John W. Pearson

According to our database, John W. Pearson authored at least 36 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Double saddle-point preconditioning for Krylov methods in the inexact sequential homotopy method.

John W. Pearson, Andreas Potschka

Numer. Linear Algebra Appl., August, 2024

Parallel-in-Time Solver for the All-at-Once Runge-Kutta Discretization.

Santolo Leveque, Luca Bergamaschi, Ángeles Martínez, John W. Pearson

SIAM J. Matrix Anal. Appl., 2024

A Framework for the Solution of Tree-Coupled Saddle-Point Systems.

Christoph Hansknecht, Bernhard Heinzelreiter, John W. Pearson, Andreas Potschka

CoRR, 2024

Automatic Differentiation for All-at-once Systems Arising in Certain PDE-Constrained Optimization Problems.

Santolo Leveque, James R. Maddison, John W. Pearson

CoRR, 2024

Fast numerical solvers for parameter identification problems in mathematical biology.

Karolína Benková, John W. Pearson, Mariya Ptashnyk

CoRR, 2024

Efficient nonlocal linear image denoising: Bilevel optimization with Nonequispaced Fast Fourier Transform and matrix-free preconditioning.

Andrés Miniguano-Trujillo, John W. Pearson, Benjamin D. Goddard

CoRR, 2024

Diagonalization-Based Parallel-in-Time Preconditioners for Instationary Fluid Flow Control Problems.

Bernhard Heinzelreiter, John W. Pearson

CoRR, 2024

Spectral analysis of block preconditioners for double saddle-point linear systems with application to PDE-constrained optimization.

Luca Bergamaschi, Angeles Martinez, John W. Pearson, Andreas Potschka

CoRR, 2024

2023

A Preconditioned Interior Point Method for Support Vector Machines Using an ANOVA-Decomposition and NFFT-Based Matrix-Vector Products.

Theresa Wagner, John W. Pearson, Martin Stoll

CoRR, 2023

Fast Iterative Solver for the All-at-Once Runge-Kutta Discretization.

Santolo Leveque, Luca Bergamaschi, Ángeles Martínez, John W. Pearson

CoRR, 2023

2022

Parameter-Robust Preconditioning for Oseen Iteration Applied to Stationary and Instationary Navier-Stokes Control.

Santolo Leveque, John W. Pearson

SIAM J. Sci. Comput., 2022

Fast iterative solver for the optimal control of time-dependent PDEs with Crank-Nicolson discretization in time.

Santolo Leveque, John W. Pearson

Numer. Linear Algebra Appl., 2022

MultiShape: A Spectral Element Method, with Applications to Dynamic Density Functional Theory and PDE-Constrained Optimization.

Jonna C. Roden, Rory D. Mills-Williams, John W. Pearson, Benjamin D. Goddard

CoRR, 2022

General-purpose preconditioning for regularized interior point methods.

Jacek Gondzio, Spyridon Pougkakiotis, John W. Pearson

Comput. Optim. Appl., 2022

2021

A new preconditioning approach for an interior point-proximal method of multipliers for linear and convex quadratic programming.

Luca Bergamaschi, Jacek Gondzio, Ángeles Martínez, John W. Pearson, Spyridon Pougkakiotis

Numer. Linear Algebra Appl., 2021

A Preconditioned Inexact Active-Set Method for Large-Scale Nonlinear Optimal Control Problems.

John W. Pearson, Andreas Potschka

CoRR, 2021

A Note On Symmetric Positive Definite Preconditioners for Multiple Saddle-Point Systems.

John W. Pearson, Andreas Potschka

CoRR, 2021

Saddle point preconditioners for weak-constraint 4D-Var.

Jemima M. Tabeart, John W. Pearson

CoRR, 2021

2020

Fast Solution Methods for Convex Quadratic Optimization of Fractional Differential Equations.

Spyridon Pougkakiotis, John W. Pearson, Santolo Leveque, Jacek Gondzio

SIAM J. Matrix Anal. Appl., 2020

Interior-point methods and preconditioning for PDE-constrained optimization problems involving sparsity terms.

John W. Pearson, Margherita Porcelli, Martin Stoll

Numer. Linear Algebra Appl., 2020

Parameter-robust preconditioning for the optimal control of the wave equation.

Jun Liu, John W. Pearson

Numer. Algorithms, 2020

PDE-Constrained Optimization Models and Pseudospectral Methods for Multiscale Particle Dynamics.

Mildred Aduamoah, Benjamin D. Goddard, John W. Pearson, Jonna C. Roden

CoRR, 2020

2019

Preconditioners and Tensor Product Solvers for Optimal Control Problems from Chemotaxis.

Sergey Dolgov, John W. Pearson

SIAM J. Sci. Comput., 2019

Fast iterative solvers for an optimal transport problem.

Roland Herzog, John W. Pearson, Martin Stoll

Adv. Comput. Math., 2019

2018

Refined saddle-point preconditioners for discretized Stokes problems.

John W. Pearson, Jennifer Pestana, David J. Silvester

Numerische Mathematik, 2018

2017

A Preconditioner for the Ohta-Kawasaki Equation.

Patrick E. Farrell, John W. Pearson

SIAM J. Matrix Anal. Appl., 2017

Fast interior point solution of quadratic programming problems arising from PDE-constrained optimization.

John W. Pearson, Jacek Gondzio

Numerische Mathematik, 2017

Numerical methods for the computation of the confluent and Gauss hypergeometric functions.

John W. Pearson, Sheehan Olver, Mason A. Porter

Numer. Algorithms, 2017

2016

Fast solvers for optimal control problems from pattern formation.

Martin Stoll, John W. Pearson, Philip K. Maini

J. Comput. Phys., 2016

Fast tensor product solvers for optimization problems with fractional differential equations as constraints.

Sergey Dolgov, John W. Pearson, Dmitry V. Savostyanov, Martin Stoll

Appl. Math. Comput., 2016

2015

Preconditioned iterative methods for Navier-Stokes control problems.

John W. Pearson

J. Comput. Phys., 2015

2014

Preconditioners for state-constrained optimal control problems with Moreau-Yosida penalty function.

John W. Pearson, Martin Stoll, Andrew J. Wathen

Numer. Linear Algebra Appl., 2014

2013

Fast Iterative Solution of Reaction-Diffusion Control Problems Arising from Chemical Processes.

John W. Pearson, Martin Stoll

SIAM J. Sci. Comput., 2013

A radial basis function method for solving PDE-constrained optimization problems.

John W. Pearson

Numer. Algorithms, 2013

2012

Regularization-Robust Preconditioners for Time-Dependent PDE-Constrained Optimization Problems.

John W. Pearson, Martin Stoll, Andrew J. Wathen

SIAM J. Matrix Anal. Appl., 2012

A new approximation of the Schur complement in preconditioners for PDE-constrained optimization.

John W. Pearson, Andrew J. Wathen

Numer. Linear Algebra Appl., 2012