Computing GCRDs of Approximate Differential Polynomials
This work provides a novel algebraic tool for approximate differential operators, benefiting researchers in symbolic computation and differential equations.
The paper introduces approximate differential polynomials and presents an algorithm for computing their approximate Greatest Common Right Divisor (GCRD) by finding nearby polynomials with a non-trivial GCRD, generalizing the SVD-based method for regular polynomials. The algorithm is implemented in Maple and demonstrates robustness.
Differential (Ore) type polynomials with approximate polynomial coefficients are introduced. These provide a useful representation of approximate differential operators with a strong algebraic structure, which has been used successfully in the exact, symbolic, setting. We then present an algorithm for the approximate Greatest Common Right Divisor (GCRD) of two approximate differential polynomials, which intuitively is the differential operator whose solutions are those common to the two inputs operators. More formally, given approximate differential polynomials $f$ and $g$, we show how to find "nearby" polynomials $\widetilde f$ and $\widetilde g$ which have a non-trivial GCRD. Here "nearby" is under a suitably defined norm. The algorithm is a generalization of the SVD-based method of Corless et al. (1995) for the approximate GCD of regular polynomials. We work on an appropriately "linearized" differential Sylvester matrix, to which we apply a block SVD. The algorithm has been implemented in Maple and a demonstration of its robustness is presented.