Optimality of Curtiss Bound on
Poincaré Multiplier for
Positive Univariate Polynomials
Hoon Hong 111
Department of Mathematics,
North Carolina State University,
Raleigh NC 27695 USA,
[email protected]
and
Brittany Riggs 222
Corresponding author.
Department of Mathematics,
Elon University,
Elon NC 27244 USA,
[email protected]
Abstract
Let be a monic univariate polynomial with non-zero constant term. We say
that is positive if is positive over all . If all
the coefficients of are non-negative, then is trivially positive. In
1883, Poincaré proved that is positive if and only if there exists a monic
polynomial such that all the coefficients of are non-negative. Such
polynomial is called a Poincaré multiplier for the positive
polynomial . Of course one hopes to find a multiplier with smallest degree.
This naturally raised a challenge: find an upper bound on the smallest degree
of multipliers. In 1918, Curtiss provided such a bound. Curtiss also showed
that the bound is optimal (smallest) when degree of is 1 or 2. It is easy
to show that the bound is not optimal when degree of is higher. The
Curtiss bound is a simple expression that depends only on the angle (argument)
of non-real roots of . In this paper, we show that the Curtiss bound is
optimal among all the bounds that depends only on the angles.
1 Introduction
Let be a monic univariate polynomial with non-zero constant term. We say
that is positive if is positive over
all . 333
It is equivalent to the condition that has no positive real root.
In fact, most of the discussion in the paper has a counter-part in a real root counting
problem, namely checking whether has exactly positive real roots for a given . There have been many works on the topic and on its extensions, just to list a few: [7, 4, 9, 10, 21, 22, 5, 8].
If all
the coefficients of are non-negative, then obviously is positive, but the converse is not true. Consider the following small (toy) examples:
Note that all the coefficients of are non-negative. Thus
it is trivial to see that is positive. However and
have negative coefficients, and thus it is not obvious whether they are
positive or not. It turns out that is not positive and is positive.
In 1883, Poincaré [15] proposed and proved a modification of the converse: if is positive, then there exists a monic
polynomial such that all the coefficients of are non-negative. Such
polynomial is called a Poincaré multiplier for the positive
polynomial . For the above examples, we have
•
Since is not positive, there is no Poincaré multiplier for
.
•
Since is positive, there is a Poincaré multiplier for
. For instance, let . Then
Note that all the coefficients of are non-negative.
In 1928, Pólya provided a shape for the Poincaré multiplier [16], namely . 444
Pólya used the multivariate version
of the shape while trying to find a certificate for a variant of Hilbert’s 17th problem [11]. For similar efforts on different variants of Hilbert’s 17th problem,
see [1, 12, 19, 20, 18, 14].
In fact, in the above example, we used a multiplier of the Pólya shape with . However, there are other multipliers for
that do not have the Pólya shape. For instance, let . Note that it does not have the Pólya shape, but it is a Poincaré multiplier since all the coefficients of are again non-negative:
In fact, there are infinitely many multipliers of diverse shapes.
Hence a challenge naturally arises: Find an upper bound on the
smallest degree of multipliers.
In 1918, Curtiss found an upper bound [6]. However, it seems to have been forgotten until the paper was digitized. Separately, in 2001 Powers and Reznick gave another bound based on the Pólya shape in the multivariate homogeneous case [17]. Avendaño (page 2885 of [2]) specialized this bound to the univariate case.
It is easy to show that the Powers-Reznick bound is not optimal. Curtiss showed that his bound is optimal when degree of is
or . It is, however, easy to show that the bound is not optimal when degree of is
higher. For example, the Curtiss bound for is , which is bigger
than the optimal degree which is . Hence another challenge naturally arises: Find the optimal (smallest) bound.
This can be done, in principle, by the following process: for each degree , starting
with , we check whether there exists a Poincaré multiplier of degree . If not, we continue with next degree. If yes, we report to be the optimal bound. The process is guaranteed to terminate due to Poincaré’s theorem.
Checking the existence of a Poincaré multiplier of a given degree can be
carried out by any algorithm (such as simplex phase I) for deciding the existence of a solution for a system of linear inequalities in the coefficients of the multiplier polynomial.
However, it has two major difficulties. (1) It
requires that we run the software on each from scratch.
(2) Each run on degree quickly becomes very time-consuming as grows, making this approach practically infeasible, especially when the optimal bound is very large.
