Improved Structural Methods for Nonlinear Differential-Algebraic Equations via Combinatorial Relaxation

TL;DR

This paper introduces two novel combinatorial relaxation-based modification methods for nonlinear DAEs, enabling more effective preprocessing for complex systems that traditional structural methods cannot handle, demonstrated through successful numerical experiments.

Contribution

The paper proposes substitution and augmentation methods for nonlinear DAEs, extending combinatorial relaxation techniques to a broader class of equations.

Findings

Both methods effectively modify high-index DAEs.

The augmentation method retains sparsity and avoids solving equations.

Numerical experiments show improved handling of challenging DAEs.

Abstract

Differential-algebraic equations (DAEs) are widely used for modeling of dynamical systems. In numerical analysis of DAEs, consistent initialization and index reduction are important preprocessing prior to numerical integration. Existing DAE solvers commonly adopt structural preprocessing methods based on combinatorial optimization. Unfortunately, the structural methods fail if the DAE has numerical or symbolic cancellations. For such DAEs, methods have been proposed to modify them to other DAEs to which the structural methods are applicable, based on the combinatorial relaxation technique. Existing modification methods, however, work only for a class of DAEs that are linear or close to linear. This paper presents two new modification methods for nonlinear DAEs: the substitution method and the augmentation method. Both methods are based on the combinatorial relaxation approach and are applicable to a large class of nonlinear DAEs. The substitution method symbolically solves equations for some derivatives based on the implicit function theorem and substitutes the solution back into the system. Instead of solving equations, the augmentation method modifies DAEs by appending new variables and equations. The augmentation method has advantages that the equation solving is not needed and the sparsity of DAEs is retained. It is shown in numerical experiments that both methods, especially the augmentation method, successfully modify high-index DAEs that the DAE solver in MATLAB cannot handle.