1 citations · 1 across the 11 of their papers we have counts for
14 papers
A class of low-rank short recurrences for nonsymmetric linear matrix equations
Davide Palitta, Catherine E. Powell, Valeria Simoncini
We propose a new class of short matrix recurrences for the solution of nonsymmetric linear equations of the type $\mathbf{A}_1\mathbf{X}\mathbf{B}_1+\ldots+\mathbf{A}_p\mathbf{X}\m…
-SVD via Sketching and the Nearest -orthogonal Matrix
Davide Palitta, Valeria Simoncini
Sketching techniques have gained popularity in numerical linear algebra to accelerate the solution of least squares problems. The so-called -subspace embedding propert…
A subspace-conjugate gradient method for linear matrix equations
Davide Palitta, Martina Iannacito, Valeria Simoncini
The efficient solution of large-scale multiterm linear matrix equations is a challenging task in numerical linear algebra, and it is a largely open problem. We propose a new iterat…
Row-aware Randomized SVD with applications
Davide Palitta, Sascha Portaro
The randomized singular value decomposition proposed in [27] has certainly become one of the most well-established randomization-based algorithms in numerical linear algebra. The k…
Sketched and Truncated Polynomial Krylov Subspace Methods: Matrix Sylvester Equations
Davide Palitta, Marcel Schweitzer, Valeria Simoncini
Thanks to its great potential in reducing both computational cost and memory requirements, combining sketching and Krylov subspace techniques has attracted a lot of attention in th…
Regularized methods via cubic model subspace minimization for nonconvex optimization
Stefania Bellavia, Davide Palitta, Margherita Porcelli +1
Adaptive cubic regularization methods for solving nonconvex problems need the efficient computation of the trial step, involving the minimization of a cubic model. We propose a new…
Sketched and truncated polynomial Krylov methods: Evaluation of matrix functions
Davide Palitta, Marcel Schweitzer, Valeria Simoncini
Among randomized numerical linear algebra strategies, so-called sketching procedures are emerging as effective reduction means to accelerate the computation of Krylov subspace meth…
Stein-based preconditioners for weak-constraint 4D-var
Davide Palitta, Jemima M. Tabeart
Algorithms for data assimilation try to predict the most likely state of a dynamical system by combining information from observations and prior models. Variational approaches, suc…
Very Weak Space-Time Variational Formulation for the Wave Equation: Analysis and Efficient Numerical Solution
Julian Henning, Davide Palitta, Valeria Simoncini +1
We introduce a very weak space-time variational formulation for the wave equation, prove its well-posedness (even in the case of minimal regularity) and optimal inf-sup stability.…
The Short-term Rational Lanczos Method and Applications
Davide Palitta, Stefano Pozza, Valeria Simoncini
Rational Krylov subspaces have become a reference tool in dimension reduction procedures for several application problems. When data matrices are symmetric, a short-term recurrence…
Matrix oriented reduction of space-time Petrov-Galerkin variational problems
Julian Henning, Davide Palitta, Valeria Simoncini +1
Variational formulations of time-dependent PDEs in space and time yield -dimensional problems to be solved numerically. This increases the number of unknowns as well as the…
Numerical methods for large-scale Lyapunov equations with symmetric banded data
Davide Palitta, Valeria Simoncini
The numerical solution of large-scale Lyapunov matrix equations with symmetric banded data has so far received little attention in the rich literature on Lyapunov equations. We aim…
Computationally enhanced projection methods for symmetric Sylvester and Lyapunov matrix equations
Davide Palitta, Valeria Simoncini
In the numerical treatment of large-scale Sylvester and Lyapunov equations, projection methods require solving a reduced problem to check convergence. As the approximation space ex…
Matrix-equation-based strategies for convection-diffusion equations
Davide Palitta, Valeria Simoncini
We are interested in the numerical solution of nonsymmetric linear systems arising from the discretization of convection-diffusion partial differential equations with separable coe…