Public profile
Biography
Matthias Heinkenschloss’ research focuses on the development of theoretically founded, computationally efficient numerical algorithms for large-scale nonlinear optimization problems and their applications to engineering and science problems. Matthias Heinkenschloss’ research focuses on the development of theoretically founded, computationally efficient numerical algorithms for large-scale nonlinear optimization problems and their applications to engineering and science problems. Specific research areas include partial differential equation (PDE) constrained optimization, optimization under uncertainty, optimal control, shape optimization, data-driven model reduction, problem discretzations, iterative solution of linear systems, and domain decomposition in optimization. Applications include flow control, reservoir management, shell structure acoustic optimization, imaging, and shape optimization.
Research areas
Matthias Heinkenschloss’ research interests are in the design and analysis of mathematical optimization algorithms for nonlinear, large-scale (often infinite dimensional) problems and their applications to science and engineering problems. Research areas include large-scale nonlinear optimization, model order reduction, optimal control of partial differential equations (PDEs), optimization under uncertainty, PDE constrained optimization, iterative solution of KKT systems and domain decomposition in optimization.