Fast Numerical Solvers for Parameter Identification Problems in Mathematical Biology
Abstract
In this paper, we consider effective discretization strategies and iterative solvers for nonlinear PDE-constrained optimization models of pattern evolution within biological processes. Upon a Sequential Quadratic Programming linearization of the optimization problem, we devise appropriate time-stepping schemes and discrete approximations of the cost functionals such that the discretization and optimization operations are commutative, a highly desirable property of a discretization of such problems. We formulate the large-scale, coupled linear systems in such a way that efficient preconditioned iterative methods can be applied within a Krylov subspace solver. Numerical experiments demonstrate the viability and efficiency of our approach.
Fields of Study & Subjects
Gene Regulatory Network Analysis
Mathematical Biology Tumor Growth
Publication details
- Journal / source
- Journal of Scientific Computing
- Publisher
- Springer Science and Business Media LLC
- Volume
- 107
- Issue
- 1
- Pages
- 24–24
- Publication date
- 16 March 2026
- Language
- EN