Toward the best algorithm for approximate GCD of univariate polynomials

Approximate polynomial GCD (greatest common divisor) of polynomials with a priori errors on their coefficients, is one of interesting problems in Symbolic-Numeric Computations. In fact, there are many known algorithms: QRGCD, UVGCD, STLN based methods, Fastgcd and so on. The fundamental question of this paper is "which is the best?" from the practical point of view, and subsequently "is there any better way?" by any small extension, any effect by pivoting, and any combination of sub-routines along the algorithms. In this paper, we consider a framework that covers those algorithms and their sub-routines, and makes their sub-routines being interchangeable between the algorithms (i.e. disassembling the algorithms and reassembling their parts). By this framework along with/without small new extensions and a newly adapted refinement sub-routine, we have done many performance tests and found the current answer. In summary, 1) UVGCD is the best way to get smaller tolerance, 2) modified Fastgcd is better for GCD that has one or more clusters of zeros with large multiplicity, and 3) modified ExQRGCD is better for GCD that has no cluster of zeros. (C) 2020 Elsevier Ltd. All rights reserved.