11 research outputs found
Waring-like decompositions of polynomials - 1
Let be a homogeneous form of degree in variables. A Waring
decomposition of is a way to express as a sum of powers of
linear forms. In this paper we consider the decompositions of a form as a sum
of expressions, each of which is a fixed monomial evaluated at linear forms.Comment: 12 pages; Section 5 added in this versio
Region of Attraction Estimation Using Invariant Sets and Rational Lyapunov Functions
This work addresses the problem of estimating the region of attraction (RA)
of equilibrium points of nonlinear dynamical systems. The estimates we provide
are given by positively invariant sets which are not necessarily defined by
level sets of a Lyapunov function. Moreover, we present conditions for the
existence of Lyapunov functions linked to the positively invariant set
formulation we propose. Connections to fundamental results on estimates of the
RA are presented and support the search of Lyapunov functions of a rational
nature. We then restrict our attention to systems governed by polynomial vector
fields and provide an algorithm that is guaranteed to enlarge the estimate of
the RA at each iteration
Expressing a General Form as a Sum of Determinants
Let A= (a_{ij}) be a non-negative integer k x k matrix. A is a homogeneous
matrix if a_{ij} + a_{kl}=a_{il} + a_{kj} for any choice of the four indexes.
We ask: If A is a homogeneous matrix and if F is a form in C[x_1, \dots x_n]
with deg(F) = trace(A), what is the least integer, s(A), so that F = det M_1 +
... + det M_{s(A)}, where the M_i's are k x k matrices of forms with degree
matrix A? We consider this problem for n>3 and we prove that s(A) is at most
k^{n-3} and s(A) <k^{n-3} in infinitely many cases. However s(A) = k^{n-3} when
the entries of A are large with respect to k
Convex computation of the region of attraction of polynomial control systems
We address the long-standing problem of computing the region of attraction
(ROA) of a target set (e.g., a neighborhood of an equilibrium point) of a
controlled nonlinear system with polynomial dynamics and semialgebraic state
and input constraints. We show that the ROA can be computed by solving an
infinite-dimensional convex linear programming (LP) problem over the space of
measures. In turn, this problem can be solved approximately via a classical
converging hierarchy of convex finite-dimensional linear matrix inequalities
(LMIs). Our approach is genuinely primal in the sense that convexity of the
problem of computing the ROA is an outcome of optimizing directly over system
trajectories. The dual infinite-dimensional LP on nonnegative continuous
functions (approximated by polynomial sum-of-squares) allows us to generate a
hierarchy of semialgebraic outer approximations of the ROA at the price of
solving a sequence of LMI problems with asymptotically vanishing conservatism.
This sharply contrasts with the existing literature which follows an
exclusively dual Lyapunov approach yielding either nonconvex bilinear matrix
inequalities or conservative LMI conditions. The approach is simple and readily
applicable as the outer approximations are the outcome of a single semidefinite
program with no additional data required besides the problem description
Zero-Convex Functions, Perturbation Resilience, and Subgradient Projections for Feasibility-Seeking Methods
The convex feasibility problem (CFP) is at the core of the modeling of many
problems in various areas of science. Subgradient projection methods are
important tools for solving the CFP because they enable the use of subgradient
calculations instead of orthogonal projections onto the individual sets of the
problem. Working in a real Hilbert space, we show that the sequential
subgradient projection method is perturbation resilient. By this we mean that
under appropriate conditions the sequence generated by the method converges
weakly, and sometimes also strongly, to a point in the intersection of the
given subsets of the feasibility problem, despite certain perturbations which
are allowed in each iterative step. Unlike previous works on solving the convex
feasibility problem, the involved functions, which induce the feasibility
problem's subsets, need not be convex. Instead, we allow them to belong to a
wider and richer class of functions satisfying a weaker condition, that we call
"zero-convexity". This class, which is introduced and discussed here, holds a
promise to solve optimization problems in various areas, especially in
non-smooth and non-convex optimization. The relevance of this study to
approximate minimization and to the recent superiorization methodology for
constrained optimization is explained.Comment: Mathematical Programming Series A, accepted for publicatio
Inner approximations for polynomial matrix inequalities and robust stability regions
A mistake is fixed in the proof of Lemma 1. It does not affect the remainder of the paper.International audienceFollowing a polynomial approach, many robust fixed-order controller design problems can be formulated as optimization problems whose set of feasible solutions is modelled by parametrized polynomial matrix inequalities (PMI). These feasibility sets are typically nonconvex. Given a parametrized PMI set, we provide a hierarchy of linear matrix inequality (LMI) problems whose optimal solutions generate inner approximations modelled by a single polynomial sublevel set. Those inner approximations converge in a strong analytic sense to the nonconvex original feasible set, with asymptotically vanishing conservatism. One may also impose the hierarchy of inner approximations to be nested or convex. In the latter case they do not converge any more to the feasible set, but they can be used in a convex optimization framework at the price of some conservatism. Finally, we show that the specific geometry of nonconvex polynomial stability regions can be exploited to improve convergence of the hierarchy of inner approximations