Structural Preprocessing Method for Nonlinear Differential-Algebraic Equations Using Linear Symbolic Matrices

Differential-algebraic equations (DAEs) have been used in modeling various dynamical systems in science and engineering. There are several preprocessing methods that are needed before performing numerical simulations for DAEs, such as consistent initialization and index reduction. Preprocessing methods that use structural information on DAEs run fast and are widely used. Unfortunately, structural preprocessing methods may fail when the system Jacobian, which is a functional matrix, derived from the DAE is singular. To transform a DAE with a singular system Jacobian into a nonsingular system, several structural regularization methods have been proposed. Most of all existing structural regularization methods rely on symbolic computation to eliminate the system Jacobian for finding a certificate of singularity, resulting in much computational time. Iwata-Oki-Takamatsu (2019) proposed a method (IOT-method) to find a certificate without symbolic computations. The IOT method approximates the system Jacobian by a simpler symbolic matrix, called a layered mixed matrix, which admits a fast combinatorial algorithm for singularity testing. Although the IOT method runs fast, it often overlooks the singularity of the system Jacobian since the approximation largely discards algebraic relationships among entries in the original system Jacobian. In this study, we propose a new structural regularization method extending the idea of the IOT method. Instead of layered mixed matrices, our method approximates the system Jacobian by more expressive symbolic matrices, called rank-1 coefficient mixed (1CM) matrices. This makes our method more widely applicable than the existing method. We give a fast combinatorial algorithm for finding a singularity certificate of 1CM-matrices, making our structural regularization method free from symbolic elimination. Our method is also advantageous in that it globally preserves the solution set to the DAE. Through numerical experiments, we confirmed that our method runs fast for large-scale DAEs from real instances.