The full-space Lagrange--Newton algorithm is one of the numerical algorithms for solving
problems arising from optimization problems constrained by nonlinear partial differential …

In recent years, Bayesian model updating techniques based on measured data have been
applied to many engineering and applied science problems. At the same time, parallel …

PDE-constrained optimization problems have a wide range of applications, but they lead to
very large and ill-conditioned linear systems, especially if the problems are time dependent …

As the number of processor cores on supercomputers becomes larger and larger, algorithms
with high degree of parallelism attract more attention. In this work, we propose a two-level …

In this paper, we propose a parallel space-time domain decomposition method for solving
an unsteady source identification problem governed by the linear convection-diffusion …

We study a new optimization scheme that generates smooth and robust solutions for
Dirichlet velocity boundary control (DVBC) of conjugate heat transfer (CHT) processes. The …

In large-scale simulations of optimization problems constrained by partial differential
equations (PDEs), the class of fully coupled methods is attracting great attention due to their …

This work aims to develop an adaptive nonlinear elimination preconditioned inexact Newton
method as the numerical solution of large sparse multi-component partial differential …

In this paper, we develop a class of parallel semismooth Newton algorithms for the
numerical solution of the American option under the Black–Scholes–Merton pricing …

For time-dependent control problems, the class of sub-optimal algorithms is popular and the
parallelization is usually applied in the spatial dimension only. In the paper, we develop a …