Complexity bounds for zero-test algorithms

by John Shackell and Joris van der Hoeven

Department of Mathematics Dépt. de Mathématiques (bât. 425)
University of Kent at Canterbury Université Paris-Sud
Canterbury 91405 Orsay Cedex
England France
jrs@ukc.ac.uk Joris.Vanderhoeven@math.u-psud.fr

January 5, 2015

Abstract

In this paper, we analyze the complexity of a zero test for expressions built from formal power series solutions of first order differential equations with non degenerate initial conditions. We will prove a doubly exponential complexity bound. This bound establishes a power series analogue for “witness conjectures”.

1Introduction

Zero equivalence is a major issue on the analysis side of symbolic computation. Standard mathematical notation provides a way of representing many transcendental functions. However, trivial cases apart, this notation gives rise to the following problems:

, but they represent the same function.

Often, one is interested in expressions which represent functions in a ring. In that case, the third problem reduces to deciding when a given expression represents the zero function.

As to the first two problems, one has to decide where and how we want our functions to be defined. In this paper, we will mainly be concerned with expressions that represent multivariate power series. The expressions will then be formed from the constants and the indeterminates using the ring operations and power series solutions to first-order differential equations. The correctness and non-ambiguity of expressions may then be ensured by structural induction. This may involve zero-testing for the series represented by subexpressions.

In order to evaluate the complexity of algorithms, one needs a reasonable notion for the size of an expression. In this paper, the size of an expression will always be the number of nodes in the corresponding “expression tree”. For instance the size of is . In section 3.1, we will also introduce the alternative notion of the “pseudo-norm” of an expression. Roughly speaking, all expressions in this paper may be represented by polynomials in a tower of field extensions . Such towers start with a field of constants and each with is of the form

where is a solution to an algebraic differential equations over and the are polynomials over . The pseudo-norm of an element in is defined recursively in terms of its degree in and the pseudo-norms of its coefficients.

1.1Zero-tests for constants

As a first step, one would like to be able to solve zero-equivalence for the elementary constants, that is to say the smallest field of constants closed under the application of the exponential, trigonometric and (for non-zero argument) logarithmic functions. Alas no such algorithm is known and it is clear that some formidable problems in transcendental number theory would need to be solved before one was found. In the face of this dilemma implementers have used heuristic methods generally involving floating-point computations.

Theoreticians have often resorted to the use of an oracle; in other words they pre-supposed a solution to the problem for constants. They have then gone on to develop other algorithms, for example to decide zero equivalence of functions, on this basis. However for elementary constants one can do better than merely invoke an oracle.

The Schanuel Conjecture may be stated as follows: Let be complex numbers which are linearly independent over the rational numbers . Then the transcendence degree of

is at least . Many special cases of this are well known unsolved conjectures in transcendental number theory. Following work by Lang, [Lang(1971)], algorithms for deciding the signs of elementary constants based on the Schanuel Conjecture have been given by Caviness and Prelle, [Caviness and Prelle(1978)] and Richardson, [Richardson(1997)]. The conjecture has been shown to imply the decidability of the real exponential field, [Macintyre and Wilkie(1995)].

There are definite advantages in using a conjecture from number theory rather than heuristic methods, in that it is clear what is being assumed and any counter example found would be of considerable mathematical interest. However in a practical situation, a zero equivalence method for constants is generally needed very often, and here the algorithms based on the Schanuel Conjecture are really rather slow.

Another limitation of the above approach is that it is very hard to see how to generalize the Schanuel Conjecture to cover constants given by Liouvillian or Pfaffian functions. For the substance of the conjecture is that the relations between exponentials and logarithms of numbers are just the ones we already know about, but it seems impossible to even formulate such a claim when integrals and solutions of differential equations are involved.

In [van der Hoeven(2001b)], the following witness conjectures was made. Let and consider the set of real exp-log expressions such that for each subexpression of the form or , we have resp. , where denotes the value of as a real constant. Then there exists a witness function of the form (strong witness conjectures) or (weak witness conjecture) such that for any of size , it suffices to evaluate up to digits in order to determine whether it vanishes.

