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Therefore, the problem can be expressed by a bipartite graph.
and the dual optimization problem can be expressed by begin{array}rcl@ &maxlimits_{boldsymbol{Z}_{2}}& D_{DF}left(boldsymbol{Z}_{2}right), &mathrm{s.t.}& boldsymbol{Z_{2}}succeq 0. end{array} (36).
In the Bayesian inference context, the solution of an inverse problem can be expressed by the posterior distribution of the model parameters that combine the likelihood function and the prior distributions of the model parameters.
and the dual optimization problem can be expressed by begin{array}rcl@ &maxlimits_{boldsymbol{Z}_{1}}& D_{AF}left(boldsymbol{Z}_{1}right), &mathrm{s.t.}& boldsymbol{Z}_{1} succeq 0. end{array} (20).
Thus, the optimization problem can be expressed by. left{ begin{array}{l} text{min},fleft( {S,d,B,n_{P},n,zeta } right), hfill s.t.,B_{text{min} }, le B, le B_{text{max} }, hfill zeta_{text{min},} le,zeta le zeta_{text{max} }, hfill n_{{P_{text{min},} }} le,n_{P} le n_{{P_{text{max} } }}, hfill n = leftlceil {frac{Scdottan zeta }{2d}} rightrceil, hfill end{array} right.
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Therefore, it is interesting to address the problem of determining if the stoichiometric capacitance, determined by solving the proposed MILP problem, can be expressed as a combination of a given set ℛ of known enzymatic reactions.
We show that the data replication problem can be expressed as a special Bin Packing Problem and can hence be solved by an off-the shelf solver for integer linear programs.
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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