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Section: Overall Objectives

Scientific ground

Basic computable objects and algorithms

The basic computable objects and algorithms we study, use, optimize or develop are among the most classical ones in computer algebra and are studied by many people around the world: they mainly focus on basic computer arithmetic, linear algebra, lattices, and both polynomial system and differential system solving.

In the context of OURAGAN, it is important to avoid reinventing the wheel and to re-use wherever possible existing objects and algorithms, not necessarily developed in our team so that the main effort is focused on finding good formulations/modelisations for an efficient use. Also, our approach for the development of basic computable objects and algorithms is application driven and follows a simple strategy : use the existing tools in priority, develop missing tools when required and then optimize the critical operations. First, for some selected problems, we do propose and develop general key algorithms (isolation of real roots of univariate polynomials, parametrisations of solutions of zero-dimensional polynomial systems, solutions of parametric equations, equidimensional decompositions, etc.) in order to complement the existing set computable objects developed and studied around the world (Gröbner bases, resultants [64], subresultants [85], critical point methods [41], etc.) which are also deeply used in our developments. Second, for a selection of well-known problems, we propose different computational strategies (for example the use of approximate arithmetic to speed up LLL algorithm or root isolators, still certifying the final result). Last, we propose specialized variants of known algorithms optimized for a given problem (for example, dedicated solvers for degenerated bivariate polynomials to be used in the computation of the topology of plane curves).

In the activity of OURAGAN, many key objects or algorithms around the resolution of algebraic systems are developed or optimized within the team, such as the resolution of polynomials in one variable with real coefficients [105] [14], rational parameterizations of solutions of zero-dimensional systems with rational coefficients [49] [13] or discriminant varieties for solving systems depending on parameters [11], but we are also power users of existing software (mainly Sage (http://www.sagemath.org/), Maple (https://maplesoft.com), Pari-GP (https://pari.math.u-bordeaux.fr),Snappea (http://www.geometrygames.org/SnapPea/)) and libraries (mainly gmp (https://gmplib.org/), mpfr (https://www.mpfr.org/), flint (http://www.flintlib.org/), arb (http://arblib.org/), etc.) to which we contribute when it makes sense.

For our studies in number theory and applications to the security of cryptographic systems, our team works on three categories of basic algorithms: discrete logarithm computations [99] (for example to make progress on the computation of class groups in number fields [90]), network reductions by means of LLL variants [75] and, obviously, various computations in linear algebra, for example dedicated to almost sparse matrices [100].

Finally, for the algorithmic approach to algebraic analysis of functional equations [45] [103] [104], we developed the effective study of both module theory and homological algebra [136] over certain noncommutative polynomial rings of functional operators [4], of Stafford's famous theorems on the Weyl algebras [127], of the equidimensional decomposition of functional systems [122], etc.

Computational Number Theory

Many frontiers between computable objects, algorithms (above section), computational number theory and applications, especially in cryptography are porous. However, one can classify our work in computational number theory into two classes of studies : computational algebraic number theory and (rigorous) numerical computations in number theory.

Our work on rigorous numerical computations is somehow a transverse activity in Ouragan : floating point arithmetic is used in many basic algorithms we develop (root isolation, LLL) and is thus present in almost all our research directions. However there are specific developments that could be labelized Number Theory, in particular contributions to numerical evaluations of L-functions which are deeply used in many problems in number theory (for example the Riemann Zeta function). We participate, for example to the L-functions and Modular Forms Database (http://www.lmfdb.org) a world wide collaborative project.

Our work in computational algebraic number theory is driven by the algorithmic improvement to solve presumably hard problems relevant to cryptography. The use of number-theoretic hard problems in cryptography dates back to the invention of public-key cryptography by Diffie and Hellman [71], where they proposed a first instantiation of their paradigm based on the discrete logarithm problem in prime fields. The invention of RSA [134], based on the hardness of factoring came as a second example. The introduction of discrete logarithms on elliptic curves [106] [116] only confirmed this trend.

These crypto-systems attracted a lot of interest on the problems of factoring and discrete log. Their study led to the invention of fascinating new algorithms that can solve the problems much faster than initially expected :

  • the elliptic curve method (ECM) [101]

  • the quadratic field for factoring [120] and its variant for discrete log called the Gaussian integers method [114]

  • the number field sieve (NFS) [39]

Since the invention of NFS in the 90’s, many optimizations of this algorithm have been performed. However, an algorithm with better complexity hasn’t been found for factoring and discrete logarithms in large characteristic.

While factorization and discrete logarithm problems have a long history in cryptography, the recent post-quantum cryptosystems introduce a new variety of presumably hard problems/objects/algorithms with cryptographic relevance: the shortest vector problem (SVP), the closest vector problem (CVP) or the computation of isogenies between elliptic curves, especially in the supersingular case.

