123 research outputs found
Improving the use of equational constraints in cylindrical algebraic decomposition
When building a cylindrical algebraic decomposition (CAD) savings can be made
in the presence of an equational constraint (EC): an equation logically implied
by a formula.
The present paper is concerned with how to use multiple ECs, propagating
those in the input throughout the projection set. We improve on the approach of
McCallum in ISSAC 2001 by using the reduced projection theory to make savings
in the lifting phase (both to the polynomials we lift with and the cells lifted
over). We demonstrate the benefits with worked examples and a complexity
analysis
Formulating problems for real algebraic geometry
We discuss issues of problem formulation for algorithms in real algebraic
geometry, focussing on quantifier elimination by cylindrical algebraic
decomposition. We recall how the variable ordering used can have a profound
effect on both performance and output and summarise what may be done to assist
with this choice. We then survey other questions of problem formulation and
algorithm optimisation that have become pertinent following advances in CAD
theory, including both work that is already published and work that is
currently underway. With implementations now in reach of real world
applications and new theory meaning algorithms are far more sensitive to the
input, our thesis is that intelligently formulating problems for algorithms,
and indeed choosing the correct algorithm variant for a problem, is key to
improving the practical use of both quantifier elimination and symbolic real
algebraic geometry in general.Comment: To be presented at The "Encuentros de \'Algebra Computacional y
Aplicaciones, EACA 2014" (Meetings on Computer Algebra and Applications) in
Barcelon
Validity proof of Lazard's method for CAD construction
In 1994 Lazard proposed an improved method for cylindrical algebraic
decomposition (CAD). The method comprised a simplified projection operation
together with a generalized cell lifting (that is, stack construction)
technique. For the proof of the method's validity Lazard introduced a new
notion of valuation of a multivariate polynomial at a point. However a gap in
one of the key supporting results for his proof was subsequently noticed. In
the present paper we provide a complete validity proof of Lazard's method. Our
proof is based on the classical parametrized version of Puiseux's theorem and
basic properties of Lazard's valuation. This result is significant because
Lazard's method can be applied to any finite family of polynomials, without any
assumption on the system of coordinates. It therefore has wider applicability
and may be more efficient than other projection and lifting schemes for CAD.Comment: 21 page
The Potential and Challenges of CAD with Equational Constraints for SC-Square
Cylindrical algebraic decomposition (CAD) is a core algorithm within Symbolic
Computation, particularly for quantifier elimination over the reals and
polynomial systems solving more generally. It is now finding increased
application as a decision procedure for Satisfiability Modulo Theories (SMT)
solvers when working with non-linear real arithmetic. We discuss the potentials
from increased focus on the logical structure of the input brought by the SMT
applications and SC-Square project, particularly the presence of equational
constraints. We also highlight the challenges for exploiting these: primitivity
restrictions, well-orientedness questions, and the prospect of incrementality.Comment: Accepted into proceedings of MACIS 201
Iterated Resultants in CAD
Cylindrical Algebraic Decomposition (CAD) by projection and lifting requires
many iterated univariate resultants. It has been observed that these often
factor, but to date this has not been used to optimise implementations of CAD.
We continue the investigation into such factorisations, writing in the specific
context of SC-Square.Comment: Presented at the 2023 SC-Square Worksho
Need Polynomial Systems Be Doubly-Exponential?
Polynomial Systems, or at least their algorithms, have the reputation of
being doubly-exponential in the number of variables [Mayr and Mayer, 1982],
[Davenport and Heintz, 1988]. Nevertheless, the Bezout bound tells us that that
number of zeros of a zero-dimensional system is singly-exponential in the
number of variables. How should this contradiction be reconciled?
We first note that [Mayr and Ritscher, 2013] shows that the doubly
exponential nature of Gr\"{o}bner bases is with respect to the dimension of the
ideal, not the number of variables. This inspires us to consider what can be
done for Cylindrical Algebraic Decomposition which produces a
doubly-exponential number of polynomials of doubly-exponential degree.
We review work from ISSAC 2015 which showed the number of polynomials could
be restricted to doubly-exponential in the (complex) dimension using McCallum's
theory of reduced projection in the presence of equational constraints. We then
discuss preliminary results showing the same for the degree of those
polynomials. The results are under primitivity assumptions whose importance we
illustrate.Comment: Extended Abstract for ICMS 2016 Presentation. arXiv admin note: text
overlap with arXiv:1605.0249
The Complexity of Cylindrical Algebraic Decomposition with Respect to Polynomial Degree
Cylindrical algebraic decomposition (CAD) is an important tool for working
with polynomial systems, particularly quantifier elimination. However, it has
complexity doubly exponential in the number of variables. The base algorithm
can be improved by adapting to take advantage of any equational constraints
(ECs): equations logically implied by the input. Intuitively, we expect the
double exponent in the complexity to decrease by one for each EC. In ISSAC 2015
the present authors proved this for the factor in the complexity bound
dependent on the number of polynomials in the input. However, the other term,
that dependent on the degree of the input polynomials, remained unchanged.
In the present paper the authors investigate how CAD in the presence of ECs
could be further refined using the technology of Groebner Bases to move towards
the intuitive bound for polynomial degree
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