785 research outputs found
Algebraic Parameter Estimation of Damped Exponentials
International audienceThe parameter estimation of a sum of exponentials or the exponential fitting of data is a well known problem with a rich history. It is a nonlinear problem which presents several difficulties as the ill-conditioning when roots have close values and the order of the estimated parameters, among others. One of the best existing methods is the modified Prony algorithm which suffers in the presence of noise. In this paper we propose an algebraic method for the parameter estimation. The method, differently from the modified Prony method, is considerably robust to noise. The comparison of both through simulations confirm the good performance of the algebraic method
Algebraic Parameter Estimation of Damped Exponentials
International audienceThe parameter estimation of a sum of exponentials or the exponential fitting of data is a well known problem with a rich history. It is a nonlinear problem which presents several difficulties as the ill-conditioning when roots have close values and the order of the estimated parameters, among others. One of the best existing methods is the modified Prony algorithm which suffers in the presence of noise. In this paper we propose an algebraic method for the parameter estimation. The method, differently from the modified Prony method, is considerably robust to noise. The comparison of both through simulations confirm the good performance of the algebraic method
Fast and scalable Gaussian process modeling with applications to astronomical time series
The growing field of large-scale time domain astronomy requires methods for
probabilistic data analysis that are computationally tractable, even with large
datasets. Gaussian Processes are a popular class of models used for this
purpose but, since the computational cost scales, in general, as the cube of
the number of data points, their application has been limited to small
datasets. In this paper, we present a novel method for Gaussian Process
modeling in one-dimension where the computational requirements scale linearly
with the size of the dataset. We demonstrate the method by applying it to
simulated and real astronomical time series datasets. These demonstrations are
examples of probabilistic inference of stellar rotation periods, asteroseismic
oscillation spectra, and transiting planet parameters. The method exploits
structure in the problem when the covariance function is expressed as a mixture
of complex exponentials, without requiring evenly spaced observations or
uniform noise. This form of covariance arises naturally when the process is a
mixture of stochastically-driven damped harmonic oscillators -- providing a
physical motivation for and interpretation of this choice -- but we also
demonstrate that it can be a useful effective model in some other cases. We
present a mathematical description of the method and compare it to existing
scalable Gaussian Process methods. The method is fast and interpretable, with a
range of potential applications within astronomical data analysis and beyond.
We provide well-tested and documented open-source implementations of this
method in C++, Python, and Julia.Comment: Updated in response to referee. Submitted to the AAS Journals.
Comments (still) welcome. Code available: https://github.com/dfm/celerit
Matrix methods for Pad\'e approximation: numerical calculation of poles, zeros and residues
A representation of the Pad\'e approximation of the -transform of a signal
as a resolvent of a tridiagonal matrix is given. Several formulas for the
poles, zeros and residues of the Pad\'e approximation in terms of the matrix
are proposed. Their numerical stability is tested and compared. Methods for
computing forward and backward errors are presented
Non-asymptotic fractional order differentiators via an algebraic parametric method
Recently, Mboup, Join and Fliess [27], [28] introduced non-asymptotic integer
order differentiators by using an algebraic parametric estimation method [7],
[8]. In this paper, in order to obtain non-asymptotic fractional order
differentiators we apply this algebraic parametric method to truncated
expansions of fractional Taylor series based on the Jumarie's modified
Riemann-Liouville derivative [14]. Exact and simple formulae for these
differentiators are given where a sliding integration window of a noisy signal
involving Jacobi polynomials is used without complex mathematical deduction.
The efficiency and the stability with respect to corrupting noises of the
proposed fractional order differentiators are shown in numerical simulations
Optimized auxiliary oscillators for the simulation of general open quantum systems
A method for the systematic construction of few-body damped harmonic
oscillator networks accurately reproducing the effect of general bosonic
environments in open quantum systems is presented. Under the sole assumptions
of a Gaussian environment and regardless of the system coupled to it, an
algorithm to determine the parameters of an equivalent set of interacting
damped oscillators obeying a Markovian quantum master equation is introduced.
By choosing a suitable coupling to the system and minimizing an appropriate
distance between the two-time correlation function of this effective bath and
that of the target environment, the error induced in the reduced dynamics of
the system is brought under rigorous control. The interactions among the
effective modes provide remarkable flexibility in replicating non-Markovian
effects on the system even with a small number of oscillators, and the
resulting Lindblad equation may therefore be integrated at a very reasonable
computational cost using standard methods for Markovian problems, even in
strongly non-perturbative coupling regimes and at arbitrary temperatures
including zero. We apply the method to an exactly solvable problem in order to
demonstrate its accuracy, and present a study based on current research in the
context of coherent transport in biological aggregates as a more realistic
example of its use; performance and versatility are highlighted, and
theoretical and numerical advantages over existing methods, as well as possible
future improvements, are discussed.Comment: 23 + 9 pages, 11 + 2 figures. No changes from previous version except
publication info and updated author affiliation
- …