4,029 research outputs found
An Incremental Algorithm for Computing Cylindrical Algebraic Decompositions
In this paper, we propose an incremental algorithm for computing cylindrical
algebraic decompositions. The algorithm consists of two parts: computing a
complex cylindrical tree and refining this complex tree into a cylindrical tree
in real space. The incrementality comes from the first part of the algorithm,
where a complex cylindrical tree is constructed by refining a previous complex
cylindrical tree with a polynomial constraint. We have implemented our
algorithm in Maple. The experimentation shows that the proposed algorithm
outperforms existing ones for many examples taken from the literature
Cylindrical Algebraic Sub-Decompositions
Cylindrical algebraic decompositions (CADs) are a key tool in real algebraic
geometry, used primarily for eliminating quantifiers over the reals and
studying semi-algebraic sets. In this paper we introduce cylindrical algebraic
sub-decompositions (sub-CADs), which are subsets of CADs containing all the
information needed to specify a solution for a given problem.
We define two new types of sub-CAD: variety sub-CADs which are those cells in
a CAD lying on a designated variety; and layered sub-CADs which have only those
cells of dimension higher than a specified value. We present algorithms to
produce these and describe how the two approaches may be combined with each
other and the recent theory of truth-table invariant CAD.
We give a complexity analysis showing that these techniques can offer
substantial theoretical savings, which is supported by experimentation using an
implementation in Maple.Comment: 26 page
Developing a labelled object-relational constraint database architecture for the projection operator
Current relational databases have been developed in order to improve the handling of
stored data, however, there are some types of information that have to be analysed for
which no suitable tools are available. These new types of data can be represented and treated
as constraints, allowing a set of data to be represented through equations, inequations
and Boolean combinations of both. To this end, constraint databases were defined and
some prototypes were developed. Since there are aspects that can be improved, we propose
a new architecture called labelled object-relational constraint database (LORCDB). This provides
more expressiveness, since the database is adapted in order to support more types of
data, instead of the data having to be adapted to the database. In this paper, the projection
operator of SQL is extended so that it works with linear and polynomial constraints and
variables of constraints. In order to optimize query evaluation efficiency, some strategies
and algorithms have been used to obtain an efficient query plan.
Most work on constraint databases uses spatiotemporal data as case studies. However,
this paper proposes model-based diagnosis since it is a highly potential research area,
and model-based diagnosis permits more complicated queries than spatiotemporal examples.
Our architecture permits the queries over constraints to be defined over different sets
of variables by using symbolic substitution and elimination of variables.Ministerio de Ciencia y Tecnología DPI2006-15476-C02-0
An implementation of Sub-CAD in Maple
Cylindrical algebraic decomposition (CAD) is an important tool for the
investigation of semi-algebraic sets, with applications in algebraic geometry
and beyond. We have previously reported on an implementation of CAD in Maple
which offers the original projection and lifting algorithm of Collins along
with subsequent improvements.
Here we report on new functionality: specifically the ability to build
cylindrical algebraic sub-decompositions (sub-CADs) where only certain cells
are returned. We have implemented algorithms to return cells of a prescribed
dimensions or higher (layered {\scad}s), and an algorithm to return only those
cells on which given polynomials are zero (variety {\scad}s). These offer
substantial savings in output size and computation time.
The code described and an introductory Maple worksheet / pdf demonstrating
the full functionality of the package are freely available online at
http://opus.bath.ac.uk/43911/.Comment: 9 page
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
An implementation of CAD in Maple utilising problem formulation, equational constraints and truth-table invariance
Cylindrical algebraic decomposition (CAD) is an important tool for the
investigation of semi-algebraic sets, with applications within algebraic
geometry and beyond. We recently reported on a new implementation of CAD in
Maple which implemented the original algorithm of Collins and the subsequent
improvement to projection by McCallum. Our implementation was in contrast to
Maple's in-built CAD command, based on a quite separate theory. Although
initially developed as an investigative tool to compare the algorithms, we
found and reported that our code offered functionality not currently available
in any other existing implementations. One particularly important piece of
functionality is the ability to produce order-invariant CADs. This has allowed
us to extend the implementation to produce CADs invariant with respect to
either equational constraints (ECCADs) or the truth-tables of sequences of
formulae (TTICADs). This new functionality is contained in the second release
of our code, along with commands to consider problem formulation which can be a
major factor in the tractability of a CAD. In the report we describe the new
functionality and some theoretical discoveries it prompted. We describe how the
CADs produced using equational constraints are able to take advantage of not
just improved projection but also improvements in the lifting phase. We also
present an extension to the original TTICAD algorithm which increases both the
applicability of TTICAD and its relative benefit over other algorithms. The
code and an introductory Maple worksheet / pdf demonstrating the full
functionality of the package are freely available online.Comment: 12 pages; University of Bath, Dept. Computer Science Technical Report
Series, 2013-02, 201
Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition
Cylindrical algebraic decomposition(CAD) is a key tool in computational
algebraic geometry, particularly for quantifier elimination over real-closed
fields. When using CAD, there is often a choice for the ordering placed on the
variables. This can be important, with some problems infeasible with one
variable ordering but easy with another. Machine learning is the process of
fitting a computer model to a complex function based on properties learned from
measured data. In this paper we use machine learning (specifically a support
vector machine) to select between heuristics for choosing a variable ordering,
outperforming each of the separate heuristics.Comment: 16 page
Constructing Fewer Open Cells by GCD Computation in CAD Projection
A new projection operator based on cylindrical algebraic decomposition (CAD)
is proposed. The new operator computes the intersection of projection factor
sets produced by different CAD projection orders. In other words, it computes
the gcd of projection polynomials in the same variables produced by different
CAD projection orders. We prove that the new operator still guarantees
obtaining at least one sample point from every connected component of the
highest dimension, and therefore, can be used for testing semi-definiteness of
polynomials. Although the complexity of the new method is still doubly
exponential, in many cases, the new operator does produce smaller projection
factor sets and fewer open cells. Some examples of testing semi-definiteness of
polynomials, which are difficult to be solved by existing tools, have been
worked out efficiently by our program based on the new method.Comment: Accepted by ISSAC 2014 (July 23--25, 2014, Kobe, Japan
Cylindrical Algebraic Decomposition Using Local Projections
We present an algorithm which computes a cylindrical algebraic decomposition
of a semialgebraic set using projection sets computed for each cell separately.
Such local projection sets can be significantly smaller than the global
projection set used by the Cylindrical Algebraic Decomposition (CAD) algorithm.
This leads to reduction in the number of cells the algorithm needs to
construct. We give an empirical comparison of our algorithm and the classical
CAD algorithm
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