Earlier versions and variants of witness conjectures appeared in [van der Hoeven(1997), van der Hoeven(2001a), Richardson(2001), van der Hoeven(2001b)]. Also, Dan Richardson has accumulated numerical evidence and worked out some number-theoretic consequences. It should be noticed that these conjectures are apparently independent of the Schanuel conjecture. Indeed, there might exist non zero elementary constants, which yet evaluate to extremely small numbers. On the other hand, there might exist counterexamples to the Schanuel conjecture which can be “detected” to be zero by evaluating a reasonable number of digits. The interest of witness conjectures is that they potentially provide us with fast zero tests, if they can be proved to to hold for “reasonably small” witness functions .

Remark 1. Recently, Joris van der Hoeven and Dan Richardson found a counterexample to the strong witness conjecture: consider the function

The variable occurs only twice in the function, but has valuation as a power series. Therefore, the -th iterate of has size , but valuation . Consequently, the constant yields a counterexample to the strong witness conjecture for sufficiently large . This counterexample has been generalized in [van der Hoeven(2003)] to all polynomial witness functions. Nevertheless, no counterexamples to the weak witness conjecture are currently known.

1.2Zero-tests for functions

Although zero-test algorithms for constants are extremely hard to design, more progress has been made on zero-tests for functions [Shackell(1989), Shackell(1993), Péladan-Germa(1995)]. Unfortunately, no reasonable complexity bounds (i.e. less that the Ackermann function) for these algorithms were known up till now. In this paper, we both generalize the algorithm from [Shackell(1989), Shackell(2004)] to the multivariate setting and provide complexity bounds. A recent survey on the theoretical complexity of calculations involving Pfaffian functions is given in [Gabrielov and Vorobjov(2004)].

Now it is interesting to study the significance of such bounds for the exp-log constant conjecture. Indeed, since number theoretical questions about transcendence or Diophantine approximation are usually very hard, a first step usually consists of formulating analogue questions in the setting of function fields. A deep and well-known theorem of Ax [Ax(1971)] states that the power series version of Schanuel's conjecture does hold.

The exp-log conjecture also admits a natural power series analogue. Given a field , consider the set of multivariate power series expressions constructed from and using and left composition of infinitesimal series by , and . Now let be such an expression of size and let be the power series represented by . Then we expect that there exists a constant , such that if and only if the coefficient of in vanishes for all .

As a side effect of our complexity bounds, we will be able to prove a weaker result: with the above notation, there exists a constant , such that vanishes if and only if the coefficient of in vanishes for all . Just as the Ax theorem gives theoretical evidence that for the numerical Schanuel conjecture, our result thereby gives evidence that the numerical witness conjecture might be true.

1.3Overview

In section 2, we describe our setup of effective local domains for doing computations on power series. Such computations may either be effective zero-tests or the extraction of coefficients. We will next consider the extension of effective local domains by solutions of first order partial differential equations. In section 5, we will show that such extensions are again effective local domains.

In section 3, we recall the Bareiss method for Gaussian elimination of matrices with coefficients in an integral domain. This method has the advantage of limiting the expression swell. More precisely, we give bounds in terms of pseudo-norms on integral domains. In the sequel, the Bareiss method is applied to the efficient g.c.d. computation of several polynomials. This is an essential improvement with respect to [Shackell(1989)], which allows us to obtain “reasonable” complexity bounds for our zero test.

In section 4, we prove four key lemmas which ensure the correctness of our zero test. We also corrected a small mistake in the original correctness proof in [Shackell(1989)]. In section 5 we present the actual algorithm and complexity bounds. The main idea behind the algorithm is as follows: consider a polynomial , where are solutions to given algebraic differential equations. Then is zero-equivalent if and only if any differentially algebraic consequence of and the defining equations of is zero-equivalent. Using g.c.d. computations, we first compute a particularly simple such consequence. Next, it will suffice to check the zero-equivalence of this consequence up to a certain order.

