu Noritaka +5 more
doaj +1 more source