114 citations · 171 across the 6 of their papers we have counts for
9 papers
Conjugate gradient based acceleration for inverse problems
Sergey Voronin, Christophe Zaroli, Naresh P. Cuntoor
The conjugate gradient method is a widely used algorithm for the numerical solution of a system of linear equations. It is particularly attractive because it allows one to take adv…
Randomized Matrix Decompositions using R
N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton +1
Matrix decompositions are fundamental tools in the area of applied mathematics, statistical computing, and machine learning. In particular, low-rank matrix decompositions are vital…
An Iteratively Reweighted Least Squares Algorithm for Sparse Regularization
Sergey Voronin, Ingrid Daubechies
We present a new algorithm and the corresponding convergence analysis for the regularization of linear inverse problems with sparsity constraints, applied to a new generalized spar…
A randomized blocked algorithm for efficiently computing rank-revealing factorizations of matrices
Per-Gunnar Martinsson, Sergey Voronin
This manuscript describes a technique for computing partial rank-revealing factorizations, such as, e.g, a partial QR factorization or a partial singular value decomposition. The m…
RSVDPACK: An implementation of randomized algorithms for computing the singular value, interpolative, and CUR decompositions of matrices on multi-core and GPU architectures
Sergey Voronin, Per-Gunnar Martinsson
RSVDPACK is a library of functions for computing low rank approximations of matrices. The library includes functions for computing standard (partial) factorizations such as the Sin…
Efficient Algorithms for CUR and Interpolative Matrix Decompositions
Sergey Voronin, Per-Gunnar Martinsson
The manuscript describes efficient algorithms for the computation of the CUR and ID decompositions. The methods used are based on simple modifications to the classical truncated pi…
Convolution based smooth approximations to the absolute value function with application to non-smooth regularization
Sergey Voronin, Gorkem Ozkaya, Davis Yoshida
We present new convolution based smooth approximations to the absolute value function and apply them to construct gradient based algorithms such as the nonlinear conjugate gradient…
Compression Approaches for the Regularized Solutions of Linear Systems from Large-Scale Inverse Problems
Sergey Voronin, Dylan Mikesell, Guust Nolet
We introduce and compare new compression approaches to obtain regularized solutions of large linear systems which are commonly encountered in large scale inverse problems. We first…
Solving or resolving global tomographic models with spherical wavelets, and the scale and sparsity of seismic heterogeneity
Frederik J. Simons, Ignace Loris, Guust Nolet +6
We propose a class of spherical wavelet bases for the analysis of geophysical models and forthe tomographic inversion of global seismic data. Its multiresolution character allows f…