Members of OURAGAN started working on the topic of discrete logarithms around 1998, with several computation records that were announced on the NMBRTHRY mailing list. In large characteristic, especially for the case of prime fields, the best current method is the number field sieve (NFS) algorithm. In particular, they published the first NFS based record computation[10]. Despite huge practical improvements, the prime field case algorithm hasn't really changed since that first record. Around the same time, we also presented small characteristic computation record based on simplifications of the Function Field Sieve (FFS) algorithm  [98].

In 2006, important changes occurred concerning the FFS and NFS algorithms, indeed, while the algorithms only covered the extreme case of constant characteristic and constant extension degree, two papers extended their ranges of applicability to all finite fields. At the same time, this permitted a big simplification of the FFS, removing the need for function fields.

Starting from 2012, new results appeared in small characteristic. Initially based on a simplification of the 2006 result, they quickly blossomed into the Frobenial representation methods, with quasi-polynomial time complexity [99], [91].

An interesting side-effect of this research was the need to revisit the key sizes of pairing-based cryptography. This type of cryptography is also a topic of interest for OURAGAN. In particular, it was introduced in 2000 [9].

The computations of class groups in number fields has strong links with the computations of discrete logarithms or factorizations using the NFS (number field sieve) strategy which as the name suggests is based on the use of number fields. Roughly speaking, the NFS algorithm uses two number fields and the strategy consists in choosing number fields with small sized coefficients in their definition polynomials. On the contrary, in class group computations, there is a single number field, which is clearly a simplification, but this field is given as input by some fixed definition polynomial. Obviously, the degree of this polynomial as well as the size of its coefficients are both influencing the complexity of the computations so that finding other polynomials representing the same class group but with a better characterization (degree or coefficient's sizes) is a mathematical problem with direct practical consequences. We proposed a method to address the problem [90], but many issues remain open.

Computing generators of principal ideals of cyclotomic fields is also strongly related to the computation of class groups in number fields. Ideals in cyclotomic fields are used in a number of recent public-key cryptosystems. Among the difficult problems that ensure the safety of these systems, there is one that consists in finding a small generator, if it exists, of an ideal. The case of cyclotomic fields is considered [44].

Topology in small dimension

Character varieties

There is a tradition of using computations and software to study and understand the topology of small dimensional manifolds, going back at least to Thurston's works (and before him, Riley's pioneering work). The underlying philosophy of these tools is to build combinatorial models of manifolds (for example, the torus is often described as a square with an identification of the sides). For dimensions 2, 3 and 4, this approach is relevant and effective. In the team OURAGAN, we focus on the dimension 3, where the manifolds are modelized by a finite number of tetrahedra with identification of the faces. The software SnapPy (https://www.math.uic.edu/t3m/SnapPy/) implements this strategy [139] and is regularly used as a starting point in our work. Along the same philosophy of implementation, we can also cite Regina (https://regina-normal.github.io). A specific trait of SnapPy is that it focuses on hyperbolic structures on the 3-dimensional manifolds. This setting is the object of a huge amount of theoretical work that were used to speed up computations. For example, some Newton methods were implemented without certification for solving a system of equations, but the theoretical knowledge of the uniqueness of the solution made this implementation efficient enough for the target applications. In recent years, in part under the influence of our team (as part of the CURVE project), more attention has been given to certified computations (at least with an error control) and now this is implemented in SnapPy.

This philosophy (modelization of manifolds by quite simple combinatoric models to compute such complicated objects as representations of the fundamental group) was applied in a pioneering work of Falbel [8] when he begins to look for another type of geometry on 3-dimensional manifolds (called CR-spherical geometry). From a computational point of view, this change of objectives was a jump in the unknown: the theoretical justification for the computations were missing, and the number of variables of the systems were multiplied by four. So instead of a relatively small system that could be tackled by Newton methods and numerical approximations, we had to deal with/study (were in front of) relatively big systems (the smallest example being 8 variables of degree 6) with no a priori description of the solutions.

Still, the computable objects that appear from the theoretical study are very often outside the reach of automated computations and are to be handled case by case. A few experts around the world have been tackling this kind of computations (Dunfield, Goerner, Heusener, Porti, Tillman, Zickert) and the main current achievement is the Ptolemy module (https://www.math.uic.edu/t3m/SnapPy/ptolemy.html) for SnapPy.

