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Conjugate gradient method

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Rational Krylov for Stieltjes matrix functions: convergence and pole selection

Stefano Massei

Evaluating the action of a matrix function on a vector, that is

x=f(\mathcal M)v

, is an ubiquitous task in applications. When

\mathcal M

is large, one usually relies on Krylov projection methods. In this paper, we provide effective choices for the pole ...

2019

Conjugate gradient method

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SparseFool: a few pixels make a big difference

Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint

Seismic displacement demand prediction in non-linear domain: Optimization of the N2 method

Accuracy Assessment of Nonlinear Seismic Displacement Demand Predicted by Simplified Methods for the Plateau Range of Design Response Spectra

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An algebraic least squares reduced basis method for the solution of nonaffinely parametrized Stokes equations

Rational Krylov for Stieltjes matrix functions: convergence and pole selection

Shape optimisation of technical devices via gradient descent using convolutional neural network proxies

Inverse Analysis of Radiative Flux Maps for the Characterization of High Flux Sources

Mapping the site-specific accuracy of loop-based local pulse wave velocity estimation and reflection magnitude: a 1D arterial network model analysis

Seismic displacement demand prediction in non-linear domain: Optimization of the N2 method

Accuracy Assessment of Nonlinear Seismic Displacement Demand Predicted by Simplified Methods for the Plateau Range of Design Response Spectra

Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Interpolation Approach

An algebraic least squares reduced basis method for the solution of nonaffinely parametrized Stokes equations

SparseFool: a few pixels make a big difference

Rational Krylov for Stieltjes matrix functions: convergence and pole selection

Mapping the site-specific accuracy of loop-based local pulse wave velocity estimation and reflection magnitude: a 1D arterial network model analysis

Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint

Shape optimisation of technical devices via gradient descent using convolutional neural network proxies

Inverse Analysis of Radiative Flux Maps for the Characterization of High Flux Sources