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From the numerical point of view, we present a characteristic-Galerkin method for solving the shallow water equations, and an optimization algorithm for the computation of the optimal design variables.
Using the COP method, we compute the posterior market simulations and use them for the computation of the optimal weights for the portfolio at time t.
An iterative gradient type method for the computation of the optimal control is proposed and the results obtained by its use in particular cases are discussed.
From the numerical point of view, we present a characteristic-Galerkin method for solving the shallow water equations, and a direct search algorithm for the computation of the optimal design variables.
This estimation is very important for the computation of the optimal request for the next control interval.
The already mentioned search procedure can be used for the computation of the optimal value of γ as well.
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Optimizing the use oFirst specthem by adjustiterativeain transmission processers is the main purpose.
The goal of this process is the computation of the optimal rigid transformation for the alignment of several sets of geometric entities (points and/or surfaces).
This optimal temperature corresponds to 20°C and 21°C for London and Rome respectively, and it represents the reference point x0 for the computation of the attributable risk measures.
The animation displays the contruction of the visibility graph, and the computation of the time-optimal trajectory.
Finally, a very general learning-theoretic model allows for the computation of optimal social choice functions (i.e., ones that maximize expected social welfare) under arbitrary, sampleable distributions.
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