From these early computational needs, topology in small dimension has historically been the source of collaboration with the IMJ-PRG laboratory. At the beginning, the goal was essentially to provide computational tools for finding geometric structures in triangulated 3-dimensional varieties. Triangulated varieties can be topologically encoded by a collection of tetrahedra with gluing constraints (this can be called a triangulation or mesh, but it is not an approximation of the variety by simple structures, rather a combinatorial model). Imposing a geometric structure on this combinatorial object defines a number of constraints that we can translate into an algebraic system that we then have to solve to study geometric structures of the initial variety, for example in relying on solutions to study representations of the fundamental group of the variety. For these studies, a large part of the computable objects or algorithms we develop are required, from the algorithms for univariate polynomials to systems depending on parameters. It should be noted that most of the computational work lies in the modeling of problems [43][7] that have strictly no chance to be solved by blindly running the most powerful black boxes: we usually deal here with systems that have 24 to 64 variables, depend on 4 to 8 parameters and with degrees exceeding 10 in each variable. With an ANR (ANR project Structures Géométriques et Triangulations) funding on the subject, the progress that we did [79] were (much) more significant than expected. In particular, we have introduced new computable objects with an immediate theoretical meaning (let us say rather with a theoretical link established with the usual objects of the domain), namely, the so-called deformation variety.

Knot theory

Knot theory is a wide area of mathematics. We are interested in polynomial representations of long knots, that is to say polynomial embeddings 𝐑𝐑3𝐒3. Every knot admits a polynomial representation and a natural question is to determine explicit parameterizations, minimal degree parameterizations. On the other hand we are interested to determine what is the knot of a given polynomial smooth embedding 𝐑𝐑3. These questions involve real algebraic curves. This subject was first considered by Vassiliev in the 90's [138].

A Chebyshev knot [108], is a polynomial knot parameterized by a Chebyshev curve (Ta(t),Tb(t),Tc(t+ϕ)) where Tn(t)=cos(narccost) is the n-th Chebyshev polynomial of the first kind. Chebyshev knots are polynomial analogues of Lissajous knots that have been studied by Jones, Hoste, Lamm... It was first established that any knot can be parameterized by Chebyshev polynomials, then we have studied the properties of harmonic nodes [110] which then opened the way to effective computations.

Our activity in Knot theory is a bridge between our work in computational geometry (topology and drawing of real space curves) and our work on topology in small dimensions (varieties defined as a knot complement).

Two-bridge knots (or rational knots) are particularly studied because they are much easier to study. The first 26 knots (except 85) are two-bridge knots. We were able to give an exhaustive, minimal and certified list of Chebyshev parameterizations of the first rational two-bridge knots, using blind computations [111]. On the other hand, we propose the identification of Chebyshev knot diagrams [112] by developing new certified algorithms for computing trigonometric expressions [113]. These works share many tools with our action in visualization and computational geometry.

We made use of Chebyshev polynomials so as Fibonacci polynomials which are families of orthogonal polynomials. Considering the Alexander-Conway polynomials as continuant polynomials in the Fibonacci basis, we were able to give a partial answer to Hoste's conjecture on the roots of Alexander polynomials of alternating knots ( [109]).

We study the lexicographic degree of the two-bridge knots, that is to say the minimal (multi)degree of a polynomial representation of a N-crossing two-bridge knot. We show that this degree is (3,b,c) with b+c=3N. We have determined the lexicographic degree of the first 362 first two-bridge knots with 12 crossings or fewer [58] (Minimal degrees are listed in https://webusers.imj-prg.fr/~pierre-vincent.koseleff/knots/2bk-lexdeg.html). These results make use of the braid theoretical approach developped by Y. Orevkov to study real plane curves and the use of real pseudoholomorphic curves [56], the slide isotopies on trigonal diagrams, namely those that never increase the number of crossings [57].

Visualization and Computational Geometry

The drawing of algebraic curves and surfaces is a critical action in OURAGAN since it is a key ingredient in numerous developments. For example, a certified plot of a discriminant variety could be the only admissible answer that can be proposed for engineering problems that need the resolution of parametric algebraic systems: this variety (and the connected components of its counter part) defines a partition of the parameter’s space in regions above which the solutions are numerically stable and topologically simple. Several directions have been explored since the last century, ranging from pure numerical computations to infallible exact ones, depending on the needs (global topology, local topology, simple drawing, etc.). For plane real algebraic curves, one can mention the cylindrical algebraic decomposition [63], grids methods (for ex. the marching square algorithm), subdivision methods, etc.

As mentioned above, we focus on curves and surfaces coming from the study of parametric systems. They mostly come from some elimination process, they highly (numerically) unstable (a small deformation of the coefficients might change a lot the topology of the curve) and we are mostly interested in getting qualitative information about their counter part in the parameter's space.

For this work, we are associated with the GAMBLE EPI (Inria Nancy Grand Est) with the aim of developing computational techniques for the study, plotting and topology. In this collaboration, Ouragan focuses on CAD-Like methods while Gamble develops numerical strategies (that could also apply on non algebraic curves). Ouragan's work involves the development of effective methods for the resolution of algebraic systems with 2 or 3 variables [49], [105], [50], [51] which are basic engines for computing the topology [118], [70] and / or plotting.