Fortunately, there is a related challenge which might be feasible to tackle. For this, we note the Curtiss bound (Theorem 1) depends only on the angles
(arguments) of the non-real roots of . Thus, we formulate the following alternative challenge: Find an optimal bound among the family of bounds that depend only on the angles of the non-real roots.
Note that Curtiss already showed that his bound is optimal among the family of those bounds for degrees 1 and 2. One wonders whether the Curtiss bound is optimal among this family of bounds for higher degrees. The main contribution of this paper is
to prove that it is so (Theorem 2).
2 Main Result
Let be monic such that , that is, does not have any non-negative
real root. We first define a notation for the optimal degree of the Poincaré multiplier as it will be used throughout this work.
Definition 1 (Optimal Bound)
The optimal degree of the Poincaré multiplier for , written as , is defined by
Example 2
Consider the polynomial
where .
First, note that , as has mixed sign coefficients. If we let the Poincaré multiplier be , we see we have , since .
As the proof is rather lengthy, we will include an overview of the structure of the proof. First, we provide a diagram of the various components of the proof. As shown in the diagram, the main claim is split into two sub-claims, which are then proven via several smaller claims. Below, we explain the diagram from a big picture standpoint. In the sections referenced in the diagram, we provide the proofs for each claim.
Our goal is to prove the angle-based optimality of Curtiss’s bound on the degree of the Poincaré multiplier for a positive polynomial.
Symbolically, we have
After extensive exploration, we identified a natural split in strategies between collections of angles and those with additional angles . We now have
We first focus on the claim for , polynomials with complex root arguments . That is, for any collection of angles in this subset, there exists a corresponding collection of radii such that Curtiss’s bound is the minimum possible degree for the Poincaré multiplier to achieve a product with all non-negative coefficients. One way to show this is to prove that there exist radii such that any multiplier of smaller degree will produce a product with a negative coefficient. We can then rewrite the claim as
Note, with this rewrite we consider a problem with three quantifier alternations. As this is a very complex problem, we seek to reduce the search space for the negative coefficient. It is straightforward to prove that, rather than consider the entire set of multipliers of smaller degree, it is sufficient to focus on the set of multipliers with degree one less than Curtiss’s bound to show that any such multiplier will produce a product with a negative coefficient. We now have
for a subset of the original multiplier search space that is determined by the Curtiss bound (hence, by ). After detailed analysis of examples, we surprisingly discovered that we can also narrow down the potential locations of the negative coefficient. In fact, although the collection of product coefficients grows in size with Curtiss’s bound, we can identify a negative coefficient from a subset of just 3 possibilities. Hence
for a subset of the original coefficient search space that is determined by the number of angles in and the Curtiss bound (hence, by ). Finally, with more difficulty, we determined how to reduce the search space for the desired radii. By identifying a witness, in terms of , for all but one of the radii, we reduced the radii search space to just a one-dimensional space for the final radius. We have
for a subset of the original radii search space . Proving this final version of the claim was still a significant challenge. We examined the geometry of the product coefficients as a function of this final unknown radius in order to verify the claim. Expressing the coefficients of the original polynomial in terms of the elementary symmetric polynomials in the roots of proved essential to our progress, enabling us to make claims about the sign of the coefficients that were otherwise quite challenging.
Having proved the claim in the case where all angles fall in , we move on to the case where we have mixed angles from or . The crux of this claim is that any polynomial in which Curtiss’s bound is optimal can be multiplied by a quadratic with angle and a radius that preserves the optimality of Curtiss’s bound or a linear factor with an appropriate radius that preserves the optimality of Curtiss’s bound. Thus, for any collection of additional angles in quadrant 2, we can inductively prove the existence of the necessary radii to ensure the optimality of Curtiss’s bound.
Note that, in this case, the additional factors in the polynomial do not increase the Curtiss bound. For this sub-claim we utilize a different strategy, relying on linear algebra to rewrite the optimality claim as a separation between two sets.
We need to find a witness for such that
.
We propose a witness candidate as follows.
From the All Angles in Quadrant 1 Lemma (Lemma 4), for the fixed , we have
(1)
From the Some Angles in Quadrant 2 Lemma (Lemma 11), for the fixed and , we have
(2)
We propose appearing in the above two facts as a
witness candidate.
