Pascal Giorgi

LINBOX: A GENERIC LIBRARY FOR EXACT LINEAR ALGEBRA

Black box techniques [12] are enabling exact linear algebra computations of a scale well beyond anything previously possible. The development of new and interesting algorithms has proceeded apace for the past two decades. It is time for the dissemination of these algorithms in an easily used software library so that the mathematical community may readily take advantage of their power. LinBox is that library. In this paper, we describe the design of this generic library, sketch its current range of capabilities, and give several examples of its use. The examples include a solution of Trefethen’s “Hundred Digit Challenge” problem #7 [14] and the computation of all the homology groups of simplicial complexes using the Smith normal form [8]. Exact black box methods are currently successful on sparse matrices with hundreds of thousands of rows and columns and having several million nonzero entries. The main reason large problems can be solved by black box methods is that they require much less memory in general than traditional eliminationbased methods do. This fact is widely used in the numerical computation area. We refer for instance to the templates for linear system solution and eigenvalue problems [2,1]. This has also led the computer algebra community to a considerable interest in black box methods. Since Wiedemann’s seminal paper [16], many developments have been proposed especially to adapt Krylov or Lanczos methods to fast exact algorithms. We refer to [5] and references therein for a review of problems and solutions. LinBox supplies efficient black box solutions for a variety of problems including linear equations and matrix normal forms with the guiding design principle of re-usability. The most essential and driving design criterion for LinBox is that it is generic with respect to the domain of computation. This is because there are many and various representations of finite fields each of which is advantageous to use for some algorithm under some circumstance. The integral and rational number capabilities depend heavily on modular

Dense Linear Algebra over Word-Size Prime Fields: the FFLAS and FFPACK Packages

In the past two decades, some major efforts have been made to reduce exact (e.g. integer, rational, polynomial) linear algebra problems to matrix multiplication in order to provide algorithms with optimal asymptotic complexity. To provide efficient implementations of such algorithms one need to be careful with the underlying arithmetic. It is well known that modular techniques such as the Chinese remainder algorithm or the p-adic lifting allow very good practical performance, especially when word size arithmetic is used. Therefore, finite field arithmetic becomes an important core for efficient exact linear algebra libraries. In this article, we study high performance implementations of basic linear algebra routines over word size prime fields: especially matrix multiplication; our goal being to provide an exact alternate to the numerical BLAS library. We show that this is made possible by a careful combination of numerical computations and asymptotically faster algorithms. Our kernel has several symbolic linear algebra applications enabled by diverse matrix multiplication reductions: symbolic triangularization, system solving, determinant, and matrix inverse implementations are thus studied.

FFPACK: finite field linear algebra package

The FFLAS project has established that exact matrix multiplication over finite fields can be performed at the speed of the highly optimized numerical BLAS routines. Since many algorithms have been reduced to use matrix multiplication in order to be able to prove an optimal theoretical complexity, this paper shows that those optimal complexity algorithms, such as LSP factorization, rank determinant and inverse computation can also be the most efficient.

Solving sparse rational linear systems

We propose a new algorithm to find a rational solution to a sparse system of linear equations over the integers. This algorithm is based on a p-adic lifting technique combined with the use of block matrices with structured blocks. It achieves a sub-cubic complexity in terms of machine operations subject to a conjecture on the effectiveness of certain sparse projections. A LinBox-based implementation of this algorithm is demonstrated, and emphasizes the practical benefits of this new method over the previous state of the art.

Faster inversion and other black box matrix computations using efficient block projections

Efficient block projections of non-singular matrices have recently been used by the authors in [10] to obtain an efficient algorithm to find rational solutions for sparse systems of linear equations. In particular a bound ofO~(n2.5) machine operations is presented for this computation assuming that the input matrix can be multiplied by a vector with constant-sized entries using O~(n) machine operations. Somewhat more general bounds for black-box matrix computations are also derived. Unfortunately, the correctness of this algorithm depends on the existence of efficient block projections of non-singular matrices, and this was only conjectured. In this paper we establish the correctness of the algorithm from [10] by proving the existence of efficient block projections for arbitrary non-singular matrices over sufficiently large fields. We further demonstrate the usefulness of these projections by incorporating them into existing black-box matrix algorithms to derive improved bounds for the cost of several matrix problems. We consider, in particular, matrices that can be multiplied by a vector using O~(n) field operations: We show how to compute the inverse of any such non-singular matrix over any field using an expected number of O~(n2.27) operations in that field. A basis for the null space of such a matrix, and a certification of its rank, are obtained at the same cost. An application of this technique to Kaltofen and Villards Baby-Steps/Giant-Steps algorithms for the determinant and Smith Form of an integer matrix is also sketched, yielding algorithms requiring O~(n2.66) machine operations. More general bounds involving the number of black-box matrix operations to be used are also obtained. The derived algorithms are all probabilistic of the Las Vegas type. They are assumed to be able to generate random elements - bits or field elements - at unit cost, and always output the correct answer in the expected time given.

Parallel computation of the rank of large sparse matrices from algebraic K-theory

This paper deals with the computation of the rank and some integer Smith forms of a series of sparse matrices arising in algebraic K-theory. The number of non zero entries in the considered matrices ranges from 8 to 37 millions. The largest rank computation took more than 35 days on 50 processors. We report on the actual algorithms we used to build the matrices, their link to the motivic cohomology and the linear algebra and parallelizations required to perform such huge computations. In particular, these results are part of the first computation of the cohomology of the linear group GL 7(Z).