1,274 research outputs found
Improved convergence analysis of Lasserre's measure-based upper bounds for polynomial minimization on compact sets
We consider the problem of computing the minimum value of a
polynomial over a compact set , which can be
reformulated as finding a probability measure on minimizing . Lasserre showed that it suffices to consider such measures of the form
, where is a sum-of-squares polynomial and is a given
Borel measure supported on . By bounding the degree of by one gets
a converging hierarchy of upper bounds for . When is
the hypercube , equipped with the Chebyshev measure, the parameters
are known to converge to at a rate in . We
extend this error estimate to a wider class of convex bodies, while also
allowing for a broader class of reference measures, including the Lebesgue
measure. Our analysis applies to simplices, balls and convex bodies that
locally look like a ball. In addition, we show an error estimate in when satisfies a minor geometrical condition, and in when is a convex body, equipped with the Lebesgue measure. This
improves upon the currently best known error estimates in and
for these two respective cases.Comment: 30 pages with 10 figures. Update notes for second version: Added a
new section containing numerical examples that illustrate the theoretical
results -- Fixed minor mistakes/typos -- Improved some notation -- Clarified
certain explanations in the tex
The quadratic assignment problem is easy for Robinsonian matrices with Toeplitz structure
We present a new polynomially solvable case of the Quadratic Assignment
Problem in Koopmans-Beckman form , by showing that the identity
permutation is optimal when and are respectively a Robinson similarity
and dissimilarity matrix and one of or is a Toeplitz matrix. A Robinson
(dis)similarity matrix is a symmetric matrix whose entries (increase) decrease
monotonically along rows and columns when moving away from the diagonal, and
such matrices arise in the classical seriation problem.Comment: 15 pages, 2 figure
A Sparse Flat Extension Theorem for Moment Matrices
In this note we prove a generalization of the flat extension theorem of Curto
and Fialkow for truncated moment matrices. It applies to moment matrices
indexed by an arbitrary set of monomials and its border, assuming that this set
is connected to 1. When formulated in a basis-free setting, this gives an
equivalent result for truncated Hankel operators
Conic approach to quantum graph parameters using linear optimization over the completely positive semidefinite cone
We investigate the completely positive semidefinite cone ,
a new matrix cone consisting of all matrices that admit a Gram
representation by positive semidefinite matrices (of any size). In particular
we study relationships between this cone and the completely positive and doubly
nonnegative cones, and between its dual cone and trace positive non-commutative
polynomials.
We use this new cone to model quantum analogues of the classical independence
and chromatic graph parameters and , which are roughly
obtained by allowing variables to be positive semidefinite matrices instead of
scalars in the programs defining the classical parameters. We can
formulate these quantum parameters as conic linear programs over the cone
. Using this conic approach we can recover the bounds in
terms of the theta number and define further approximations by exploiting the
link to trace positive polynomials.Comment: Fixed some typo
A new graph parameter related to bounded rank positive semidefinite matrix completions
The Gram dimension \gd(G) of a graph is the smallest integer
such that any partial real symmetric matrix, whose entries are specified on the
diagonal and at the off-diagonal positions corresponding to edges of , can
be completed to a positive semidefinite matrix of rank at most (assuming a
positive semidefinite completion exists). For any fixed the class of graphs
satisfying \gd(G) \le k is minor closed, hence it can characterized by a
finite list of forbidden minors. We show that the only minimal forbidden minor
is for and that there are two minimal forbidden minors:
and for . We also show some close connections to
Euclidean realizations of graphs and to the graph parameter of
\cite{H03}. In particular, our characterization of the graphs with \gd(G)\le
4 implies the forbidden minor characterization of the 3-realizable graphs of
Belk and Connelly \cite{Belk,BC} and of the graphs with of van
der Holst \cite{H03}.Comment: 31 pages, 6 Figures. arXiv admin note: substantial text overlap with
arXiv:1112.596
Perfect Elimination Orderings for Symmetric Matrices
We introduce a new class of structured symmetric matrices by extending the
notion of perfect elimination ordering from graphs to weighted graphs or
matrices. This offers a common framework capturing common vertex elimination
orderings of monotone families of chordal graphs, Robinsonian matrices and
ultrametrics. We give a structural characterization for matrices that admit
perfect elimination orderings in terms of forbidden substructures generalizing
chordless cycles in graphs.Comment: 16 pages, 3 figure
Convergence analysis of a Lasserre hierarchy of upper bounds for polynomial minimization on the sphere
We study the convergence rate of a hierarchy of upper bounds for polynomial
minimization problems, proposed by Lasserre [SIAM J. Optim. 21(3) (2011), pp.