In the case of power series over the real numbers, it is possible to obtain better theoretical bounds using techniques from [Khovanskii(1991)]. Nevertheless, we think that the results of this paper are interesting from several point of views:

We plan to improve our complexity bounds in a forthcoming paper, with the hope of obtaining bounds closer to those in [Khovanskii(1991), Theorem 1.2], i.e. of the form instead of .

2The main setup

2.1Effective local domains

Let be an effective field of constants, which means that all field operations can be performed algorithmically and that we have an effective zero test. The ring is a differential ring for the partial differentiations w.r.t. on .

We will frequently consider multivariate power series in as recursive power series in . For this reason, it is convenient to introduce the partial evaluation mappings with

for all . We re-obtain the total evaluation mappings as special cases. Notice that

for every and .

Definition 2. A differential subring of is called an effective power series domain, if we have algorithms for and an algorithm to test whether for each and .

Remark 3. Given an effective power series domain , we observe that may be considered as an effective power series domain for each . Indeed, this follows from the fact that commutes with .

Let be an effective power series domain and let be a power series in , which satisfies partial differential equations

(1)

where are such that for each . Then the ring

is a differential subring of , which is called a regular D-algebraic extension of . The main aim of this paper is to show that is also an effective power series domain and to give complexity bounds for the corresponding algorithms.

2.2Computations in

Elements in are naturally represented by polynomials in a formal variable , via the unique -algebra morphism with . This mapping naturally extends to a mapping , where

The structure of may be transported to in a natural way. The partial differentiations on extend uniquely to by setting for each (so that ). Each partial evaluation mapping induces a natural evaluation mapping on , which we will also denote by . Since is an effective local domain, the operations can clearly be performed algorithmically on . Our main problem will therefore be to design a zero-test for , which amounts to deciding whether for a given rational function .

Actually, it is more convenient to work with polynomials in instead of rational functions in . Our main problem will then be to decide whether for , since a rational function represents zero if and only if does. Unfortunately, the ring is not necessarily stable under the derivations . For this reason, we introduce the derivations

which do map into itself.

In order to determine whether for polynomials , we will consider the roots of such polynomials in the algebraic closure of . Now it is classical that the algebraic closure of the ring of univariate power series over a field is the field of Puiseux series over the algebraic closure of . Interpreting multivariate power series in as recursive power series in , we may thus view elements in as recursive Puiseux series in .

2.3Extraction of coefficients

In what follows, it will convenient to use vector notation. We define the anti-lexicographical ordering on by

for .

Consider a Puiseux series . We will write

for the power series expansion of w.r.t. . Each coefficient may recursively be expanded in a similar way w.r.t. . Alternatively, we may expand at once w.r.t. all variables using the anti-lexicographical ordering:

where . If , then the minimal with is called the valuation of and we denote it by . If , then may we define to be the coefficient of in , for each . As usual, we denote .

If is a non zero polynomial, then we define the valuation of to be the minimum of the valuations of its non zero coefficients. Suppose that . Then we recall that are power series and define

for each .

It should be noticed that the recursive extraction of coefficients can be done effectively in an effective power series domain , because

for all and . More generally, if , then we may formally represent the coefficient of by polynomials in . Such representations are best derived through relaxed evaluation of formal power series [van der Hoeven(2002b)], by using the partial differential equations satisfied by .

3The Bareiss method and g.c.d. computations

3.1Pseudo-norms

Let be an effective integral domain. In what follows, we will describe algorithms to triangulate matrices with entries in and compute g.c.d.s of polynomials with coefficients in . In order to state complexity bounds, it is convenient to measure the “sizes” of coefficients in in terms of a pseudo-norm, which is a function with the following properties:

N1

.

N2

.

If is actually a differential ring with derivations , then we also assume the existence of a constant with

N3
.

As remarked in the introduction, the pseudo-norm of an expression should not be confused with its “natural size”, i.e. the number of nodes of the corresponding expression tree.

