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For optimal (minimal) values of the objective functions the optimal values of parameters connected with the shapes of the rods are obtained.
Since we are only interested in the maximal and minimal values of the solutions, by Remark 8, we have to obtain only representations for values x ( t 1 ) and x ( t 2 ).
Main results of our paper are based on the maximum boundaries principle, that is, on the fact that the maximal and minimal values of the solution can be achieved only at the points or The boundaries maximum principle in the case of the zero operator was considered in the recent papers [2, 3].
Now we introduce the notations analogous to the one in [21], p.46 for the maximal and minimal values of the functions ρ and θ for fixed t: begin{aligned} &m_{rho}(t)=min _{xin[0,1]}rho x,t),qquad M_{rho}(t)=max_{[0,1]} rho (x,t), &m_{theta}(t)=min_{xin[0,1]}theta x,t),qquad M_{theta}(t)=max_{xin[0,1]}theta x,t).
These two values for μ can be combined with β of specific flour to calculate the maximal and minimal values of the product β × μ.
The boundaries of the GC content determine, in turn, theoretically maximal and minimal values of the non-codon-biased GC content (GCNCB).
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In contrast, CoreD first finds a minimal value of the optimal coalition structure so that there exists no blocking coalition.
The design problem is to find a causal stabilizing controller, which ensures the minimal value of the mean output variance.
First, there is no guarantee that as the data increases, the minimal value of the regularization functional will remain bounded.
For an arrow in a decorated tree, let its value be the minimal value of the leaves of the subtree grafted to its end.
The objective of the optimization model is to maximize the minimal value of the in-plane minor principal stress over the entire membrane.
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