989 research outputs found
A lower bound on the positive semidefinite rank of convex bodies
The positive semidefinite rank of a convex body is the size of its
smallest positive semidefinite formulation. We show that the positive
semidefinite rank of any convex body is at least where
is the smallest degree of a polynomial that vanishes on the boundary of the
polar of . This improves on the existing bound which relies on results from
quantifier elimination. The proof relies on the B\'ezout bound applied to the
Karush-Kuhn-Tucker conditions of optimality. We discuss the connection with the
algebraic degree of semidefinite programming and show that the bound is tight
(up to constant factor) for random spectrahedra of suitable dimension
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
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
On polyhedral approximations of the positive semidefinite cone
Let be the set of positive semidefinite matrices of trace
equal to one, also known as the set of density matrices. We prove two results
on the hardness of approximating with polytopes. First, we show that if and is an arbitrary matrix of trace equal to one, any
polytope such that must have
linear programming extension complexity at least where is a constant that depends on . Second, we show that any polytope
such that and such that the Gaussian width of is at most
twice the Gaussian width of must have extension complexity at least
. The main ingredient of our proofs is hypercontractivity of
the noise operator on the hypercube.Comment: 12 page
Theta Bodies for Polynomial Ideals
Inspired by a question of Lov\'asz, we introduce a hierarchy of nested
semidefinite relaxations of the convex hull of real solutions to an arbitrary
polynomial ideal, called theta bodies of the ideal. For the stable set problem
in a graph, the first theta body in this hierarchy is exactly Lov\'asz's theta
body of the graph. We prove that theta bodies are, up to closure, a version of
Lasserre's relaxations for real solutions to ideals, and that they can be
computed explicitly using combinatorial moment matrices. Theta bodies provide a
new canonical set of semidefinite relaxations for the max cut problem. For
vanishing ideals of finite point sets, we give several equivalent
characterizations of when the first theta body equals the convex hull of the
points. We also determine the structure of the first theta body for all ideals.Comment: 26 pages, 3 figure
On the existence of 0/1 polytopes with high semidefinite extension complexity
In Rothvo\ss{} it was shown that there exists a 0/1 polytope (a polytope
whose vertices are in \{0,1\}^{n}) such that any higher-dimensional polytope
projecting to it must have 2^{\Omega(n)} facets, i.e., its linear extension
complexity is exponential. The question whether there exists a 0/1 polytope
with high PSD extension complexity was left open. We answer this question in
the affirmative by showing that there is a 0/1 polytope such that any
spectrahedron projecting to it must be the intersection of a semidefinite cone
of dimension~2^{\Omega(n)} and an affine space. Our proof relies on a new
technique to rescale semidefinite factorizations
Semidefinite descriptions of the convex hull of rotation matrices
We study the convex hull of , thought of as the set of
orthogonal matrices with unit determinant, from the point of view of
semidefinite programming. We show that the convex hull of is doubly
spectrahedral, i.e. both it and its polar have a description as the
intersection of a cone of positive semidefinite matrices with an affine
subspace. Our spectrahedral representations are explicit, and are of minimum
size, in the sense that there are no smaller spectrahedral representations of
these convex bodies.Comment: 29 pages, 1 figur
Approximating Hereditary Discrepancy via Small Width Ellipsoids
The Discrepancy of a hypergraph is the minimum attainable value, over
two-colorings of its vertices, of the maximum absolute imbalance of any
hyperedge. The Hereditary Discrepancy of a hypergraph, defined as the maximum
discrepancy of a restriction of the hypergraph to a subset of its vertices, is
a measure of its complexity. Lovasz, Spencer and Vesztergombi (1986) related
the natural extension of this quantity to matrices to rounding algorithms for
linear programs, and gave a determinant based lower bound on the hereditary
discrepancy. Matousek (2011) showed that this bound is tight up to a
polylogarithmic factor, leaving open the question of actually computing this
bound. Recent work by Nikolov, Talwar and Zhang (2013) showed a polynomial time
-approximation to hereditary discrepancy, as a by-product
of their work in differential privacy. In this paper, we give a direct simple
-approximation algorithm for this problem. We show that up to
this approximation factor, the hereditary discrepancy of a matrix is
characterized by the optimal value of simple geometric convex program that
seeks to minimize the largest norm of any point in a ellipsoid
containing the columns of . This characterization promises to be a useful
tool in discrepancy theory
- …