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The least-squares methods which minimize the trace of eigenmode matrix suggested by Pešek and Lallement, respectively, for self-adjoint systems are extended to general non-defective systems in this paper.
Toward this end, the informative sensors in each subset ({mathcal {S}}_{rho _{ell }}) will be placed/move in locations that minimize the trace of the error covariance associated with the estimator (hat {mathbf {s}}_{rho _{ell }}(t|t)).
In doing so, one can either minimize the trace tr(P n ) if N opt needs to be applied to all of the states, or the (k k th component P (k k)n of P n corresponding to the k th state, respectively, N opt = arg min n ( tr P n ) + 1, (106).
We need to find the training scheme that can minimize the trace of R v. Suppose there are two training schemes with identical E H ). Thus, the first and the second items in (78) are the same for both training schemes.
In classic optimal design methods, it is proposed to give a new input which minimize the trace or determinant of the main term (mathrm {E}[(hat {boldsymbol {theta }}-mathrm {E}[hat {boldsymbol {theta }}])(hat {boldsymbol {theta }}-mathrm {E}[hat {boldsymbol {theta }}])']), and it is called A-optimal design or D-optimal design, respectively (Kiefer [4, 5], Kiefer & Wolfowitz [6]).
Specifically, the goal is to choose (at design-time) a subset of sensors (satisfying certain budget constraints) from a given set in order to minimize the trace of the steady state a priori or a posteriori error covariance produced by a Kalman filter.
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This is equivalent to minimizing the trace of x ^ x ^ *.
Subsequently, filter parameters are determined by minimizing the trace of the derived upper bound.
The problem addressed is the design of a filter that minimizes the trace of the estimation error variance.
Given initial value of β and α, one can estimate α by minimizing the trace of (hat {Omega } alpha)).
First, we consider this problem by minimizing the trace of the constrained biased CRB, which is referred to as the Trace-opt criterion [7].
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

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