38,403 research outputs found
Analysis of switched and hybrid systems - beyond piecewise quadratic methods
This paper presents a method for stability analysis of switched and hybrid systems using polynomial and piecewise polynomial Lyapunov functions. Computation of such functions can be performed using convex optimization, based on the sum of squares decomposition of multivariate polynomials. The analysis yields several improvements over previous methods and opens up new possibilities, including the possibility of treating nonlinear vector fields and/or switching surfaces and parametric robustness analysis in a unified way
Partitioning Procedure for Polynomial Optimization: Application to Portfolio Decisions with Higher Order Moments
We consider the problem of finding the minimum of a real-valued multivariate polynomial function constrained in a compact set defined by polynomial inequalities and equalities. This problem, called polynomial optimization problem (POP), is generally nonconvex and has been of growing interest to many researchers in recent years. Our goal is to tackle POPs using decomposition. Towards this goal we introduce a partitioning procedure. The problem manipulations are in line with the pattern used in the Benders decomposition [1], namely relaxation preceded by projection. Stengle’s and Putinar’s Positivstellensatz are employed to derive the so-called feasibility and optimality constraints, respectively. We test the performance of the proposed method on a collection of benchmark problems and we present the numerical results. As an application, we consider the problem of selecting an investment portfolio optimizing the mean, variance, skewness and kurtosis of the portfolio.Polynomial optimization, Semidefinite relaxations, Positivstellensatz, Sum of squares, Benders decomposition, Portfolio optimization
Resultant of an equivariant polynomial system with respect to the symmetric group
Given a system of n homogeneous polynomials in n variables which is
equivariant with respect to the canonical actions of the symmetric group of n
symbols on the variables and on the polynomials, it is proved that its
resultant can be decomposed into a product of several smaller resultants that
are given in terms of some divided differences. As an application, we obtain a
decomposition formula for the discriminant of a multivariate homogeneous
symmetric polynomial
On Difference-of-SOS and Difference-of-Convex-SOS Decompositions for Polynomials
In this paper, we are interested in developing polynomial decomposition
techniques to reformulate real valued multivariate polynomials into
difference-of-sums-of-squares (namely, D-SOS) and
difference-of-convex-sums-of-squares (namely, DC-SOS). Firstly, we prove that
the set of D-SOS and DC-SOS polynomials are vector spaces and equivalent to the
set of real valued polynomials. Moreover, the problem of finding D-SOS and
DC-SOS decompositions are equivalent to semidefinite programs (SDP) which can
be solved to any desired precision in polynomial time. Some important algebraic
properties and the relationships among the set of sums-of-squares (SOS)
polynomials, positive semidefinite (PSD) polynomials, convex-sums-of-squares
(CSOS) polynomials, SOS-convex polynomials, D-SOS and DC-SOS polynomials are
discussed. Secondly, we focus on establishing several practical algorithms for
constructing D-SOS and DC-SOS decompositions for any polynomial without solving
SDP. Using DC-SOS decomposition, we can reformulate polynomial optimization
problems in the realm of difference-of-convex (DC) programming, which can be
handled by efficient DC programming approaches. Some examples illustrate how to
use our methods for constructing D-SOS and DC-SOS decompositions. Numerical
performance of D-SOS and DC-SOS decomposition algorithms and their parallelized
methods are tested on a synthetic dataset with 1750 randomly generated large
and small sized sparse and dense polynomials. Some real-world applications in
higher order moment portfolio optimization problems, eigenvalue complementarity
problems, Euclidean distance matrix completion problems, and Boolean polynomial
programs are also presented.Comment: 47 pages, 19 figure
Integrand Reduction for Two-Loop Scattering Amplitudes through Multivariate Polynomial Division
We describe the application of a novel approach for the reduction of
scattering amplitudes, based on multivariate polynomial division, which we have
recently presented. This technique yields the complete integrand decomposition
for arbitrary amplitudes, regardless of the number of loops. It allows for the
determination of the residue at any multiparticle cut, whose knowledge is a
mandatory prerequisite for applying the integrand-reduction procedure. By using
the division modulo Groebner basis, we can derive a simple integrand recurrence
relation that generates the multiparticle pole decomposition for integrands of
arbitrary multiloop amplitudes. We apply the new reduction algorithm to the
two-loop planar and nonplanar diagrams contributing to the five-point
scattering amplitudes in N=4 super Yang-Mills and N=8 supergravity in four
dimensions, whose numerator functions contain up to rank-two terms in the
integration momenta. We determine all polynomial residues parametrizing the
cuts of the corresponding topologies and subtopologies. We obtain the integral
basis for the decomposition of each diagram from the polynomial form of the
residues. Our approach is well suited for a seminumerical implementation, and
its general mathematical properties provide an effective algorithm for the
