18 research outputs found
Using Machine Learning to Decide When to Precondition Cylindrical Algebraic Decomposition With Groebner Bases
Cylindrical Algebraic Decomposition (CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. However, it can be expensive, with worst case complexity doubly exponential in the size of the input. Hence it is important to formulate the problem in the best manner for the CAD algorithm. One possibility is to precondition the input polynomials using Groebner Basis (GB) theory. Previous experiments have shown that while this can often be very beneficial to the CAD algorithm, for some problems it can significantly worsen the CAD performance.In the present paper we investigate whether machine learning, specifically a support vector machine (SVM), may be used to identify those CAD problems which benefit from GB preconditioning. We run experiments with over 1000 problems (many times larger than previous studies) and find that the machine learned choice does better than the human-made heuristic
Using Machine Learning to Decide When to Precondition Cylindrical Algebraic Decomposition With Groebner Bases
Cylindrical Algebraic Decomposition (CAD) is a key tool in computational
algebraic geometry, particularly for quantifier elimination over real-closed
fields. However, it can be expensive, with worst case complexity doubly
exponential in the size of the input. Hence it is important to formulate the
problem in the best manner for the CAD algorithm. One possibility is to
precondition the input polynomials using Groebner Basis (GB) theory. Previous
experiments have shown that while this can often be very beneficial to the CAD
algorithm, for some problems it can significantly worsen the CAD performance.
In the present paper we investigate whether machine learning, specifically a
support vector machine (SVM), may be used to identify those CAD problems which
benefit from GB preconditioning. We run experiments with over 1000 problems
(many times larger than previous studies) and find that the machine learned
choice does better than the human-made heuristic
Algorithmically generating new algebraic features of polynomial systems for machine learning
There are a variety of choices to be made in both computer algebra systems
(CASs) and satisfiability modulo theory (SMT) solvers which can impact
performance without affecting mathematical correctness. Such choices are
candidates for machine learning (ML) approaches, however, there are
difficulties in applying standard ML techniques, such as the efficient
identification of ML features from input data which is typically a polynomial
system. Our focus is selecting the variable ordering for cylindrical algebraic
decomposition (CAD), an important algorithm implemented in several CASs, and
now also SMT-solvers. We created a framework to describe all the previously
identified ML features for the problem and then enumerated all options in this
framework to automatically generation many more features. We validate the
usefulness of these with an experiment which shows that an ML choice for CAD
variable ordering is superior to those made by human created heuristics, and
further improved with these additional features. We expect that this technique
of feature generation could be useful for other choices related to CAD, or even
choices for other algorithms with polynomial systems for input.Comment: To appear in Proc SC-Square Workshop 2019. arXiv admin note:
substantial text overlap with arXiv:1904.1106
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
Using Machine Learning to Improve Cylindrical Algebraic Decomposition
Cylindrical Algebraic Decomposition (CAD) is a key tool in computational
algebraic geometry, best known as a procedure to enable Quantifier Elimination over real-closed fields. However, it has a worst case complexity doubly exponential in the size of the input, which is often encountered in practice. It has been observed that for many problems a change in algorithm settings or problem formulation can cause huge differences in runtime costs, changing problem instances from intractable to easy. A number of heuristics have been developed to help with such choices, but the complicated nature of the geometric relationships involved means these are imperfect and can sometimes make poor choices. We investigate the use of machine learning (specifically
support vector machines) to make such choices instead. 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 apply it in two case studies: the first to select between heuristics for choosing a CAD variable ordering; the second to identify when a CAD problem instance would benefit from Groebner Basis preconditioning. These appear to be the first such applications of machine learning to Symbolic Computation. We demonstrate in both cases that the machine learned choice outperforms human developed heuristics.This work was supported by EPSRC grant EP/J003247/1; the European Union’s Horizon 2020 research and innovation programme under grant agreement No 712689 (SC2); and the China Scholarship
Council (CSC)
A Poly-algorithmic Approach to Quantifier Elimination
Cylindrical Algebraic Decomposition (CAD) was the first practical means for
doing real quantifier elimination (QE), and is still a major method, with many
improvements since Collins' original method. Nevertheless, its complexity is
inherently doubly exponential in the number of variables. Where applicable,
virtual term substitution (VTS) is more effective, turning a QE problem in
variables to one in variables in one application, and so on. Hence there
is scope for hybrid methods: doing VTS where possible then using CAD.
This paper describes such a poly-algorithmic implementation, based on the
second author's Ph.D. thesis. The version of CAD used is based on a new
implementation of Lazard's recently-justified method, with some improvements to
handle equational constraints
Cylindrical algebraic decomposition with equational constraints
Cylindrical Algebraic Decomposition (CAD) has long been one of the most
important algorithms within Symbolic Computation, as a tool to perform
quantifier elimination in first order logic over the reals. More recently it is
finding prominence in the Satisfiability Checking community as a tool to
identify satisfying solutions of problems in nonlinear real arithmetic.
The original algorithm produces decompositions according to the signs of
polynomials, when what is usually required is a decomposition according to the
truth of a formula containing those polynomials. One approach to achieve that
coarser (but hopefully cheaper) decomposition is to reduce the polynomials
identified in the CAD to reflect a logical structure which reduces the solution
space dimension: the presence of Equational Constraints (ECs).
This paper may act as a tutorial for the use of CAD with ECs: we describe all
necessary background and the current state of the art. In particular, we
present recent work on how McCallum's theory of reduced projection may be
leveraged to make further savings in the lifting phase: both to the polynomials
we lift with and the cells lifted over. We give a new complexity analysis to
demonstrate that the double exponent in the worst case complexity bound for CAD
reduces in line with the number of ECs. We show that the reduction can apply to
both the number of polynomials produced and their degree.Comment: Accepted into the Journal of Symbolic Computation. arXiv admin note:
text overlap with arXiv:1501.0446