A continuation method for the efficient solution of parametric optimization problems in kinetic model reduction
It addresses the computational bottleneck of identifying slow invariant manifolds in dynamical systems for practitioners in kinetic modeling.
The paper presents a predictor-corrector method for efficiently solving parametric optimization problems in kinetic model reduction, demonstrating the benefit of a step size strategy on an example.
Model reduction methods often aim at an identification of slow invariant manifolds in the state space of dynamical systems modeled by ordinary differential equations. We present a predictor corrector method for a fast solution of an optimization problem the solution of which is supposed to approximate points on slow invariant manifolds. The corrector method is either an interior point method or a generalized Gauss--Newton method. The predictor is an Euler prediction based on the parameter sensitivities of the optimization problem. The benefit of a step size strategy in the predictor corrector scheme is shown for an example.