We verify that the proposed candidate is indeed a witness, that is,
.
Note
Hence the theorem is proved.
3.3 Common Notations
The following notation and lemma will be used in both the subsequent subsections. Let
where and . We first rewrite them using vectors. Let
and let
Then we can write and compactly as
Let
Lemma 3 (Coefficients)
Proof:
Note
Hence .
3.4 Concerning Angles in Quadrant 1,
First, we present the overarching argument for the claim that, given a collection of angles in quadrant 1 (excluding the axes), there exist radii such that the Curtiss bound is optimal. There are a number of claims required for this proof.
We will utilize the following notations throughout this subsection. For , let
Note that where is
the elementary symmetric polynomial of degree in the roots . When , we define . Second, since .
Lemma 4 (All Angles in Quadrant 1)
Proof:
We need to prove the following claim for every .
where .
By the One Less Degree Lemma (Lemma 5), it suffices to show
We will show it in three cases depending on the values of .
We prove this final claim to be true in the Existence of Radii Lemma, Lemma 10.
Now we prove Lemmas 5 and 6, cited above, and also several other lemmas required for the main claim, the Existence of Radii Lemma (Lemma 10).
First, we prove the small but essential claim that if there is no Poincaré multiplier, , of a certain degree, then there is also no Poincaré multiplier with smaller degree.
Lemma 5 (One Less Degree)
Proof:
We will prove via the contrapositive:
Assume . Then there exists a with such that
has all non-negative coefficients. Let .
Consider the multiplier . Note that
must have all non-negative coefficients and . Then there exists a multiplier, with degree equal to
such that the product has all non-negative coefficients.
Hence we have
Our ultimate goal for this section is to prove that there exist radii such that there is no multiplier with degree less than . The One Less Degree Lemma (Lemma 5) states that it suffices to show there is no multiplier with degree .
Next, we show that if we consider a multiplier of degree one less, it suffices to examine a specific subset of the coefficients in the product rather than all coefficients to prove the claim. Note that the surprisingly small subset of coefficients was selected by examining the geometry of the coefficients and determining a minimally sufficient set. This is the proof that a subset will suffice. The proof that this particular subset satisfies the claim is given in Lemma 10.
Lemma 6 (3 Coefficients)
Proof:
Recall that
Note that if and , then .
The next two lemmas are helpful in the proof of Lemma 10 and are listed separately here to streamline the proof of Lemma 10.
Lemma 7 (Coefficient Expressions)
We have
where
Proof: Recall from the Coefficients Lemma (Lemma 3) for and ,
Then
Hence,
Then we have
The following lemma verifies a property of elementary symmetric polynomials in the roots that is required for the proof of the Existence of Radii Lemma (Lemma 10).
Lemma 8 (Symmetric Polynomials)
If , then for .
Proof:
We will induct on , the number of quadratic factors of with non-real roots.
Base Case:
.
Note that .
Hypothesis:
.
Assume for and .
Induction Step:
Prove for .
Let and .
Note that
Then
Hence, by dividing the above by , we have
by the inductive hypothesis and the fact that .
The next lemma provides witnesses for a claim that is utilized in the proof of the Existence of Radii Lemma (Lemma 10). It is stated separately to preserve the intuitive flow of the proof of the Existence of Radii Lemma.
Lemma 9 (Partial Witness)
where
Proof: Note
Then
Consider the following witness candidates for the existentially
quantified variables :
Obviously, . Note
Hence the candidates are witnesses.
Note that the candidates provided above were discovered via guess-and-check in an attempt to manipulate the expression into a fraction of the form where the stated property would be clear.
Finally, we provide the Existence of Radii Lemma, the core argument supporting the All Angles in Quadrant 1 Lemma (Lemma 4). One of the biggest obstacles was in determining which coefficients to examine to prove the claim. You will notice that different coefficients are required based on the number of angles in this quadrant and where they lie. For example, the coefficients needed to prove the claim when there are two angles are different from the coefficients needed when and .
Lemma 10 (Existence of Radii)
Proof: Note
since the leading coefficient of is assumed to be 1.
Let and be such that . Let .
Claim 1:
When , .
Note that only when and . Note
We have
Note . Note
Note
since
Hence . The claim has been proved.
Example for Claim 1:The following two graphs demonstrate Claim 1 for the degree 4 polynomial
where . Note that and , as .