864-885], for the special case when the feasible set is the unit (hyper)sphere.
The upper bound at level r of the hierarchy is defined as the minimal expected
value of the polynomial over all probability distributions on the sphere, when
the probability density function is a sum-of-squares polynomial of degree at
most 2r with respect to the surface measure.
We show that the exact rate of convergence is Theta(1/r^2), and explore the
implications for the related rate of convergence for the generalized problem of
moments on the sphere.Comment: 14 pages, 2 figure
Worst-case examples for Lasserre's measure--based hierarchy for polynomial optimization on the hypercube
We study the convergence rate of a hierarchy of upper bounds for polynomial
optimization problems, proposed by Lasserre [SIAM J. Optim. 21(3) (2011), pp.
864-885], and a related hierarchy by De Klerk, Hess and Laurent [SIAM J. Optim.
27(1), (2017) pp. 347-367]. For polynomial optimization over the hypercube, we
show a refined convergence analysis for the first hierarchy. We also show lower
bounds on the convergence rate for both hierarchies on a class of examples.
These lower bounds match the upper bounds and thus establish the true rate of
convergence on these examples. Interestingly, these convergence rates are
determined by the distribution of extremal zeroes of certain families of
orthogonal polynomials.Comment: 17 pages, no figure
On the closure of the completely positive semidefinite cone and linear approximations to quantum colorings
We investigate structural properties of the completely positive semidefinite
cone , consisting of all the symmetric matrices
that admit a Gram representation by positive semidefinite matrices of any size.
This cone has been introduced to model quantum graph parameters as conic
optimization problems. Recently it has also been used to characterize the set
of bipartite quantum correlations, as projection of an affine
section of it. We have two main results concerning the structure of the
completely positive semidefinite cone, namely about its interior and about its
closure. On the one hand we construct a hierarchy of polyhedral cones which
covers the interior of , which we use for computing some
variants of the quantum chromatic number by way of a linear program. On the
other hand we give an explicit description of the closure of the completely
positive semidefinite cone, by showing that it consists of all matrices
admitting a Gram representation in the tracial ultraproduct of matrix algebras.Comment: 20 page
Forbidden minor characterizations for low-rank optimal solutions to semidefinite programs over the elliptope
We study a new geometric graph parameter \egd(G), defined as the smallest
integer for which any partial symmetric matrix which is completable to
a correlation matrix and whose entries are specified at the positions of the
edges of , can be completed to a matrix in the convex hull of correlation
matrices of \rank at most . This graph parameter is motivated by its
relevance to the problem of finding low rank solutions to semidefinite programs
over the elliptope, and also by its relevance to the bounded rank Grothendieck
constant. Indeed, \egd(G)\le r if and only if the rank- Grothendieck
constant of is equal to 1. We show that the parameter \egd(G) is minor
monotone, we identify several classes of forbidden minors for \egd(G)\le r
and we give the full characterization for the case . We also show an upper
bound for \egd(G) in terms of a new tree-width-like parameter \sla(G),
defined as the smallest for which is a minor of the strong product of a
tree and . We show that, for any 2-connected graph on at
least 6 nodes, \egd(G)\le 2 if and only if \sla(G)\le 2.Comment: 33 pages, 8 Figures. In its second version, the paper has been
modified to accommodate the suggestions of the referees. Furthermore, the
title has been changed since we feel that the new title reflects more
accurately the content and the main results of the pape
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