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The blue circles represent the expected values of the optimization variable ({lambda }_{mathcal {M}_{t}}).
The return value xopt is a NumPy array containing the optimized values of the optimization parameters.
This is because of the lack of a standard methodology for matching a suitable optimization algorithm with a particular design problem, and also for the need to first determine the control parameter values of the optimization algorithm prior to actually using the algorithm for design purposes.
The red circles stands for the obtained values of the optimization variable.
We used ρ = 1.0/kb and γ = 1.0/kb as the starting values of the optimization procedure in the first iteration.
In this case, we used ρ = 5.0/kb, γ = 5.0/kb, and λ = 0.352 kb as the starting values of the optimization procedure in the first iteration.
Similar(54)
The policy which minimizes the cumulative value of the optimization objective is regarded as the optimal one.
A reinforcement learning algorithm is applied to predict the overall value of the optimization objective given vehicles' states.
We use to denote the total expected value of the optimization objective for each car until it arrives at the destination given its current node, direction, place and the decision of the light.
To show the lemma, it is sufficient to show that the combination of the transmitting user i ∗ and the packet (kappa _{i}^) achieves the minimal value of the optimization problem (3).
However, most of these approaches can only obtain the mean value of the optimization objective, and cannot directly obtain other statistical information such as the standard deviation and probability distribution curves.
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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