A fast python function for computing roots of a quartic equation (4th order polynomial) and a cubic equation (3rd order polynomial) in tensorflow. Works properly even when the polynomial coefficients are complex.
The original numpy version is at NKrvavica/fqs
- The function is optimized for computing single or multiple roots of 3rd and 4th order polynomials (cubic and quartic equations).
- A closed-form analytical solutions are used for roots of cubic and quartic equations.
- Implemented in tensorflow, supports solving many polynomials in parallel.
Python 3+, tensorflow
All functions are found in fqs.py, which can be cloned to local folder.
See test_fqs.py for unit tests and example usage.
Why not simply use
numpy.rootsornumpy.linalg.eigvalsfor all polynomials?
For single polynomial, both quartic and cubic solvers are one order of magnitude faster than numpy.roots and numpy.linalg.eigvals.
For large number of polynomials (>1_0000), both quartic and cubic solver are one order of magnitude faster than numpy.linalg.eigvals and two order of magnitude faster than numpy.roots inside a list comprehension.
Why did you create this fork?
To support root finding within a tensorflow graph.