activity
20142025
most citedA New Preconditioning Approach for an Interior Point-Proximal Method of Multipliers for Linear and Convex Quadratic Programming

19 citations · 43 across the 17 of their papers we have counts for

collaborators

23 papers

math.NA2025

Parallel-in-Time Preconditioning for Time-Dependent Variational Mean Field Games

Heidi Wolles Ljósheim, Dante Kalise, John W. Pearson +1

We study the numerical approximation of a time-dependent variational mean field game system with local couplings and either periodic or Neumann boundary conditions. Following a var…

math.NA2025

Stabilizing randomized GMRES through flexible GMRES

Stefan Güttel, John W. Pearson

We explore the use of flexible GMRES as an outer wrapper for sketched GMRES. Building on a new bound for the residual of FGMRES in terms of the residual of the preconditioner, we d…

math.NA2025

Block Alpha-Circulant Preconditioners for All-at-Once Diffusion-Based Covariance Operators

Jemima M. Tabeart, Selime Gürol, John W. Pearson +1

Covariance matrices are central to data assimilation and inverse methods derived from statistical estimation theory. Previous work has considered the application of an all-at-once…

math.NA2024

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

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

An automated framework is presented for the numerical solution of optimal control problems with PDEs as constraints, in both the stationary and instationary settings. The associate…

math.NA2024

Fast numerical solvers for parameter identification problems in mathematical biology

Karolína Benková, John W. Pearson, Mariya Ptashnyk

In this paper, we consider effective discretization strategies and iterative solvers for nonlinear PDE-constrained optimization models for pattern evolution within biological proce…

math.NA2024

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

Bernhard Heinzelreiter, John W. Pearson

We derive a new parallel-in-time approach for solving large-scale optimization problems constrained by time-dependent partial differential equations arising from fluid dynamics. Th…