Quadratization of ODEs: Monomial vs. Non-Monomial
share
Quadratization is a transform of a system of ODEs with polynomial right-hand side into a system of ODEs with at most quadratic right-hand side via the introduction of new variables. It has been recently used as a pre-processing step for new model order reduction methods, so it is important to keep the number of new variables small. Several algorithms have been designed to search for a quadratization with the new variables being monomials in the original variables. To understand the limitations and potential ways of improving such algorithms, we study the following question: can quadratizations with not necessarily monomial new variables produce a model of substantially smaller dimension than quadratization with only monomial new variables? To do this, we restrict our attention to scalar polynomial ODEs. Our first result is that a scalar polynomial ODE ẋ=p(x)=a_nx^n+a_n-1x^n-1+… + a_0 with n⩾ 5 and a_n≠0 can be quadratized using exactly one new variable if and only if p(x-a_n-1/n· a_n)=a_nx^n+ax^2+bx for some a, b ∈ℂ. In fact, the new variable can be taken z:=(x-a_n-1/n· a_n)^n-1. Our second result is that two non-monomial new variables are enough to quadratize all degree 6 scalar polynomial ODEs. Based on these results, we observe that a quadratization with not necessarily monomial new variables can be much smaller than a monomial quadratization even for scalar ODEs. The main results of the paper have been discovered using computational methods of applied nonlinear algebra (Gröbner bases), and we describe these computations.
READ FULL TEXT