Example 4. If , then we may take to be the maximum of the degrees of in and .

Example 5. Assume that and are as in section 2 and assume that we have a pseudo-norm on . Then we define a pseudo-norm on by

This pseudo-norm induces a pseudo-norm on by

Notice that we may take

3.2The Bareiss method

Let still be an effective integral domain with a pseudo-norm and quotient field . Consider an matrix with entries in (i.e. a matrix with rows and columns). For all indices and , we will also write for the minor of when we only keep the rows and columns .

It is classical that we may upper triangulate using Gaussian elimination. This leads to a formula

where is a matrix with determinant one and an upper triangular matrix. Unfortunately, this process usually leads to a fast coefficient growth for the numerators of the entries of the successive matrices. In order to remove this drawback, we will rather do all computations over instead of . In this section, we will briefly recall this approach, which is due to Bareiss [Bareiss(1968), Loos(1983)].

So let us now be given an matrix with entries in . For simplicity, we will first assume that the usual triangulation of as a matrix with entries in does not involve row permutations. This usual triangulation of gives rise to a sequence of identities

with , where is the matrix obtained from after steps. More precisely, is obtained from by leaving the first rows invariant and by adding multiples of the -th row to the others (in particular, the will be lower triangular throughout the process). If there exists a with , then let be the minimal such , so that for all and . Each may be rewritten as a product

of an invertible diagonal matrix and another matrix with entries in . Our aim is to show that we may choose the and of small pseudo-norms. In fact, we claim that we may take for each , where .

In order to see this, let , and . Then

since is a lower triangular matrix. Moreover, this matrix has only ones on its diagonal, whence

Since is upper triangular, we also have

By our choice of , we finally have

Putting this together, we see that each coefficient of (whence in particular ) may be written as the determinant of a minor of :

(2)

Hence, we have not only shown that the and have coefficients in , but even that they may be written explicitly as determinants of minors of . This result remains so (up to a factor ) if row permutations were needed in the triangulation process, since we may always permute the rows of a priori, so that no further row permutations are necessary during the triangulations. This proves the following theorem:

Theorem 6. There exists an algorithm, which takes an matrix with entries in on input, and which computes an invertible diagonal matrix and an upper triangular matrix with entries in , such that there exists a matrix with entries in , of determinant one, and so that . Moreover, and . Here .

By way of comment, we note that the actual computation of involves elementary operations. If we do have an algorithm for exact division in , then this is also the time complexity of the algorithm in terms of operations in . Otherwise, it may be necessary to compute the entries of the intermediate matrices by formula (2), which yields an overall complexity of .

3.3Computing greatest common divisors of several polynomials

Let still be an effective integral domain with a pseudo-norm and quotient field . Consider a finite number of polynomials in . In this section, we address the question of computing a g.c.d. of and a corresponding Bezout relation. Since we are only computing over an integral domain, we call a g.c.d., if is a scalar multiple of the g.c.d. of , when considered as polynomials over the quotient field . Accordingly, a Bezout relation for has the form

(3)

where and . From the computational point of view, we are interested in minimizing the pseudo-norms of and . As to the degrees, such “small Bezout relations” always exist. In fact quite a lot of research has been done in this area, see [Kollar(1999)] for example. Here we give a relatively simple result which is sufficient for our purposes.

Proposition 7. Let be more than one non-zero polynomial. Then there exists a Bezout relation (3), such that .

Proof. Assume the contrary and choose a Bezout relation (3) of minimal degree and such that the number of indices with is minimal. Since , we must have , and modulo a permutation of indices, we may assume that for each . Let be the leading coefficient of and the leading coefficient of . Then for , we have

is again a Bezout relation, which contradicts our minimality hypothesis.

Let . In order to actually find a Bezout relation of degree , we now consider the matrix with rows and columns, which is the vertical superposition of all matrices of the form

where . Now triangulate as in the section above

(4)

and let be the number of non-zero rows in . The -th row of corresponds to a polynomial linear combination of of minimal degree. In other words, it contains the coefficients of a g.c.d. of .