When both radii are equal to 1, there are potential values of that do not produce a negative coefficient. When is increased to 10, at least one coefficient is negative for any .
For the remainder of the proof, let . Let be such that is true, where
The existence of such radii is justified based on the value of .
1.
In the case where and ,
Since , we must have and . Hence, .
2.
In the case where , we know such radii exist from Lemma 9.
Using the coefficient expressions from Lemma 7, note
Let be arbitrary but fixed.
Let be the line given by in space. Let be the slope of
. Then
Since , we have
Then
Claim 2:
.
Note as
Then
Then for sufficiently large ,
which is true for the we have previously chosen based on the claim from Lemma 9. Recall that the elementary symmetric polynomials in these roots are always positive by Lemma 8. The claim has been proved.
Continuing with the proof of the lemma, let be such that . Such
exists due to the previous claim. Let
be arbitrary but fixed such that . Then over the space , there exists a unique
intersection point of and . Let be the intersection point.
Example of :In this example, , so , , and is the intersection point of with .
We will continue using this example to illustrate the remainder of the proof.
Continuing with the proof of the lemma, as shown below, we have and
where and . Note
Claim 3:
.
Let be such that . In Claim 2, we showed that such exists. Let be arbitrary but fixed.
Let be arbitrary but fixed. We need to show
.
Over the space where , we have
1.
line and line do not intersect
2.
line is above line.
Let be an arbitrary but fixed point
such that . Then is above
or below . Note
since and .
Thus or .
The claim has been proved.
Example of Claim 3:Again, , so ,
, and is the intersection point of with .
Example of Claim 4:Once more, , so , , and is the intersection point of with .
Notice that when , there is a region of points with where is not negative. However, when , for all points where .
We continue with the proof of the lemma.
From Claim 1, when , for some we have
.
From Claim 3, when , for some we have
From Claim 4, when , for some we have
.
Then for , we have
Hence
The lemma has finally been proved.
3.5 Concerning Angles in Quadrant 2,
Finally, we present the proof for the case when some angles fall in quadrant 2, including the axes. We convert the optimality claim into a statement on the separation of two sets in Lemma 12. Then we prove that for any polynomial for which Curtiss’ bound is optimal, there is a radius such that we can multiply by a linear or quadratic factor with angle and this radius while maintaining the optimality of the bound. Inductively, we can extend this argument to any collection of angles in quadrant 2.
The main challenge of this section was in the One New Quadrant 2 Factor Lemma (Lemma 13). To prove a separation between the two necessary sets, we examined the Euclidean distance between them and proved it must be greater than zero, a surprisingly non-trivial task.
Lemma 11 (Some Angles in Quadrant 2)
We have
Proof:
We will first induct on , the number of quadratic factors with in , to show that
.
Base Case:
The claim holds for .
It is trivially true.
Hypothesis:
Assume the claim holds for quadratic factors with .
Induction Step:
Consider quadratic factors with .
Assume are
such that the induction hypothesis holds. By the One More Quadrant 2 Factor Lemma (Lemma 13),
The above proof relies on the One More Quadrant 2 Factor Lemma (Lemma 13). To prove this lemma, we will build additional notation and convert the optimality claim to a claim about separation of sets.
Recall that
and, from the Coefficients Lemma (Lemma 3), . We partition into two sub-matrices as where
Let
Lemma 12 (Separation of Sets)
We have
where is viewed as a set of row vectors.
Proof:
Note that
Thus we have
Notation 5
Note
•
when
•
when
•
Lemma 13 (One New Quadrant 2 Factor)
Proof:
Let and . We need to show since . We will divide the proof into several claims.
Claim 1:
where
•
•
•
•
The are the rows of for .
•
is obtained from by deleting the first elements.
Note
Claim 2:
where stands for the minimum Euclidean
distance betweenand , that is,
Immediate from the above claim.
Claim 3:
is continuous at .
Note
where is Euclidean distance and
Note, since is the distance between two closed sets, the
minimum distance is realized by a point in each set. Hence,
Note the following:
1.
By Section 3.1.5 of [3], is a convex function since
is a norm.
2.
By Section 3.2.5 of [3], is a convex function, since is convex and
is bounded below.
3.
