1,530 research outputs found
A globally convergent matricial algorithm for multivariate spectral estimation
In this paper, we first describe a matricial Newton-type algorithm designed
to solve the multivariable spectrum approximation problem. We then prove its
global convergence. Finally, we apply this approximation procedure to
multivariate spectral estimation, and test its effectiveness through
simulation. Simulation shows that, in the case of short observation records,
this method may provide a valid alternative to standard multivariable
identification techniques such as MATLAB's PEM and MATLAB's N4SID
Using Optimization to Obtain a Width-Independent, Parallel, Simpler, and Faster Positive SDP Solver
We study the design of polylogarithmic depth algorithms for approximately
solving packing and covering semidefinite programs (or positive SDPs for
short). This is a natural SDP generalization of the well-studied positive LP
problem.
Although positive LPs can be solved in polylogarithmic depth while using only
parallelizable iterations, the best known
positive SDP solvers due to Jain and Yao require parallelizable iterations. Several alternative solvers have
been proposed to reduce the exponents in the number of iterations. However, the
correctness of the convergence analyses in these works has been called into
question, as they both rely on algebraic monotonicity properties that do not
generalize to matrix algebra.
In this paper, we propose a very simple algorithm based on the optimization
framework proposed for LP solvers. Our algorithm only needs iterations, matching that of the best LP solver. To surmount
the obstacles encountered by previous approaches, our analysis requires a new
matrix inequality that extends Lieb-Thirring's inequality, and a
sign-consistent, randomized variant of the gradient truncation technique
proposed in
On the Geometry of Maximum Entropy Problems
We show that a simple geometric result suffices to derive the form of the
optimal solution in a large class of finite and infinite-dimensional maximum
entropy problems concerning probability distributions, spectral densities and
covariance matrices. These include Burg's spectral estimation method and
Dempster's covariance completion, as well as various recent generalizations of
the above. We then apply this orthogonality principle to the new problem of
completing a block-circulant covariance matrix when an a priori estimate is
available.Comment: 22 page
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