Team Arénaire

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Section: Scientific Foundations

Arithmetics and algorithms

 

When computing a solution to a numerical problem, an obvious question is that of the quality of the produced numbers. One may also require a certain level of quality, such as: approximate with a given error bound, correctly rounded, or –if possible– exact. The question thus becomes twofold: how to produce such a well-specified output and at what cost? To answer it, we focus on polynomial and integer matrix operations , Euclidean lattices and global optimization , and study the following directions:

 

Numerical Algorithms using Arbitrary Precision Interval Arithmetic. When validated results are needed, interval arithmetic can be used. New problems can be solved with this arithmetic, which provides sets instead of numbers. In particular, we target the global optimization of continuous functions. A solution to obviate the frequent overestimation of results is to increase the precision of computations.

Our work is twofold. On the one hand, efficient software for arbitrary precision interval arithmetic is developed, along with a library of algorithms based on this arithmetic. On the other hand, new algorithms that really benefit from this arithmetic are designed, tested, and compared.

To reduce the overestimation of results, variants of interval arithmetic have been developed, such as Taylor models arithmetic or affine arithmetic. These arithmetics can also benefit from arbitrary precision computations.

 

Algorithms for Exact Linear Algebra and Lattice Basis Reduction. The techniques for exactly solving linear algebra problems have been evolving rapidly in the last few years, substantially reducing the complexity of several algorithms (see for instance [3] for an essentially optimal result, or [4] ). Our main focus is on matrices whose entries are integers or univariate polynomials over a field. For such matrices, our main interest is how to relate the size of the data (integer bit lengths or polynomial degrees) to the cost of solving the problem exactly. A first goal is to design asymptotically faster algorithms, to reduce problems to matrix multiplication in a systematic way, and to relate bit complexity to algebraic complexity. Another direction is to make these algorithms fast in practice as well, especially since applications yield very large matrices that are either sparse or structured. Within the LinBox international project we work on a software library that corresponds to our algorithmic research on matrices. LinBox is a generic library that allows to plug external components in a plug-and-play fashion. The library is devoted to sparse or structured exact linear algebra and its applications.

We recently started a direction around lattice basis reduction. Euclidean lattices provide powerful tools in various algorithmic domains, we especially investigate computer arithmetic, cryptography, and algorithmic number theory. We work on improving the complexity estimates and providing better codes for LLL reduction, and for obtaining more reduced bases. The above recent progress in linear algebra may provide new insights.

 

Certified computing. Most of the algorithmic complexity questions that we investigate concern algebraic or bit-complexity models for exact computations. Much less seems to be known in approximate computing, especially for the complexity of computing (certified) error bounds, and for establishing bridges between exact, interval, and constant precision complexity estimates. We are developing this direction both for a theoretical impact, and for the design and implementation of algorithm synthesis tools for arithmetic operators, and mathematical expression evaluation.


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