71 research outputs found
Smaller SDP for SOS Decomposition
A popular numerical method to compute SOS (sum of squares of polynomials)
decompositions for polynomials is to transform the problem into semi-definite
programming (SDP) problems and then solve them by SDP solvers. In this paper,
we focus on reducing the sizes of inputs to SDP solvers to improve the
efficiency and reliability of those SDP based methods. Two types of
polynomials, convex cover polynomials and split polynomials, are defined. A
convex cover polynomial or a split polynomial can be decomposed into several
smaller sub-polynomials such that the original polynomial is SOS if and only if
the sub-polynomials are all SOS. Thus the original SOS problem can be
decomposed equivalently into smaller sub-problems. It is proved that convex
cover polynomials are split polynomials and it is quite possible that sparse
polynomials with many variables are split polynomials, which can be efficiently
detected in practice. Some necessary conditions for polynomials to be SOS are
also given, which can help refute quickly those polynomials which have no SOS
representations so that SDP solvers are not called in this case. All the new
results lead to a new SDP based method to compute SOS decompositions, which
improves this kind of methods by passing smaller inputs to SDP solvers in some
cases. Experiments show that the number of monomials obtained by our program is
often smaller than that by other SDP based software, especially for polynomials
with many variables and high degrees. Numerical results on various tests are
reported to show the performance of our program.Comment: 18 page
Termination of Linear Programs with Nonlinear Constraints
Tiwari proved that termination of linear programs (loops with linear loop
conditions and updates) over the reals is decidable through Jordan forms and
eigenvectors computation. Braverman proved that it is also decidable over the
integers. In this paper, we consider the termination of loops with polynomial
loop conditions and linear updates over the reals and integers. First, we prove
that the termination of such loops over the integers is undecidable. Second,
with an assumption, we provide an complete algorithm to decide the termination
of a class of such programs over the reals. Our method is similar to that of
Tiwari in spirit but uses different techniques. Finally, we conjecture that the
termination of linear programs with polynomial loop conditions over the reals
is undecidable in general by %constructing a loop and reducing the problem to
another decision problem related to number theory and ergodic theory, which we
guess undecidable.Comment: 17pages, 0 figure
Generic Regular Decompositions for Parametric Polynomial Systems
This paper presents a generalization of our earlier work in [19]. In this
paper, the two concepts, generic regular decomposition (GRD) and
regular-decomposition-unstable (RDU) variety introduced in [19] for generic
zero-dimensional systems, are extended to the case where the parametric systems
are not necessarily zero-dimensional. An algorithm is provided to compute GRDs
and the associated RDU varieties of parametric systems simultaneously on the
basis of the algorithm for generic zero-dimensional systems proposed in [19].
Then the solutions of any parametric system can be represented by the solutions
of finitely many regular systems and the decomposition is stable at any
parameter value in the complement of the associated RDU variety of the
parameter space. The related definitions and the results presented in [19] are
also generalized and a further discussion on RDU varieties is given from an
experimental point of view. The new algorithm has been implemented on the basis
of DISCOVERER with Maple 16 and experimented with a number of benchmarks from
the literature.Comment: It is the latest version. arXiv admin note: text overlap with
arXiv:1208.611
Special Algorithm for Stability Analysis of Multistable Biological Regulatory Systems
We consider the problem of counting (stable) equilibriums of an important
family of algebraic differential equations modeling multistable biological
regulatory systems. The problem can be solved, in principle, using real
quantifier elimination algorithms, in particular real root classification
algorithms. However, it is well known that they can handle only very small
cases due to the enormous computing time requirements. In this paper, we
present a special algorithm which is much more efficient than the general
methods. Its efficiency comes from the exploitation of certain interesting
structures of the family of differential equations.Comment: 24 pages, 5 algorithms, 10 figure
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