A better way to use “Maxima” to solve cubic equations…

One of the facts which I’ve pointed out in earlier postings was, that if I give the free Computer Algebra System (‘CAS’) named ‘Maxima’, a cubic equation to solve, as in, to find the exact analytical solutions to, it will fail to produce intelligible output, if the usage was naive.

More specifically, cubic equations exist that have 3 distinct, real, irrational roots, and which a ‘CAS’ should be able to solve, just because their general solution is publicly known. That solution boils down, to deriving a second cubic, which is called a ‘depressed cubic equation’, and then performing a trigonometric substitution. (:1)

A fact which I’ve also known for some time is that, especially if a person is using a free or open-source CAS, then in some cases its behaviour has not been made particularly user-friendly, in that work needs to be done by the user, to set up his or her problem for the ‘CAS’ to solve. This latter observation casts a shadow of doubt, over the question of whether a ‘CAS’ will ultimately lead experienced Mathematicians to new discoveries in Algebra, or whether this can only reduce the workload in certain situations.

In this posting I’m going to show, how ‘Maxima’ can be coerced into giving correct answers, by users who know how. What I tend to use is a Graphical Front-End to ‘Maxima’, that is itself named ‘wxMaxima’, but which has equal capabilities, except for the abilities to typeset its solutions, as well as to export its Worksheets to PDF as well as HTML format, using LaTeX.

The following embedded worksheet will only display properly in the reader’s browser, if

  • The reader has allowed JavaScript from my blog to run on his browser, and
  • The reader has also allowed JavaScript to run from a CDN named ‘mathjax.org’.

 

 


What’s observable here is the fact that the package ‘odes’ can be loaded, which is mainly used to solve Ordinary Differential Equations, and that afterwards, the function ‘solvet()’ can be used, even to solve certain polynomials – better than what Maxima can solve on its own, with the built-in ‘solve()’ function. (:3)

(Updated 6/14/2020, 0h30… )

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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… )

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How the general solution of polynomials, of any degree greater than 2, is extremely difficult to compute.

There are certain misconceptions that exist about Math, and one of them could be, that if a random system of equations is written down, the Algebraic solution of those equations is at hand. In fact, equations can arise easily, which are known to have numerical answers, but for which the exact, Algebraic (= analytical) answer, is nowhere in sight. And one example where this happens, is with polynomials ‘of a high degree’ . We are taught what the general solution to the quadratic is in High-School. But what I learned was, that the general solution to a cubic is extremely difficult to comprehend, while that of a 4th-degree polynomial – a quartic –  is too large to be printed out. These last two have in fact been computed, but not on consumer-grade hardware or software.

In fact, I was taught that for degrees of polynomials greater than 4, there is no general solution.

This seems to fly in the face of two known facts:

  1. Some of those equations have the full number of real roots,
  2. Computers have made finding numerical solutions relatively straightforward.

But the second fact is really only a testament, to how computers can perform numerical approximations, as their main contribution to Math and Engineering. In the case of polynomials, the approach used is to find at least one root – closely enough, and then, to use long division or synthetic division, to divide by (1 minus that root), to arrive at a new polynomial, which has been reduced in degree by 1. This is because (1 minus a root) must be a factor of the original polynomial.

Once the polynomial has arrived at a quadratic, computers will eagerly apply its general solution to what remains, thereby perhaps also generating 2 complex roots.

In the case of a cubic, a trick which people use, is first to normalize the cubic, so that the coefficient of its first term is 1. Then, the 4th, constant term is examined. Any one of its factors, or the factors of the terms it is a product of, positively or negatively, could be a root of the cubic. And if one of them works, the equation has been cracked.

In other words, if this constant term is the product of square roots of integers, the corresponding products of the square roots, of the factors of these integers, could lead to roots of the cubic.

(Updated 1/12/2019, 21h05 … )

Continue reading How the general solution of polynomials, of any degree greater than 2, is extremely difficult to compute.