10,595 research outputs found
Computation of sum of squares polynomials from data points
We propose an iterative algorithm for the numerical computation of sums of
squares of polynomials approximating given data at prescribed interpolation
points. The method is based on the definition of a convex functional
arising from the dualization of a quadratic regression over the Cholesky
factors of the sum of squares decomposition. In order to justify the
construction, the domain of , the boundary of the domain and the behavior at
infinity are analyzed in details. When the data interpolate a positive
univariate polynomial, we show that in the context of the Lukacs sum of squares
representation, is coercive and strictly convex which yields a unique
critical point and a corresponding decomposition in sum of squares. For
multivariate polynomials which admit a decomposition in sum of squares and up
to a small perturbation of size , is always
coercive and so it minimum yields an approximate decomposition in sum of
squares. Various unconstrained descent algorithms are proposed to minimize .
Numerical examples are provided, for univariate and bivariate polynomials
Efficient Algorithms for Optimal Control of Quantum Dynamics: The "Krotov'' Method unencumbered
Efficient algorithms for the discovery of optimal control designs for
coherent control of quantum processes are of fundamental importance. One
important class of algorithms are sequential update algorithms generally
attributed to Krotov. Although widely and often successfully used, the
associated theory is often involved and leaves many crucial questions
unanswered, from the monotonicity and convergence of the algorithm to
discretization effects, leading to the introduction of ad-hoc penalty terms and
suboptimal update schemes detrimental to the performance of the algorithm. We
present a general framework for sequential update algorithms including specific
prescriptions for efficient update rules with inexpensive dynamic search length
control, taking into account discretization effects and eliminating the need
for ad-hoc penalty terms. The latter, while necessary to regularize the problem
in the limit of infinite time resolution, i.e., the continuum limit, are shown
to be undesirable and unnecessary in the practically relevant case of finite
time resolution. Numerical examples show that the ideas underlying many of
these results extend even beyond what can be rigorously proved.Comment: 19 pages, many figure
The EM Algorithm
The Expectation-Maximization (EM) algorithm is a broadly applicable approach to the iterative computation of maximum likelihood (ML) estimates, useful in a variety of incomplete-data problems. Maximum likelihood estimation and likelihood-based inference are of central importance in statistical theory and data analysis. Maximum likelihood estimation is a general-purpose method with attractive properties. It is the most-often used estimation technique in the frequentist framework; it is also relevant in the Bayesian framework (Chapter III.11). Often Bayesian solutions are justified with the help of likelihoods and maximum likelihood estimates (MLE), and Bayesian solutions are similar to penalized likelihood estimates. Maximum likelihood estimation is an ubiquitous technique and is used extensively in every area where statistical techniques are used. --
On nonparametric estimation of a mixing density via the predictive recursion algorithm
Nonparametric estimation of a mixing density based on observations from the
corresponding mixture is a challenging statistical problem. This paper surveys
the literature on a fast, recursive estimator based on the predictive recursion
algorithm. After introducing the algorithm and giving a few examples, I
summarize the available asymptotic convergence theory, describe an important
semiparametric extension, and highlight two interesting applications. I
conclude with a discussion of several recent developments in this area and some
open problems.Comment: 22 pages, 5 figures. Comments welcome at
https://www.researchers.one/article/2018-12-
- âŠ