Accessibility setting

Section: New Results

Algebraic computing and high-performance kernels

Algebraic Diagonals and Walks: Algorithms, Bounds, Complexity

The diagonal of a multivariate power series $F$ is the univariate power series $Diag\left(F\right)$ generated by the diagonal terms of $F$. Diagonals form an important class of power series; they occur frequently in number theory, theoretical physics and enumerative combinatorics. We study algorithmic questions related to diagonals in the case where $F$ is the Taylor expansion of a bivariate rational function. It is classical that in this case $Diag\left(F\right)$ is an algebraic function. We propose an algorithm that computes an annihilating polynomial for $Diag\left(F\right)$. We give a precise bound on the size of this polynomial and show that generically, this polynomial is the minimal polynomial and that its size reaches the bound. The algorithm runs in time quasi-linear in this bound, which grows exponentially with the degree of the input rational function. We then address the related problem of enumerating directed lattice walks. The insight given by our study leads to a new method for expanding the generating power series of bridges, excursions and meanders. We show that their first $N$ terms can be computed in quasi-linear complexity in $N$, without first computing a very large polynomial equation [6].

Multiple Binomial Sums

Multiple binomial sums form a large class of multi-indexed sequences, closed under partial summation, which contains most of the sequences obtained by multiple summation of products of binomial coefficients and also all the sequences with algebraic generating function. We study the representation of the generating functions of binomial sums by integrals of rational functions. The outcome is twofold. Firstly, we show that a univariate sequence is a multiple binomial sum if and only if its generating function is the diagonal of a rational function. Secondly, we propose algorithms that decide the equality of multiple binomial sums and that compute recurrence relations for them. In conjunction with geometric simplifications of the integral representations, this approach behaves well in practice. The process avoids the computation of certificates and the problem of the appearance of spurious singularities that afflicts discrete creative telescoping, both in theory and in practice [7].

Fast and Accurate Computation of Orbital Collision Probability for Short-Term Encounters

We provide a new method for computing the probability of collision between two spherical space objects involved in a short-term encounter under Gaussian-distributed uncertainty. In this model of conjunction, classical assumptions reduce the probability of collision to the integral of a two-dimensional Gaussian probability density function over a disk. The computational method is based on an analytic expression for the integral, derived by use of Laplace transform and D-finite functions properties. The formula has the form of a product between an exponential term and a convergent power series with positive coefficients. Analytic bounds on the truncation error are also derived and are used to obtain a very accurate algorithm. Another contribution is the derivation of analytic bounds on the probability of collision itself, allowing for a very fast and — in most cases — very precise evaluation of the risk. The only other analytical method of the literature — based on an approximation — is shown to be a special case of the new formula. A numerical study illustrates the efficiency of the proposed algorithms on a broad variety of examples and favorably compares the approach to the other methods of the literature [20].

Efficient Algorihtms for Mixed Creative Telescoping

Creative telescoping is a powerful computer algebra paradigm — initiated by Doron Zeilberger in the 90's — for dealing with definite integrals and sums with parameters. We address the mixed continuous-discrete case, and focus on the integration of bivariate hypergeometric-hyperexponential terms. We design a new creative telescoping algorithm operating on this class of inputs, based on a Hermite-like reduction procedure. The new algorithm has two nice features: it is efficient and it delivers, for a suitable representation of the input, a minimal-order telescoper. Its analysis reveals tight bounds on the sizes of the telescoper it produces [26].

Symbolic-Numeric Tools for Analytic Combinatorics in Several Variables

Analytic combinatorics studies the asymptotic behaviour of sequences through the analytic properties of their generating functions. This article provides effective algorithms required for the study of analytic combinatorics in several variables, together with their complexity analyses. Given a multivariate rational function we show how to compute its smooth isolated critical points, with respect to a polynomial map encoding asymptotic behaviour, in complexity singly exponential in the degree of its denominator. We introduce a numerical Kronecker representation for solutions of polynomial systems with rational coefficients and show that it can be used to decide several properties (0 coordinate, equal coordinates, sign conditions for real solutions, and vanishing of a polynomial) in good bit complexity. Among the critical points, those that are minimal—a property governed by inequalities on the moduli of the coordinates—typically determine the dominant asymptotics of the diagonal coefficient sequence. When the Taylor expansion at the origin has all non-negative coefficients (known as the `combinatorial case') and under regularity conditions, we utilize this Kronecker representation to determine probabilistically the minimal critical points in complexity singly exponential in the degree of the denominator, with good control over the exponent in the bit complexity estimate. Generically in the combinatorial case, this allows one to automatically and rigorously determine asymptotics for the diagonal coefficient sequence. Examples obtained with a preliminary implementation show the wide applicability of this approach [43].