By Corollary 3.5.3 in [13], since is a
convex function defined on a convex set , is
continuous on the relative interior of , .
4.
Hence, is continuous at .
Claim 4:
.
The claim follows immediately from the following subclaims.
Let . Then .
Subclaim 1
: . Note that these are the entries
of and . We will prove the claim using the fact
that for any .
Note . This is clear from the definition of
. Hence .
Note that . This is clear from the definition of
, since is composed of with columns of
zeroes added on the left.
Note that for and ,
Hence .
Subclaim 2:
where stands for the minimum Euclidean distance
betweenand , that is,
To see this, note
Subclaim 3:
.
Note since , we have
. Thus .
From the above four claims, we immediately have
Thus we have proved the lemma.
Example 6
Consider the following function, for which :
When , we have .
When , we have .
Example 7
Consider the following function, for which :
When , we have .
When , we have .
Acknowledgements. Hoon Hong’s work was
supported by National Science Foundation (CCF: 2212461 and 1813340).
References
[1]
E. Artin.
Über die zerlegung definiter funktionen in quadrate.
Abhandlungen aus dem
Mathematischen Seminar der
Universität Hamburg, 5:100–115, 12 1927.
[2]
M. Avendaño.
Descartes’ rule of signs is exact!
Journal of Algebra - J ALGEBRA, 324:2884–2892, 11 2010.
[3]
S. P. Boyd and L. Vandenberghe.
Convex optimization.
Cambridge university press, 2004.
[4]
F. Budan.
Nouvelle méthode pour la résolution des équations
numériques d’un degré quelconque : d’après laquelle toute le
calcul exigé pour cette résolution se réduit à l’emploi des
deux premières règles de l’arithmétique.
Courcier, 1807.
[5]
T. Cameron and P. Psarrakos.
On descartes’ rule of signs for matrix polynomials.
Operators and Matrices, pages 643–652, 01 2019.
[6]
D. R. Curtiss.
Recent extensions of Descartes’ rule of signs.
Ann. of Math. (2), 19(4):251–278, 1918.
[7]
R. Descartes.
The Geometry of Rene Descartes: With a facsimile of the first
edition.
Courier Corporation, 2012.
[8]
E. Feliu and M. L. Telek.
On generalizing descartes’ rule of signs to hypersurfaces.
Advances in Mathematics, 408:108582, 10 2022.
[9]
J. B. J. Fourier.
Oeuvres de Fourier: Publiées par les soins de Gaston
Darboux, volume 2 of Cambridge Library Collection - Mathematics.
Cambridge University Press, 2013.
[10]
C. Hermite.
Remarques sur le théorème de m. sturm.
C. R. Acad. Sci. Paris, 36:294–297, 1853.
[11]
D. Hilbert.
Mathematical problems.
Bulletin of the American Mathematical Society, 8(10):437 –
479, 1902.
[13]
C. Niculescu and L. Persson.
Convex functions and their applications.
Springer Verlag New York, 2006.
[14]
J. Nie and M. Schweighofer.
On the complexity of putinar’s positivstellensatz.
Journal of Complexity, 23:135–150, 02 2007.
[15]
H. Poincaré.
Sur les équations algébriques.
CR Acad. Sci. Paris, 93:1418–1414, 1883.
[16]
G. Pólya.
Über positive Darstellung von Polynomen Vierteljschr.
Naturforsch. Ges. Zurich, (73), 1928.
[17]
V. Powers and B. Reznick.
A new bound for pólya’s theorem with applications to polynomials
positive on polyhedra.
Journal of Pure and Applied Algebra, 164(1-2):221–229, 10
2001.
[18]
M. Putinar.
Positive polynomials on compact semi-algebraic sets.
Indiana University
Mathematics Journal, 42(3):969–984,
1993.
[19]
K. Schmüdgen.
The k-moment problem for compact semi-algebraic sets.
Mathematische Annalen, 289(2):203–206, 1991.
[20]
M. Schweighofer.
On the complexity of schmüdgen’s positivstellensatz.
J. Complexity, 20:529–543, 08 2004.
[21]
P. Sturm.
Mémoire sur la résolution des équations
numériques, volume 6, pages 345–390.
Birkhäuser Basel, 01 2009.
[22]
D. Tokarev.
A generalisation of descartes’ rule of signs.
Journal of the Australian Mathematical Society, 91, 12 2011.