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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…