675 research outputs found
Convex inner approximations of nonconvex semialgebraic sets applied to fixed-order controller design
We describe an elementary algorithm to build convex inner approximations of
nonconvex sets. Both input and output sets are basic semialgebraic sets given
as lists of defining multivariate polynomials. Even though no optimality
guarantees can be given (e.g. in terms of volume maximization for bounded
sets), the algorithm is designed to preserve convex boundaries as much as
possible, while removing regions with concave boundaries. In particular, the
algorithm leaves invariant a given convex set. The algorithm is based on
Gloptipoly 3, a public-domain Matlab package solving nonconvex polynomial
optimization problems with the help of convex semidefinite programming
(optimization over linear matrix inequalities, or LMIs). We illustrate how the
algorithm can be used to design fixed-order controllers for linear systems,
following a polynomial approach
Matrix Convex Hulls of Free Semialgebraic Sets
This article resides in the realm of the noncommutative (free) analog of real
algebraic geometry - the study of polynomial inequalities and equations over
the real numbers - with a focus on matrix convex sets and their projections
. A free semialgebraic set which is convex as well as bounded and open
can be represented as the solution set of a Linear Matrix Inequality (LMI), a
result which suggests that convex free semialgebraic sets are rare. Further,
Tarski's transfer principle fails in the free setting: The projection of a free
convex semialgebraic set need not be free semialgebraic. Both of these results,
and the importance of convex approximations in the optimization community,
provide impetus and motivation for the study of the free (matrix) convex hull
of free semialgebraic sets.
This article presents the construction of a sequence of LMI domains
in increasingly many variables whose projections are
successively finer outer approximations of the matrix convex hull of a free
semialgebraic set . It is based on free analogs of
moments and Hankel matrices. Such an approximation scheme is possibly the best
that can be done in general. Indeed, natural noncommutative transcriptions of
formulas for certain well known classical (commutative) convex hulls does not
produce the convex hulls in the free case. This failure is illustrated on one
of the simplest free nonconvex .
A basic question is which free sets are the projection of a free
semialgebraic set ? Techniques and results of this paper bear upon this
question which is open even for convex sets.Comment: 41 pages; includes table of contents; supplementary material (a
Mathematica notebook) can be found at
http://www.math.auckland.ac.nz/~igorklep/publ.htm
Exposed faces of semidefinitely representable sets
A linear matrix inequality (LMI) is a condition stating that a symmetric
matrix whose entries are affine linear combinations of variables is positive
semidefinite. Motivated by the fact that diagonal LMIs define polyhedra, the
solution set of an LMI is called a spectrahedron. Linear images of spectrahedra
are called semidefinite representable sets. Part of the interest in
spectrahedra and semidefinite representable sets arises from the fact that one
can efficiently optimize linear functions on them by semidefinite programming,
like one can do on polyhedra by linear programming.
It is known that every face of a spectrahedron is exposed. This is also true
in the general context of rigidly convex sets. We study the same question for
semidefinite representable sets. Lasserre proposed a moment matrix method to
construct semidefinite representations for certain sets. Our main result is
that this method can only work if all faces of the considered set are exposed.
This necessary condition complements sufficient conditions recently proved by
Lasserre, Helton and Nie
A Complete Characterization of the Gap between Convexity and SOS-Convexity
Our first contribution in this paper is to prove that three natural sum of
squares (sos) based sufficient conditions for convexity of polynomials, via the
definition of convexity, its first order characterization, and its second order
characterization, are equivalent. These three equivalent algebraic conditions,
henceforth referred to as sos-convexity, can be checked by semidefinite
programming whereas deciding convexity is NP-hard. If we denote the set of
convex and sos-convex polynomials in variables of degree with
and respectively, then our main
contribution is to prove that if and
only if or or . We also present a complete
characterization for forms (homogeneous polynomials) except for the case
which is joint work with G. Blekherman and is to be published
elsewhere. Our result states that the set of convex forms in
variables of degree equals the set of sos-convex forms if
and only if or or . To prove these results, we present
in particular explicit examples of polynomials in
and
and forms in
and , and a
general procedure for constructing forms in from nonnegative but not sos forms in variables and degree .
Although for disparate reasons, the remarkable outcome is that convex
polynomials (resp. forms) are sos-convex exactly in cases where nonnegative
polynomials (resp. forms) are sums of squares, as characterized by Hilbert.Comment: 25 pages; minor editorial revisions made; formal certificates for
computer assisted proofs of the paper added to arXi
The convex Positivstellensatz in a free algebra
Given a monic linear pencil L in g variables let D_L be its positivity
domain, i.e., the set of all g-tuples X of symmetric matrices of all sizes
making L(X) positive semidefinite. Because L is a monic linear pencil, D_L is
convex with interior, and conversely it is known that convex bounded
noncommutative semialgebraic sets with interior are all of the form D_L. The
main result of this paper establishes a perfect noncommutative
Nichtnegativstellensatz on a convex semialgebraic set. Namely, a noncommutative
polynomial p is positive semidefinite on D_L if and only if it has a weighted
sum of squares representation with optimal degree bounds: p = s^* s + \sum_j
f_j^* L f_j, where s, f_j are vectors of noncommutative polynomials of degree
no greater than 1/2 deg(p). This noncommutative result contrasts sharply with
the commutative setting, where there is no control on the degrees of s, f_j and
assuming only p nonnegative, as opposed to p strictly positive, yields a clean
Positivstellensatz so seldom that such cases are noteworthy.Comment: 22 page
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