Algebraic analysis of functional systems

Systems of functional equations or simply functional systems are systems whose unknowns are functions, such as systems of ordinary or partial differential equations, of differential time-delay equations, of difference equations, of integro-differential equations, etc.

Numerical aspects of functional systems, especially differential systems, have been widely studied in applied mathematics due to the importance of numerical simulation issues.

Complementary approaches, based on algebraic methods, are usually upstream or help the numerical simulation of systems of functional systems. These methods also tackle a different range of questions and problems such as algebraic preconditioning, elimination and simplification, completion to formal integrability or involution, computation of integrability conditions and compatibility conditions, index reduction, reduction of variables, choice of adapted coordinate systems based on symmetries, computation of first integrals of motion, conservation laws and Lax pairs, Liouville integrability, study of the (asymptotic) behavior of solutions at a singularity, etc. Although not yet very popular in applied mathematics, these theories have lengthy been studied in fundamental mathematics and were developed by Lie, Cartan, Janet, Ritt, Kolchin, Spencer, etc. [94] [103] [104] [107] [133] [121].

Over the past years, certain of these algebraic approaches to functional systems have been investigated within an algorithmic viewpoint, mostly driven by applications to engineering sciences such as mathematical systems theory and control theory. We have played a role towards these effective developments, especially in the direction of an algorithmic approach to the so-called algebraic analysis [103], [104], [45], a mathematical theory developed by the Japanese school of Sato, which studies linear differential systems by means of both algebraic and analytic methods. To develop an effective approach to algebraic analysis, we first have to make algorithmic standard results on rings of functional operators, module theory, homological algebra, algebraic geometry, sheaf theory, category theory, etc., and to implement them in computer algebra systems. Based on elimination theory (Gröbner or Janet bases [94], [62], [135], differential algebra [47] [76], Spencer's theory [121], etc.), in [4], [5], we have initiated such a computational algebraic analysis approach for general classes of functional systems (and not only for holonomic systems as done in the literature of computer algebra [62]). Based on the effective aspects to algebraic analysis approach, the parametrizability problem [4], the reduction and (Serre) decomposition problems [5], the equidimensional decomposition [122], Stafford's famous theorems for the Weyl algebras [127], etc., have been studied and solutions have been implemented in Maple , Mathematica , and GAP [61][5]. But these results are only the first steps towards computational algebraic analysis, its implementation in computer algebra systems, and its applications to mathematical systems, control theory, signal processing, mathematical physics, etc.

Synergies

Outside applications which can clearly be seen as transversal acitivies, our development directions are linked at several levels : shared computable objects, computational strategies and transversal research directions.

Sharing basic algebraic objects As seen above, is the well-known fact that the elimination theory for functional systems is deeply intertwined with the one for polynomial systems so that, topology in small dimension, applications in control theory, signal theory and robotics share naturally a large set of computable objects developped in our project team.

Performing efficient basic arithmetic operations in number fields is also a key ingredient to most of our algorithms, in Number theory as well as in topology in small dimension or , more generally in the use of roots of polynomials systems. In particular, finding good representations of number fields, lead to the same computational problems as working with roots of polynomial systems by means of triangular systems (towers of number fields) or rational parameterizations (unique number field). Making any progress in one direction will probably have direct consequences for almost all the problems we want to tackle.

Symbolic-numeric strategies. Several general low-level tools are also shared such as the use of approximate arithmetic to speed up certified computations. Sometimes these can also lead to improvement for a different purpose (for example computations over the rationals, deeply used in geometry can often be performed in parallel combining computations in finite fields together with fast Chinese remaindering and modular evaluations).

As simple example of this sharing of tools and strategies, the use of approximate arithmetic is common to the work on LLL (used in the evaluation of the security of cryptographic systems), resolutions of real-world algebraic systems (used in our applications in robotics, control theory, and signal theory), computations of signs of trigonometric expressions used in knot theory or to certified evaluations of dilogarithm functions on an algebraic variety for the computation of volumes of representations in our work in topology, numerical integration and computations of L-functions.

Transversal research directions. The study of the topology of complex algebraic curves is central in the computation of periods of algebraic curves (number theory) but also in the study of character varieties (topology in small dimension) as well as in control theory (stability criteria). Very few computational tools exists for that purpose and they mostly convert the problem to the one of variety over the reals (we can then recycle our work in computational geometry).

As for real algebraic curves, finding a way to describe the topology (an equivalent to the graph obtained in the real case) or computing certified drawings (in the case of a complex plane curve, a useful drawing is the so called associated amoeba) are central subjects for Ouragan.

As mentioned in the section 3.3.1 the computation of the Mahler measure of an algebraic implicit curve is either a challenging problem in number theory and a new direction in topology. The basic formula requires the study of points of moduli 1 , as for stability problems in Control Theory (stability problems), and certified numerical evaluations of non algebraic functions at algebraic points as for many computations for L-Functions.