12,205 research outputs found
On semidefinite representations of plane quartics
This note focuses on the problem of representing convex sets as projections
of the cone of positive semidefinite matrices, in the particular case of sets
generated by bivariate polynomials of degree four. Conditions are given for the
convex hull of a plane quartic to be exactly semidefinite representable with at
most 12 lifting variables. If the quartic is rationally parametrizable, an
exact semidefinite representation with 2 lifting variables can be obtained.
Various numerical examples illustrate the techniques and suggest further
research directions
Dualities in Convex Algebraic Geometry
Convex algebraic geometry concerns the interplay between optimization theory
and real algebraic geometry. Its objects of study include convex semialgebraic
sets that arise in semidefinite programming and from sums of squares. This
article compares three notions of duality that are relevant in these contexts:
duality of convex bodies, duality of projective varieties, and the
Karush-Kuhn-Tucker conditions derived from Lagrange duality. We show that the
optimal value of a polynomial program is an algebraic function whose minimal
polynomial is expressed by the hypersurface projectively dual to the constraint
set. We give an exposition of recent results on the boundary structure of the
convex hull of a compact variety, we contrast this to Lasserre's representation
as a spectrahedral shadow, and we explore the geometric underpinnings of
semidefinite programming duality.Comment: 48 pages, 11 figure
Convergence of the Lasserre Hierarchy of SDP Relaxations for Convex Polynomial Programs without Compactness
The Lasserre hierarchy of semidefinite programming (SDP) relaxations is an
effective scheme for finding computationally feasible SDP approximations of
polynomial optimization over compact semi-algebraic sets. In this paper, we
show that, for convex polynomial optimization, the Lasserre hierarchy with a
slightly extended quadratic module always converges asymptotically even in the
face of non-compact semi-algebraic feasible sets. We do this by exploiting a
coercivity property of convex polynomials that are bounded below. We further
establish that the positive definiteness of the Hessian of the associated
Lagrangian at a saddle-point (rather than the objective function at each
minimizer) guarantees finite convergence of the hierarchy. We obtain finite
convergence by first establishing a new sum-of-squares polynomial
representation of convex polynomials over convex semi-algebraic sets under a
saddle-point condition. We finally prove that the existence of a saddle-point
of the Lagrangian for a convex polynomial program is also necessary for the
hierarchy to have finite convergence.Comment: 17 page
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
Lifts of convex sets and cone factorizations
In this paper we address the basic geometric question of when a given convex
set is the image under a linear map of an affine slice of a given closed convex
cone. Such a representation or 'lift' of the convex set is especially useful if
the cone admits an efficient algorithm for linear optimization over its affine
slices. We show that the existence of a lift of a convex set to a cone is
equivalent to the existence of a factorization of an operator associated to the
set and its polar via elements in the cone and its dual. This generalizes a
theorem of Yannakakis that established a connection between polyhedral lifts of
a polytope and nonnegative factorizations of its slack matrix. Symmetric lifts
of convex sets can also be characterized similarly. When the cones live in a
family, our results lead to the definition of the rank of a convex set with
respect to this family. We present results about this rank in the context of
cones of positive semidefinite matrices. Our methods provide new tools for
understanding cone lifts of convex sets.Comment: 20 pages, 2 figure
On semidefinite representations of non-closed sets
Spectrahedra are sets defined by linear matrix inequalities. Projections of
spectrahedra are called semidefinitely representable sets. Both kinds of sets
are of practical use in polynomial optimization, since they occur as feasible
sets in semidefinite programming. There are several recent results on the
question which sets are semidefinite representable. So far, all results focus
on the case of closed sets. In this work we develop a new method to prove
semidefinite representability of sets which are not closed. For example, the
interior of a semidefinite representable set is shown to be semidefinite
representable. More general, one can remove faces of a semidefinite
representable set and preserve semidefinite representability, as long as the
faces are parametrized in a suitable way.Comment: 13 page
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