Tableau sequences, open diagrams, and Baxter families

Walks on Young’s lattice of integer partitions encode many objects of algebraic and combinatorial interest. Chen et al. established connections between such walks and arc diagrams. We show that walks that start at $\varnothing$, end at a row shape, and only visit partitions of bounded height are in bijection with a new type of arc diagram — open diagrams. Remarkably, two subclasses of open diagrams are equinumerous with well known objects: standard Young tableaux of bounded height, and Baxter permutations. We give an explicit combinatorial bijection in the former case, and a generating function proof and new conjecture in the second case [9].

On 3-dimensional lattice walks confined to the positive octant

Many recent papers deal with the enumeration of 2-dimensional walks with prescribed steps confined to the positive quadrant. The classification is now complete for walks with steps in ${\{0,\pm 1\}}^2$: the generating function is differentially finite if and only if a certain group associated with the step set is finite. We explore in this paper the analogous problem for 3-dimensional walks confined to the positive octant. The first difficulty is their number: we have to examine no less than 11074225 step sets in ${\{0,\pm 1\}}^3$ (instead of 79 in the quadrant case). We focus on the 35548 that have at most six steps. We apply to them a combined approach, first experimental and then rigorous. On the experimental side, we try to guess differential equations. We also try to determine if the associated group is finite. The largest finite groups that we find have order 48 — the larger ones have order at least 200 and we believe them to be infinite. No differential equation has been detected in those cases. On the rigorous side, we apply three main techniques to prove D-finiteness. The algebraic kernel method, applied earlier to quadrant walks, works in many cases. Certain, more challenging, cases turn out to have a special Hadamard structure, which allows us to solve them via a reduction to problems of smaller dimension. Finally, for two special cases, we had to resort to computer algebra proofs. We prove with these techniques all the guessed differential equations. This leaves us with exactly 19 very intriguing step sets for which the group is finite, but the nature of the generating function still unclear [5].

Asymptotic Lattice Path Enumeration Using Diagonals

We consider $d$-dimensional lattice path models restricted to the first orthant whose defining step sets exhibit reflective symmetry across every axis. Given such a model, we provide explicit asymptotic enumerative formulas for the number of walks of a fixed length: the exponential growth is given by the number of distinct steps a model can take, while the sub-exponential growth depends only on the dimension of the underlying lattice and the number of steps moving forward in each coordinate. The generating function of each model is first expressed as the diagonal of a multivariate rational function, then asymptotic expressions are derived by analyzing the singular variety of this rational function. Additionally, we show how to compute subdominant growth, reflect on the difference between rational diagonals and differential equations as data structures for D-finite functions, and show how to determine first order asymptotics for the subset of walks that start and end at the origin [18].

Asymptotics of lattice walks via analytic combinatorics in several variables

We consider the enumeration of walks on the two-dimensional non-negative integer lattice with steps defined by a finite set $S\subset {\{0,\pm 1\}}^2$. Up to isomorphism there are 79 unique two-dimensional models to consider, and previous work in this area has used the kernel method, along with a rigorous computer algebra approach, to show that 23 of the 79 models admit D-finite generating functions. In 2009, Bostan and Kauers used Padé-Hermite approximants to guess differential equations which these 23 generating functions satisfy, in the process guessing asymptotics of their coefficient sequences. In this article we provide, for the first time, a complete rigorous verification of these guesses. Our technique is to use the kernel method to express 19 of the 23 generating functions as diagonals of tri-variate rational functions and apply the methods of analytic combinatorics in several variables (the remaining 4 models have algebraic generating functions and can thus be handled by univariate techniques). This approach also shows the link between combinatorial properties of the models and features of its asymptotics such as asymptotic and polynomial growth factors. In addition, we give expressions for the number of walks returning to the x-axis, the y-axis, and the origin, proving recently conjectured asymptotics of Bostan, Chyzak, van Hoeij, Kauers, and Pech [44].

