255 research outputs found
New approximations for the cone of copositive matrices and its dual
We provide convergent hierarchies for the cone C of copositive matrices and
its dual, the cone of completely positive matrices. In both cases the
corresponding hierarchy consists of nested spectrahedra and provide outer
(resp. inner) approximations for C (resp. for its dual), thus complementing
previous inner (resp. outer) approximations for C (for the dual). In
particular, both inner and outer approximations have a very simple
interpretation. Finally, extension to K-copositivity and K-complete positivity
for a closed convex cone K, is straightforward.Comment: 8
Computational Approaches to Lattice Packing and Covering Problems
We describe algorithms which address two classical problems in lattice
geometry: the lattice covering and the simultaneous lattice packing-covering
problem. Theoretically our algorithms solve the two problems in any fixed
dimension d in the sense that they approximate optimal covering lattices and
optimal packing-covering lattices within any desired accuracy. Both algorithms
involve semidefinite programming and are based on Voronoi's reduction theory
for positive definite quadratic forms, which describes all possible Delone
triangulations of Z^d.
In practice, our implementations reproduce known results in dimensions d <= 5
and in particular solve the two problems in these dimensions. For d = 6 our
computations produce new best known covering as well as packing-covering
lattices, which are closely related to the lattice (E6)*. For d = 7, 8 our
approach leads to new best known covering lattices. Although we use numerical
methods, we made some effort to transform numerical evidences into rigorous
proofs. We provide rigorous error bounds and prove that some of the new
lattices are locally optimal.Comment: (v3) 40 pages, 5 figures, 6 tables, some corrections, accepted in
Discrete and Computational Geometry, see also
http://fma2.math.uni-magdeburg.de/~latgeo
Semidefinite representation of convex hulls of rational varieties
Using elementary duality properties of positive semidefinite moment matrices
and polynomial sum-of-squares decompositions, we prove that the convex hull of
rationally parameterized algebraic varieties is semidefinite representable
(that is, it can be represented as a projection of an affine section of the
cone of positive semidefinite matrices) in the case of (a) curves; (b)
hypersurfaces parameterized by quadratics; and (c) hypersurfaces parameterized
by bivariate quartics; all in an ambient space of arbitrary dimension
Decomposed Structured Subsets for Semidefinite and Sum-of-Squares Optimization
Semidefinite programs (SDPs) are standard convex problems that are frequently
found in control and optimization applications. Interior-point methods can
solve SDPs in polynomial time up to arbitrary accuracy, but scale poorly as the
size of matrix variables and the number of constraints increases. To improve
scalability, SDPs can be approximated with lower and upper bounds through the
use of structured subsets (e.g., diagonally-dominant and scaled-diagonally
dominant matrices). Meanwhile, any underlying sparsity or symmetry structure
may be leveraged to form an equivalent SDP with smaller positive semidefinite
constraints. In this paper, we present a notion of decomposed structured
subsets}to approximate an SDP with structured subsets after an equivalent
conversion. The lower/upper bounds found by approximation after conversion
become tighter than the bounds obtained by approximating the original SDP
directly. We apply decomposed structured subsets to semidefinite and
sum-of-squares optimization problems with examples of H-infinity norm
estimation and constrained polynomial optimization. An existing basis pursuit
method is adapted into this framework to iteratively refine bounds.Comment: 23 pages, 10 figures, 9 table
Improving Efficiency and Scalability of Sum of Squares Optimization: Recent Advances and Limitations
It is well-known that any sum of squares (SOS) program can be cast as a
semidefinite program (SDP) of a particular structure and that therein lies the
computational bottleneck for SOS programs, as the SDPs generated by this
procedure are large and costly to solve when the polynomials involved in the
SOS programs have a large number of variables and degree. In this paper, we
review SOS optimization techniques and present two new methods for improving
their computational efficiency. The first method leverages the sparsity of the
underlying SDP to obtain computational speed-ups. Further improvements can be
obtained if the coefficients of the polynomials that describe the problem have
a particular sparsity pattern, called chordal sparsity. The second method
bypasses semidefinite programming altogether and relies instead on solving a
sequence of more tractable convex programs, namely linear and second order cone
programs. This opens up the question as to how well one can approximate the
cone of SOS polynomials by second order representable cones. In the last part
of the paper, we present some recent negative results related to this question.Comment: Tutorial for CDC 201
Polynomial-sized Semidefinite Representations of Derivative Relaxations of Spectrahedral Cones
We give explicit polynomial-sized (in and ) semidefinite
representations of the hyperbolicity cones associated with the elementary
symmetric polynomials of degree in variables. These convex cones form a
family of non-polyhedral outer approximations of the non-negative orthant that
preserve low-dimensional faces while successively discarding high-dimensional
faces. More generally we construct explicit semidefinite representations
(polynomial-sized in , and ) of the hyperbolicity cones associated with
th directional derivatives of polynomials of the form where the are symmetric
matrices. These convex cones form an analogous family of outer approximations
to any spectrahedral cone. Our representations allow us to use semidefinite
programming to solve the linear cone programs associated with these convex
cones as well as their (less well understood) dual cones.Comment: 20 pages, 1 figure. Minor changes, expanded proof of Lemma
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