Moreover, we may obtain a Bezout relation when considering the matrix with an identity matrix glued at its right hand side. When triangulating this matrix, we obtain a relation of the form

such that the additional part of the triangulated matrix gives us the transformation matrix in (4) up to the diagonal matrix :

The finite sum which leads to the -th row in the product now yields the desired Bezout relation. Notice that in this Bezout relation.

From the complexity point of view, we also notice that at most rows in may actually have contributed to the first rows of . When replacing with its restriction to these rows, the above triangulations will therefore yield the same g.c.d. and the same Bezout relation. We have proved

Theorem 8. Let be more than one non-zero polynomials. Then there exists an algorithm to compute a g.c.d. of as well as a Bezout relation (3) with , such that

3.4Making polynomials square-free using pseudo-division

Let be an effective integral domain and let be polynomials over with . If were actually an effective field, then we might have used the Euclidean division algorithm to obtain the unique expression for of the form

with and . However, this algorithm involves divisions and can no longer be used if is an integral domain but not a field. Nevertheless, pseudo-division may always be used to obtain the unique expression for of the form

where is the leading coefficient or initial of and are such that . We also call the pseudo-quotient and the pseudo-remainder of the division of by .

Algorithm pdiv

Input with .

Output the pseudo-quotient resp. pseudo-remainder of the division of by .

Set and

For do

Return

In particular, we may use pseudo-division to make a polynomial square-free. Namely, if , then we take sqfree to be the square-free part of .

Proposition 9. Let of as above. Then .

Proof. Let and . Then , by Theorem 8. Also, for . Hence, .

4Four key lemmas

We recall that derivations on integral domains extend uniquely to their algebraic closures.

Lemma 10. Let be a square-free polynomial and

Consider the factorization

with and . Then each of the satisfies the partial differential equations (1).

Proof. Consider one of the factors of and write

For each , we have

Since both divides and , it also divides . Now is square-free, so that does not divide . Therefore divides . Consequently, for each , i.e. satisfies (1).

Lemma 11. Let be a Puiseux series with . Assume that satisfies the same equations (1) as and the same initial condition . Then .

Proof. Let us prove by induction over that for all . We have by assumption. Assume therefore that and . Then is a Puiseux series in of the form

(5)

Since and commute, satisfies the partial differential equation

(6)

In particular, extraction of the coefficient in yields

(7)

for every . Now

since . Consequently, we may see (7) as a recurrence relation which uniquely determines as a function of other with . Hence is the unique solution to (6) of the form (5). The lemma now follows by induction.

Lemma 12. Let be a polynomial in . Given with

(8)

there exists a root of with and .

Proof. Intuitively speaking, this lemma follows from the Newton polygon method: the existence of a solution to (8) implies that the Newton polygon associated to the equation admits a horizontal slope and that is a solution to the associated Newton polynomial. Therefore, is the first term of a solution to , the full solution being obtained using the Newton polygon method. If , then the Newton polygon admits a “strictly positive slope” and a similar argument applies.

More precisely, we may apply the results from chapter 3 in [van der Hoeven(2004)] (see also [van der Hoeven(1997)]). We first note that

is a field of grid-based power series. Now setting , the Newton degree of

(9)

is strictly positive. Indeed, this is clear if , and this follows from Lemma 3.6 in [van der Hoeven(2004)] if . Now our lemma follows from the fact that the algorithm polynomial_solve returns solutions to (9).

Lemma 13. With the notation from Lemma 10, we have

Proof. Assuming that , we have for . It follows that divides both and , whence and . Since , it follows in particular that .

Assume now that . Lemma 12 implies that admits a root with and . This root satisfies the equations (1), by Lemma 10. Hence , by Lemma 11. We conclude that and .

5The algorithm

5.1Statement of the algorithm

The lemmas from the previous section yield the following zero-test algorithm:

Algorithm zero_test

Input .

