Fast Multiplication and Sparse Structures

The applicability of fast multiplication algorithms to sparse structures is discussed. Estimates for the degree of sparseness of matrices and polynomials are given for which fast multiplication algorithms have advantages over standard multiplication algorithms in terms of the multiplicative complexity. Specifically, the Karatsuba and Strassen algorithms are studied under the assumption of the uniform distribution of zero elements.     

Symbolic-numerical solution of systems of linear ordinary differential equations with required accuracy

In the paper, a symbolic-numerical algorithm for solving systems of ordinary linear differential equations with constant coefficients and compound right-hand sides. The algorithm is based on the Laplace transform. A part of the algorithm determines the error of calculation that is sufficient for the required accuracy of the solution of the system. The algorithm is efficient in solving systems of differential equations of large size and is capable of choosing methods for solving the algebraic system (the image of the Laplace transform) depending on its type; by doing so the efficiency of the solution of the original system is optimized. The algorithm is a part of the library of algorithms of the Mathpar system [15].     

MathPartner computer algebra

In this paper, we describe general characteristics of the MathPartner computer algebra system (CAS) and Mathpar programming language thereof. MathPartner can be used for scientific and engineering calculations, as well as in high schools and universities. It allows one to carry out both simple calculations (acting as a scientific calculator) and complex calculations with large-scale mathematical objects. Mathpar is a procedural language; it supports a large number of elementary and special functions, as well as matrix and polynomial operators. This service allows one to build function images and animate them. MathPartner also makes it possible to solve some symbolic computation problems on supercomputers with distributed memory. We highlight main differences of MathPartner from other CASs and describe the Mathpar language along with the user service provided.     

Supercomputer Environment for Recursive Matrix Algorithms

Programming and Computer Software - A new runtime environment for the execution of recursive matrix algorithms on a supercomputer with distributed memory is proposed. It is designed both for dense...     

Matrix computation of subresultant polynomial remainder sequences in integral domains

We present an improved variant of the matrix-triangularization subresultant prs method [1] for the computation of a greatest common divisor of two polynomialsA andB (of degreesm andn, respectively) along with their polynomial remainder sequence. It is improved in the sense that we obtain complete theoretical results, independent of Van Vleck’s theorem [13] (which is not always true [2, 6]), and, instead of transforming a matrix of order 2·max(m, n) [1], we are now transforming a matrix of orderm+n. An example is also included to clarify the concepts.     

Solution of Systems of Linear Equations by the p-Adic Method

A new algorithm for solving systems of linear equations Ax = b in an Euclidean domain is suggested. In the case of the ring of integers, the complexity of this algorithm is O (n ³ mlog² ||A||), where n)$$ " align="middle" border="0"> is a matrix of rank n and , if standard algorithms for the multiplication of integers and matrices are used. Under the same conditions, the best algorithm of this kind among those published earlier, which was suggested by Labahn and Storjohann in [1], has complexity O (n ⁴ mlog² ||A||). True, when using fast algorithms for the multiplication of numbers and matrices, the theoretical complexity estimate for the latter algorithm is O (n mlog² ||A||), which is better than the similar estimate O (n ³ mlog||A||) for the new algorithm.