Linear Time Interactive Certificates

With J.G. Dumas (LJK, Grenoble), E. Kaltofen (NCSU, USA), and E. Thomé (Inria Nancy) we work on interactive certificates. Computational problem certificates are additional data structures for each output, which can be used by a (possibly randomized) verification algorithm that proves the correctness of each output. In [32] we give a new certificate for the minimal polynomial of sparse or structured matrices whose Monte Carlo verification complexity requires a single matrix-vector multiplication and a linear number of extra field operations (sufficiently large cardinality field). We also propose a novel preconditioner that ensures irreducibility of the characteristic polynomial of the generically preconditioned matrix. This preconditioner takes linear time to be applied and uses only two random entries. We combine these two techniques to give algorithms that compute certificates for the determinant, and thus for the characteristic polynomial, whose Monte Carlo verification complexity is therefore also linear.

Computing minimal interpolation bases

With É. Schost (U. Waterloo, Canada), we consider the problem of computing minimal bases of solutions for a general interpolation problem, which encompasses Hermite-Padé approximation and constrained multivariate interpolation, and has applications in coding theory and security. The problem is classically solved using iterative algorithms based on recurrence relations. First, we discuss in [62] a fast, divide-and-conquer version of this recurrence, taking advantage of fast matrix computations over the scalars and over the polynomials. This new algorithm is deterministic, and for computing shifted minimal bases of relations between $m$ vectors of size $\sigma$ it uses $\tilde{O}(m^{\omega -1}(\sigma +|s\left|\right))$ field operations, where the notation $\tilde{O}\left(·\right)$ indicates that logarithmic terms are omitted, $\omega \in [2,2.38]$ is the exponent of matrix multiplication, and $\left|s\right|$ is the sum of the entries of the input shift $s$, with $min\left(s\right)=0$. This complexity bound improves in particular on earlier algorithms in the case of bivariate interpolation for soft decoding, while matching fastest existing algorithms for simultaneous Hermite-Padé approximation. Then we propose in [33] an algorithm for the computation of an interpolation basis in shifted-Popov normal form with a cost of $\tilde{O}\left(m^{\omega -1}\sigma \right)$ field operations. Previous works, in the case of Hermite-Padé approximation and in the general interpolation case, compute non-normalized bases. Since for arbitrary shifts such bases may have size $\Theta \left(m^2\sigma \right)$, the cost bound $\tilde{O}\left(m^{\omega -1}\sigma \right)$ was feasible only with restrictive assumptions on the shift that ensure small output sizes. The question of handling arbitrary shifts with the same complexity bound was left open. To obtain the target cost for any shift, we strengthen the properties of the output bases, and of those obtained during the course of the algorithm: all the bases are computed in shifted Popov form, whose size is always $O\left(m\sigma \right)$. Then, we design a divide-and-conquer scheme. We recursively reduce the initial interpolation problem to sub-problems with more convenient shifts by first computing information on the degrees of the intermediate bases.

Fast computation of shifted Popov forms of polynomial matrices via systems of modular polynomial equations

In [46] we give a Las Vegas algorithm which computes the shifted Popov form of an $m\times m$ nonsingular polynomial matrix of degree $d$ in expected $\tilde{O}\left(m^{\omega }d\right)$ field operations, where $\omega$ is the exponent of matrix multiplication and $\tilde{O}\left(·\right)$ indicates that logarithmic factors are omitted. This is the first algorithm in $\tilde{O}\left(m^{\omega }d\right)$ for shifted row reduction with arbitrary shifts. Using partial linearization, we reduce the problem to the case $d\le \lceil \sigma /m\rceil$ where $\sigma$ is the generic determinant bound, with $\sigma /m$ bounded from above by both the average row degree and the average column degree of the matrix. The cost above becomes $\tilde{O}\left(m^{\omega }\lceil \sigma /m\rceil \right)$, improving upon the cost of the fastest previously known algorithm for row reduction, which is deterministic. Our algorithm first builds a system of modular equations whose solution set is the row space of the input matrix, and then finds the basis in shifted Popov form of this set. We give a deterministic algorithm for this second step supporting arbitrary moduli in $\tilde{O}\left(m^{\omega -1}\sigma \right)$ field operations, where $m$ is the number of unknowns and $\sigma$ is the sum of the degrees of the moduli. This extends previous results with the same cost bound in the specific cases of order basis computation and M-Padé approximation, in which the moduli are products of known linear factors.

