In certain situations, Maxima can actually solve a sextic equation.

For readers who don’t know, a sextic equation is a polynomial of the 6th degree. As the subject line suggests, recent versions of Maxima can find symbolic solutions to those, if used correctly, and, if the sextic actually has ‘an exact, analytical solution’, which is also referred to sometimes as ‘a symbolic solution’.

Whether these analytical solutions are actually more useful than numeric approximations, remains an unanswered question.

What has happened to me is, that I’ve tried to use the method shown below, to cause Maxima to display the solution, and that due to what amounted to a typo, I had given it a polynomial which was visually similar to the one shown, but which was also different in some small way, so that the only solution which Maxima displayed, was the original polynomial, thus implying that Maxima was not able to solve an altered one. The reason this happened is easy to explain…

Not all polynomials of the 6th degree actually have an analytical solution. If given an example that does not, Maxima will fail to display one. All polynomials of the 4th degree actually have an analytical solution, but it may easily be too complex for consumer-grade Computer Algebra Systems (CAS) to output. But, by the time the user is asking a CAS to solve a cubic, he should be able to expect this form of a solution to be output.

The sextic below is actually the product of two cubics, which also explains why Maxima was able to solve it. The reader will need to enable JavaScript:

  • From my site, And
  • From MathJax.org,

To be able to view the worksheet:

 


 

(Updated 7/04/2020, 13h30… )


Continue reading In certain situations, Maxima can actually solve a sextic equation.

Revisiting the subject of approximating roots of polynomials.

In an earlier posting, I had written about an approach, for how to find approximations of the roots of polynomials, of an arbitrary degree, but in such away, also to find all the complex roots. (:1)

But with such strategies, there are issues. One concept was, that a home-grown search algorithm would get close to the actual root. Next, polishing would make the result more accurate. And then, an augmented division would be computed, which is also referred to as “Deflation”, resulting in a deflated polynomial, as many times as the original polynomial’s degree, minus one.

Pondering this issue today, I realized that there was still a conceptual weakness in that approach, that being, the fact that some small amount of error is tolerated in the first root found, so that each successive deflated polynomial contains progressively greater degrees of error. What effectively happens next is, that accurate roots are found, of increasingly inaccurate polynomials, and, that there appeared to be few ways to detect and correct the resulting errors, in roots found afterwards. Theoretically, this problem could progress to the point, where doubt is evoked, in whether or not roots found later, were even roots of the original polynomial, since by that time, the object which the roots are being found of, is no longer that original polynomial.

(Update 6/08/2020, 18h35… )

Continue reading Revisiting the subject of approximating roots of polynomials.