Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis.

ABSTRACT:

Motivation

Parameter estimation methods for ordinary differential equation (ODE) models of biological processes can exploit gradients and Hessians of objective functions to achieve convergence and computational efficiency. However, the computational complexity of established methods to evaluate the Hessian scales linearly with the number of state variables and quadratically with the number of parameters. This limits their application to low-dimensional problems.

Results

We introduce second order adjoint sensitivity analysis for the computation of Hessians and a hybrid optimization-integration-based approach for profile likelihood computation. Second order adjoint sensitivity analysis scales linearly with the number of parameters and state variables. The Hessians are effectiv

SUBMITTER: Stapor P 

PROVIDER: S-EPMC6022646 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature