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Budan's and Vincent's theorems can be used to isolate the real roots of a real polynomial. I have read papers which compared it favorably to other root finding methods.

However, roots can also be isolated by converting the polynomial into Bernstein basis and splitting the curve by the means of De Casteljau's algorithm. Both the conversion and the splitting is very easy so I was wondering, if someone could please compare these two approaches.

What are the disadvantages of using the Bernstein approach compared to the one of Budan? I mean, from the looks of it, I would think one would favor the Bezier curve approach because of its simplicity so I would like to understand why are the former methods apparently so strongly favored? Perhaps does someone know of a paper which does such comparison?

As far as I know the splitting of Bezier curve takes $O(n^2)$ operations, whereas the Taylor shifts needed be VCA et al. can be accelerated from the naive $O(n^2)$ to $O(n log n)$. On the other hand, the splitting is said to be very stable..?

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  • $\begingroup$ I don't know much about computational real algebraic geometry but I was under the impression that Sturm’s theorem is more popular than the approaches you mention. $\endgroup$ – Sergio Parreiras Jul 27 '15 at 18:04
  • $\begingroup$ Sturm's method in some sense tackles a harder problem - it ignores multiplicities of the roots. This requires much more involved algebraic techniques. In the past years, the focus shifted towards the algorithms mentioned by Ecir. Sturm's method is still somewhat popular as a theoretical tool, but for practical applications, I am not aware of any implementation that is on par with rule-of-signs-based solvers. This holds even more if approximate (floating point) arithmetic is used. $\endgroup$ – akobel Jul 28 '15 at 9:34
  • $\begingroup$ Btw., "Polynomial Real Root Finding in Bernstein Form" dissertation by Melvin R. Spencer says that Sturm-based solver is several times slower than Bernstein-based one. $\endgroup$ – Ecir Hana Jul 28 '15 at 9:38
  • $\begingroup$ Be careful not to compare apples and oranges. There is the choice between different bases (monomial, Bernstein, ... - maybe Chebyshev for a change?), the choice between algorithms (Sturm, VCA, ...), the choice between computation paradigms (exact arithmetic, floating point, interval arithmetic in different ways, ...). Just because one combination works well, that does not mean that a specific "ingredient" is superior to all its siblings in every situation. And AFAICS from a quick glance, Spencer compared actual implementations - that's important, but doesn't yield universal truths. $\endgroup$ – akobel Jul 28 '15 at 12:01
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The Bernstein representation is great to give an intuition how and why the algorithms based on Descartes' rule of signs work: essentially, the signs of the coefficients of the "localized" polynomial which are computed in the classical VCA method show whether the corresponding vertices of the Bézier control polygon are located above or below the $x$-axis. If there is no sign change, all control points lie in either the positive or the negative halfplane, and by the convex hull property of Bézier curves, the polynomial cannot have a root. If there is only one sign change, there has to be a root (because the polynomial curve will hit the endpoints of the control polygon), and if you work out the details, you will see that there can be only one root.
On the other hand, the sign sequences in the Bernstein and the VCA approaches are identical; in fact, all coefficients of the polynomials considered are the same up to a scaling by an (obviously positive) binomial coefficient.

I have yet to find a conclusive proof why the Bernstein approach should be beneficial for general polynomials. It is true that the de Casteljau scheme is very stable, but this ignores the initial transformation to the Bernstein basis. On the other hand, a Taylor shift with a Horner-like scheme is very stable as well, and

for j from n-1 downto 0: for i from j to n-1: p[i] += p[i+1]

(the code for a "naive" Taylor shift of a polynomial $p = \sum_{i=0}^n p_i x^i$ by $1$, using the Ruffini-Horner scheme) looks "suspiciously" close to the de Casteljau scheme that I'm not convinced that the latter should be inherently more stable.

Furthermore, as far as I know, Fabrice Rouillier's empirical analysis of both approaches using his RS framework showed no significant differences. You should have a look into "Efficient Isolation of a Polynomial’s Real Roots" by Rouillier and Zimmermann, and also "Bernstein’s basis and real root isolation" by Mourrain, Rouillier and Roy. I could not quickly dig out real-life benchmark of identically sophisticated implementations of both approaches, though.
Note: Rouillier's RS library (also the default in Maple) is widely considered the state-of-the-art benchmark for real root isolation, and it does not use the Bernstein basis by default (although there is an implementation of it). Carl Witty's implementation of real_roots in SAGE is almost on par, and it does use Bernstein and de Casteljau. I leave the interpretation up to you.

I can only guess, but I assume that the somewhat bad reputation of the classical approach stems from the fact that implementations using it were traditionally designed in the computer algebra community rather than by numerical analysist, and thus tuned towards exact arithmetic. I have no doubt that the de Casteljau scheme yields more accurate results in machine precision than any fast Taylor shift algorithm. But I am also convinced that one can make the de Casteljau algorithm asymptotically fast (trivially: by conversion to the monomial basis, using a fast Taylor shift, and converting back; but I guess there is work on a "native" fast version if you search hard enough). I assume that its stability will be almost identical to the Taylor shift version.

My own approach would be: don't bother to convert something to Bernstein basis which is given in monomial basis, and don't bother to convert something to monomial basis which is given in Bernstein basis. Unless you measure and find a very convincing reason to do it.
If you find that you can solve an instance using one approach, but not the other, first see whether a mildly more complex function will still work before you claim one method to be the winner. Try to scale all coefficients by a common factor such that their magnitude is as close to 1 as possible; this is where floating point calculations tend to behave the best. And see whether using something like an xdouble (from Shoup's NTL) or a qd (from Bailey's high-precision packages) will remedy your issues.

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  • $\begingroup$ Good answer. Papers by Farouki and Rajan explain that conversion from Bernstein basis to the Taylor (power basis) is a bad idea, and that one should work entirely in the Bernstein basis, for stability reasons. $\endgroup$ – bubba Jul 28 '15 at 11:19

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