Fast, deterministic computation of the Hermite normal form and determinant of a polynomial matrix

With G. Labahn and W. Zhou (U. Waterloo, Canada) we give in [64] fast and deterministic algorithms to compute the determinant and Hermite normal form of a nonsingular $n\times n$ matrix of univariate polynomials over a field $𝕂$. Our algorithms use $\tilde{O}\left(n^{\omega }\lceil s\rceil \right)$ operations in $𝕂$, where $s$ is bounded from above by both the average of the degrees of the rows and that of the columns of the matrix and $\omega$ is the exponent of matrix multiplication. The soft-O notation indicates that logarithmic factors in the big-O are omitted while the ceiling function indicates that the cost is $\tilde{O}\left(n^{\omega }\right)$ when $s=o\left(1\right)$. Our algorithms are based on a fast and deterministic triangularization method for computing the diagonal entries of the Hermite form of a nonsingular matrix.

Fast Computation of the Rank Profile Matrix and the Generalized Bruhat Decomposition

The row (resp. column) rank profile of a matrix describes the stair-case shape of its row (resp. column) echelon form. With J. G. Dumas and Z. Sultan (LJK, Grenoble), we propose in [11] a new matrix invariant, the rank profile matrix, summarizing all information on the row and column rank profiles of all the leading sub-matrices. We show that this normal form exists and is unique over any ring, provided that the notion of McCoy's rank is used, in the presence of zero divisors. We then explore the conditions for a Gaussian elimination algorithm to compute all or part of this invariant, through the corresponding PLUQ decomposition. This enlarges the set of known Elimination variants that compute row or column rank profiles. As a consequence a new Crout base case variant significantly improves the practical efficiency of previously known implementations over a finite field. With matrices of very small rank, we also generalize the techniques of Storjohann and Yang to the computation of the rank profile matrix, achieving an ${(r^{\omega }+mn)}^{1+o\left(1\right)}$ time complexity for an $m\times n$ matrix of rank $r$, where $\omega$ is the exponent of matrix multiplication. Finally, by give connections to the Bruhat decomposition, and several of its variants and generalizations. Thus, our algorithmic improvements for the PLUQ factorization, and their implementations, directly apply to these decompositions. In particular, we show how a PLUQ decomposition revealing the rank profile matrix also reveals both a row and a column echelon form of the input matrix or of any of its leading sub-matrices, by a simple post-processing made of row and column permutations.

Computing with quasiseparable matrices

The class of quasiseparable matrices is defined by a pair of bounds, called the quasiseparable orders, on the ranks of the sub-matrices entirely located in their strictly lower and upper triangular parts. These arise naturally in applications, as e.g. the inverse of band matrices, and are widely used for they admit structured representations allowing to compute with them in time linear in the dimension. In [47] we show the connection between the notion of quasiseparability and the rank profile matrix invariant of Dumas et al. This allows us to propose an algorithm computing the quasiseparable orders $(r_L,r_U)$ in time $O\left(n^2{s}^{\omega -2}\right)$, where $s=max(r_L,r_U)$ and $\omega$ is the exponent of matrix multiplication. We then present two new structured representations, a binary tree of PLUQ decompositions, and the Bruhat generator, using respectively $O(nslog(n/s\left)\right)$ and $O\left(ns\right)$ field elements instead of $O\left(n{s}^2\right)$ for the classical generator and $O\left(nslogn\right)$ for the hierarchically semiseparable representations. We present algorithms computing these representations in time $O\left(n^2{s}^{\omega -2}\right)$. These representations allow a matrix-vector product in time linear in the size of their representation. Lastly we show how to multiply two such structured matrices in time $O\left(n^2{s}^{\omega -2}\right)$.

A Real QZ Algorithm for Structured Companion Pencils

With Y. Eidelman (U. Tel Aviv) and L. Gemignani (U. Pisa), we design in [54] a fast implicit real QZ algorithm for eigenvalue computation of structured companion pencils arising from linearizations of polynomial rootfind-ing problems. The modified QZ algorithm computes the generalized eigenvalues of an $N\times N$ structured matrix pencil using $O\left(N^2\right)$ flops and $O\left(N\right)$ memory storage. Numerical experiments and comparisons confirm the effectiveness and the stability of the proposed method.

Efficient Solution of Parameter Dependent Quasiseparable Systems and Computation of Meromorphic Matrix Functions

In [55], with Y. Eidelman (U. Tel Aviv) and L. Gemignani (U. Pisa), we focus on the solution of shifted quasiseparable systems and of more general parameter dependent matrix equations with quasiseparable representations. We propose an efficient algorithm exploiting the invariance of the quasiseparable structure under diagonal shifting and inversion. This algorithm is applied to compute various functions of matrices. Numerical experiments show the effectiveness of the approach.