Abstract. A common way of implementing multivariate polynomial multiplication and division is to represent polynomials as linked lists of terms sorted in a term ordering and to use repeated merging. This results in poor performance on large sparse polynomials. In this paper we use an auxiliary heap of pointers to reduce the number of monomial comparisons in the worst case while keeping the overall storage linear. We give two variations. In the first, the size of the heap is bounded by the number of terms in the quotient(s). In the second, which is new, the size is bounded by the number of terms in the divisor(s). We use dynamic arrays of terms rather than linked lists to reduce storage allocations and indirect memory references. We pack mon...
Given two multivariate polynomials A and B with integer coefficientswe present a new GCD algorithm w...
Abstract(i) First we show that all the known algorithms for polynomial division can be represented a...
International audienceThis work presents a technique to compute symbolic polynomial approximations o...
A common data structure that is used for multivariate polynomials is a linked list of terms sorted i...
We present a high performance algorithm for multiplying sparse distributed polynomials using a multi...
We demonstrate new routines for sparse multivariate polynomial multiplication and division over the ...
We provide a comprehensive presentation of algorithms, data structures, and implementation technique...
Polynomials may be represented sparsely in an effort to conserve memory usage and provide a succinct...
Abstract. The heap is a basic data structure used in a wide variety of applications, including short...
How should one design and implement a program for the multiplication of sparse polynomials? This is ...
This thesis is about monomial orderings and a division algorithm for polynomials in two or more vari...
The goal of this thesis is to develop an environment for doing delayed polynomial arithmetic. We pre...
In symbolic computation, polynomial multiplication is a fundamental operation akin to matrix multipl...
AbstractIn this paper we present a new view of a classical data structure, the heap. We view a heap ...
In this paper we present various algorithms for multiplying multivariate polynomials and series. All...
Given two multivariate polynomials A and B with integer coefficientswe present a new GCD algorithm w...
Abstract(i) First we show that all the known algorithms for polynomial division can be represented a...
International audienceThis work presents a technique to compute symbolic polynomial approximations o...
A common data structure that is used for multivariate polynomials is a linked list of terms sorted i...
We present a high performance algorithm for multiplying sparse distributed polynomials using a multi...
We demonstrate new routines for sparse multivariate polynomial multiplication and division over the ...
We provide a comprehensive presentation of algorithms, data structures, and implementation technique...
Polynomials may be represented sparsely in an effort to conserve memory usage and provide a succinct...
Abstract. The heap is a basic data structure used in a wide variety of applications, including short...
How should one design and implement a program for the multiplication of sparse polynomials? This is ...
This thesis is about monomial orderings and a division algorithm for polynomials in two or more vari...
The goal of this thesis is to develop an environment for doing delayed polynomial arithmetic. We pre...
In symbolic computation, polynomial multiplication is a fundamental operation akin to matrix multipl...
AbstractIn this paper we present a new view of a classical data structure, the heap. We view a heap ...
In this paper we present various algorithms for multiplying multivariate polynomials and series. All...
Given two multivariate polynomials A and B with integer coefficientswe present a new GCD algorithm w...
Abstract(i) First we show that all the known algorithms for polynomial division can be represented a...
International audienceThis work presents a technique to compute symbolic polynomial approximations o...