17,890 research outputs found
Degree of separability of bipartite quantum states
We investigate the problem of finding the optimal convex decomposition of a
bipartite quantum state into a separable part and a positive remainder, in
which the weight of the separable part is maximal. This weight is naturally
identified with the degree of separability of the state. In a recent work, the
problem was solved for two-qubit states using semidefinite programming. In this
paper, we describe a procedure to obtain the optimal decomposition of a
bipartite state of any finite dimension via a sequence of semidefinite
relaxations. The sequence of decompositions thus obtained is shown to converge
to the optimal one. This provides, for the first time, a systematic method to
determine the so-called optimal Lewenstein-Sanpera decomposition of any
bipartite state. Numerical results are provided to illustrate this procedure,
and the special case of rank-2 states is also discussed.Comment: 11 pages, 7 figures, submitted to PR
Covariance Estimation: The GLM and Regularization Perspectives
Finding an unconstrained and statistically interpretable reparameterization
of a covariance matrix is still an open problem in statistics. Its solution is
of central importance in covariance estimation, particularly in the recent
high-dimensional data environment where enforcing the positive-definiteness
constraint could be computationally expensive. We provide a survey of the
progress made in modeling covariance matrices from two relatively complementary
perspectives: (1) generalized linear models (GLM) or parsimony and use of
covariates in low dimensions, and (2) regularization or sparsity for
high-dimensional data. An emerging, unifying and powerful trend in both
perspectives is that of reducing a covariance estimation problem to that of
estimating a sequence of regression problems. We point out several instances of
the regression-based formulation. A notable case is in sparse estimation of a
precision matrix or a Gaussian graphical model leading to the fast graphical
LASSO algorithm. Some advantages and limitations of the regression-based
Cholesky decomposition relative to the classical spectral (eigenvalue) and
variance-correlation decompositions are highlighted. The former provides an
unconstrained and statistically interpretable reparameterization, and
guarantees the positive-definiteness of the estimated covariance matrix. It
reduces the unintuitive task of covariance estimation to that of modeling a
sequence of regressions at the cost of imposing an a priori order among the
variables. Elementwise regularization of the sample covariance matrix such as
banding, tapering and thresholding has desirable asymptotic properties and the
sparse estimated covariance matrix is positive definite with probability
tending to one for large samples and dimensions.Comment: Published in at http://dx.doi.org/10.1214/11-STS358 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Covariant gauge fixing and Kuchar decomposition
The symplectic geometry of a broad class of generally covariant models is
studied. The class is restricted so that the gauge group of the models
coincides with the Bergmann-Komar group and the analysis can focus on the
general covariance. A geometrical definition of gauge fixing at the constraint
manifold is given; it is equivalent to a definition of a background (spacetime)
manifold for each topological sector of a model. Every gauge fixing defines a
decomposition of the constraint manifold into the physical phase space and the
space of embeddings of the Cauchy manifold into the background manifold (Kuchar
decomposition). Extensions of every gauge fixing and the associated Kuchar
decomposition to a neighbourhood of the constraint manifold are shown to exist.Comment: Revtex, 35 pages, no figure
Deriving the Normalized Min-Sum Algorithm from Cooperative Optimization
The normalized min-sum algorithm can achieve near-optimal performance at
decoding LDPC codes. However, it is a critical question to understand the
mathematical principle underlying the algorithm. Traditionally, people thought
that the normalized min-sum algorithm is a good approximation to the
sum-product algorithm, the best known algorithm for decoding LDPC codes and
Turbo codes. This paper offers an alternative approach to understand the
normalized min-sum algorithm. The algorithm is derived directly from
cooperative optimization, a newly discovered general method for
global/combinatorial optimization. This approach provides us another
theoretical basis for the algorithm and offers new insights on its power and
limitation. It also gives us a general framework for designing new decoding
algorithms.Comment: Accepted by IEEE Information Theory Workshop, Chengdu, China, 200
Relative Entropy Relaxations for Signomial Optimization
Signomial programs (SPs) are optimization problems specified in terms of
signomials, which are weighted sums of exponentials composed with linear
functionals of a decision variable. SPs are non-convex optimization problems in
general, and families of NP-hard problems can be reduced to SPs. In this paper
we describe a hierarchy of convex relaxations to obtain successively tighter
lower bounds of the optimal value of SPs. This sequence of lower bounds is
computed by solving increasingly larger-sized relative entropy optimization
problems, which are convex programs specified in terms of linear and relative
entropy functions. Our approach relies crucially on the observation that the
relative entropy function -- by virtue of its joint convexity with respect to
both arguments -- provides a convex parametrization of certain sets of globally
nonnegative signomials with efficiently computable nonnegativity certificates
via the arithmetic-geometric-mean inequality. By appealing to representation
theorems from real algebraic geometry, we show that our sequences of lower
bounds converge to the global optima for broad classes of SPs. Finally, we also
demonstrate the effectiveness of our methods via numerical experiments
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