generalization of the integrand-reduction method to all orders in perturbation
theory.Comment: 32 pages, 4 figures. v2: published version, text improved, new
subsection 4.4 adde
Symmetric tensor decomposition
We present an algorithm for decomposing a symmetric tensor, of dimension n
and order d as a sum of rank-1 symmetric tensors, extending the algorithm of
Sylvester devised in 1886 for binary forms. We recall the correspondence
between the decomposition of a homogeneous polynomial in n variables of total
degree d as a sum of powers of linear forms (Waring's problem), incidence
properties on secant varieties of the Veronese Variety and the representation
of linear forms as a linear combination of evaluations at distinct points. Then
we reformulate Sylvester's approach from the dual point of view. Exploiting
this duality, we propose necessary and sufficient conditions for the existence
of such a decomposition of a given rank, using the properties of Hankel (and
quasi-Hankel) matrices, derived from multivariate polynomials and normal form
computations. This leads to the resolution of polynomial equations of small
degree in non-generic cases. We propose a new algorithm for symmetric tensor
decomposition, based on this characterization and on linear algebra
computations with these Hankel matrices. The impact of this contribution is
two-fold. First it permits an efficient computation of the decomposition of any
tensor of sub-generic rank, as opposed to widely used iterative algorithms with
unproved global convergence (e.g. Alternate Least Squares or gradient
descents). Second, it gives tools for understanding uniqueness conditions, and
for detecting the rank
On Multivariate Cryptosystems Based on Computable Maps with Invertible Decomposition
Let K be a commutative ring and K^n be a space over K of dimension n. Weintroduce the concept of a family of multivariate maps f(n) of K^n into itself with invertible decomposition.If f(n) is computable in polynomial time then it can be used as the public rule and theinvertible decomposition provides a private key in f(n) based public key infrastructure. Requirementsof polynomial ity of degree and density for f(n) allow to estimate the complexity of encryption procedurefor a public user. The concepts of a stable family and a family of increasing order are motivatedby the studies of discrete logarithm problem in the Cremona group. The statement on the existenceof families of multivariate maps of polynomial degree and polynomial density of increasing order withthe invertible decomposition is proved. The proof is supported by explicite construction which canbe used as a new cryptosystem. The presented multivariate encryption maps are induced by specialwalks in the algebraically dened extremal graphs A(n;K) and D(n;K) of increasing girth
Reconstruction Algorithms for Sums of Affine Powers
In this paper we study sums of powers of affine functions in (mostly) one
variable. Although quite simple, this model is a generalization of two
well-studied models: Waring decomposition and sparsest shift. For these three
models there are natural extensions to several variables, but this paper is
mostly focused on univariate polynomials. We present structural results which
compare the expressive power of the three models; and we propose algorithms
that find the smallest decomposition of f in the first model (sums of affine
powers) for an input polynomial f given in dense representation. We also begin
a study of the multivariate case. This work could be extended in several
directions. In particular, just as for Sparsest Shift and Waring decomposition,
one could consider extensions to "supersparse" polynomials and attempt a fuller
study of the multi-variate case. We also point out that the basic univariate
problem studied in the present paper is far from completely solved: our
algorithms all rely on some assumptions for the exponents in an optimal
decomposition, and some algorithms also rely on a distinctness assumption for
the shifts. It would be very interesting to weaken these assumptions, or even
to remove them entirely. Another related and poorly understood issue is that of
the bit size of the constants appearing in an optimal decomposition: is it
always polynomially related to the bit size of the input polynomial given in
dense representation?Comment: This version improves on several algorithmic result
Computation of sum of squares polynomials from data points
We propose an iterative algorithm for the numerical computation of sums of
squares of polynomials approximating given data at prescribed interpolation
points. The method is based on the definition of a convex functional
arising from the dualization of a quadratic regression over the Cholesky
factors of the sum of squares decomposition. In order to justify the
construction, the domain of , the boundary of the domain and the behavior at
infinity are analyzed in details. When the data interpolate a positive
univariate polynomial, we show that in the context of the Lukacs sum of squares
representation, is coercive and strictly convex which yields a unique
critical point and a corresponding decomposition in sum of squares. For
multivariate polynomials which admit a decomposition in sum of squares and up
to a small perturbation of size , is always
coercive and so it minimum yields an approximate decomposition in sum of
squares. Various unconstrained descent algorithms are proposed to minimize .
Numerical examples are provided, for univariate and bivariate polynomials
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