Output result of the test .

Step 1. [trivial case]

If then return true.

Step 2. [g.c.d. computations]

Replace .

Let .

Step 3. [compute the valuation of ]

Denote .

For , compute as a function of as follows:

Expand each coefficient () w.r.t. .

Stop at the least , such that there exists a with .

Step 4. [evaluate and conclude]

Return .

Remark 14. The expansion of in step 3 may be done efficiently using the technique of relaxed evaluation [van der Hoeven(2002b)].

5.2Complexity bounds

In order to derive complexity bounds, we will have to assume that we have a pseudo-norm on and that there exists a function , which gives a bound on the valuation , for each . Here we understand that

for each . It is also reasonable to also assume that is increasing and that it grows sufficiently fast such that , and for all . Notice that we may take for all when .

Theorem 15. Let

Then for any , we have

Proof. Assume first that can be represented by a square-free polynomial . Then does not change in step 2 of zero_test and for , we have

Then Theorem 8 yields

Since for each non-zero coefficient of , it follows that

in step 3 of zero_test. Since we assumed , we must have in the last step of zero_test. Considering the Taylor series expansion

in the infinitesimal power series , we observe that .

By Theorem 8, also satisfies a Bezout relation of the form

Now for all (recall that ), so that

We conclude that

This gives a bound for in the case when is square-free.

Let us now turn to the more general situation in which is represented by a polynomial which is no longer square-free. Setting , the above discussion yields the bound

since by Proposition 9. Now divides when we understand these polynomials to have coefficients in the quotient field of . If is the leading coefficient of , we thus have in , as is seen by pseudo-dividing by . It follows that

since .

Let us finally consider the case when is represented by a general element . Then we may rewrite as a fraction with and , and we have

Since , we thus get

since or .

Remark 16. It is plausible that the second and third part in the above proof may be further optimized, so as to reduce the exponent from to . In the second part, one might for instance consider the factorization of instead of gcd as in the zero-test algorithm.

As a consequence of the above bound for the valuations of non-zero series , we have a straightforward zero-test algorithm for series which consists of testing whether all coefficients of up to the bound vanish using relaxed evaluation [van der Hoeven(2002b)]. This algorithm satisfies the following complexity bound:

Theorem 17. Let . With the notation from Theorem 15, we may test whether represents zero in time .

Remark 18. Of course, the complexity bound from Theorem 17 is very pessimistic, since it reflects the theoretical worst case bounds for the valuations. In practice, we recommend using zero_test, which we expect to be much faster in average.

5.3Consequences of the complexity bounds

Consider a tower of regular D-algebraic ring extensions starting with . We have natural representations

for all . The repeated application of Theorem 15 yields

Corollary 19. There exists a constant , such that for all , we have either or .

Remark 20. In other words, for fixed , we have a polynomial time algorithm zero-test in . Theoretically speaking, we already knew this, because for a certain ideal of . Hence, it would suffice to reduce a polynomial in with respect to a Groebner basis for in order to know whether it represents zero. Unfortunately, we do not know of any algorithm to compute such a Groebner basis for . Nevertheless, even without such a Groebner basis the above corollary tells us that we still have a polynomial time zero-test.

Let us now return to exp-log series in the ring considered in the introduction. Recall that the size of an element in is the number of nodes in the corresponding expression tree. Repeated application of Theorem 15 yields

Corollary 21. Consider an exp-log series , which can be represented by an expression of size . Then either or .

Proof. Let be an expression which represents and let be its subexpressions listed in the order of a postfix traversal. We construct a tower with representations , such that for all and . We construct the tower by induction over . For we have nothing to show, so suppose and that we have performed the construction up to stage .

If or , then we clearly have . If , , or with , then . Assume finally that with , where . Then we take if or otherwise, and one the relations

holds for all . Notice that the pseudo-norm of is bounded by (whence by ) for all . Consequently, the from Theorem 15 is bounded by at each stage. By induction over , it therefore follows that for all . If , we conclude that .

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