19 citations · 33 across the 8 of their papers we have counts for
8 papers
Convergence analysis of a regularized inexact interior-point method for linear programming problems
Jeffrey Cornelis, Wim Vanroose
Interior-point methods for linear programming problems require the repeated solution of a linear system of equations. Solving these linear systems is non-trivial due to the severe…
Sequential Projected Newton method for regularization of nonlinear least squares problems
Jeffrey Cornelis, Wim Vanroose
We develop a computationally efficient algorithm for the automatic regularization of nonlinear inverse problems based on the discrepancy principle. We formulate the problem as an e…
Krylov-Simplex method that minimizes the residual in -norm or -norm
Wim Vanroose, Jeffrey Cornelis
The paper presents two variants of a Krylov-Simplex iterative method that combines Krylov and simplex iterations to minimize the residual . The first method minimizes $\|…
Projected Newton method for noise constrained regularization
Jeffrey Cornelis, Wim Vanroose
Choosing an appropriate regularization term is necessary to obtain a meaningful solution to an ill-posed linear inverse problem contaminated with measurement errors or noise. The $…
Projected Newton Method for noise constrained Tikhonov regularization
Jeffrey Cornelis, Nick Schenkels, Wim Vanroose
Tikhonov regularization is a popular approach to obtain a meaningful solution for ill-conditioned linear least squares problems. A relatively simple way of choosing a good regulari…
Improving strong scaling of the Conjugate Gradient method for solving large linear systems using global reduction pipelining
Siegfried Cools, Jeffrey Cornelis, Pieter Ghysels +1
This paper presents performance results comparing MPI-based implementations of the popular Conjugate Gradient (CG) method and several of its communication hiding (or 'pipelined') v…
Numerically Stable Recurrence Relations for the Communication Hiding Pipelined Conjugate Gradient Method
Siegfried Cools, Jeffrey Cornelis, Wim Vanroose
Pipelined Krylov subspace methods (also referred to as communication-hiding methods) have been proposed in the literature as a scalable alternative to classic Krylov subspace algor…
The Communication-Hiding Conjugate Gradient Method with Deep Pipelines
Jeffrey Cornelis, Siegfried Cools, Wim Vanroose
Krylov subspace methods are among the most efficient solvers for large scale linear algebra problems. Nevertheless, classic Krylov subspace algorithms do not scale well on massivel…