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This observation suggests a constructive procedure to formulate an upper bound on the rank of FIM based on model symmetries.
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It is formally convenient to re-formulate the problem defining an upper bound for the absolute deviation of the therapy output from the healthy one: (2) | y d t (t ) − y h (t ) | ≤ y u b − y l b 2. We applied this preliminary screening method, based on direct trajectories comparison, to our case study.
First, we formulate the dual problem whose optimum provides an upper bound to all feasible solutions.
The aim of the filtering problem is to design a least squares filter for the formulated nonlinear system with uncertain polynomials, and an upper bound of the filtering error covariance is found and subsequently minimized at each time step.
So it gives you both the lower bound and an upper bound.
It means that I only have an upper bound and not a lower bound.
We formulate a simple version of PSE and show that it leads to an upper bound on screening efficacy if the progressive detection assumption does not hold (and any effect of birth cohort is minimal) To determine if the upper bound is reasonable, for three randomized screening trials, we compared PSE estimates based only on screened subjects with PSE estimates based on all subjects.
We propose a novel approach which employs an upper bound of the worst-case performance, then formulates a non-convex quadratic minimization problem to get the update of iterative control inputs.
In fact, in this case an upper bound is derived using (18) in which ℬ ′ is replaced by ℬ I. To solve the optimization problems formulated in this study, we use the CVX toolbox [29].
That's sort of an upper bound, a worst case.
Furthermore, an upper bound of this region is estimated.
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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.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com