whose solution is the maximum of the solutions to the following three subproblems.
Then, compute the gradient vector u0 = ∂T/∂θ at θ0. Solve the equation H T u = u0 by the Incomplete Cholesky Conjugate Gradient (ICCG) method (e.g., Mori, 1986) to get the vector u for the direction of the next Linear Search in step (B) until the function T attains the maximum overall θ, which is the maximum posterior (MAP) solution for the given τ.
where T is the maximum existence time of solutions u 1 and u 2 and c depends on ∥ u 1 ( 0, x ) ∥ H 1 ( R ) and ∥ u 2 ( 0, x ) ∥ H 1 ( R ).
k is the maximum number of intermediate solutions that we will keep at each iteration of H-BOP.
In such a case, because of this intrinsic strong instability, the main difficulty of any numerical computation is the ability of discovering at run time, only using data, what is the maximum attainable accuracy on the solution.
where the optimization variables are W n,μ mn,ν nk,s mn,I nk for all n, m ∈ N, k ∈ K, using a standard SDP solver and the optimal solution is the maximum value of γ for which Problem (22) is feasible.
SH are the number of single hospital clusters in the overall solution and Max is the maximum number of hospitals in any cluster in the Hospital Group solution.
then, where is the maximum time interval on which the solution of problem (1.1)–(1.3) exists.
The CPU usage ranges between 0 and 100% for each core (e.g. 4 × 100 = 400 is the maximum value for the single-machine solution).
The Langmuir model can therefore be applied to fit the experimental data as follows: (1) q = Q m C K d + C where q is the amount of the adsorbed HSA at equilibrium, C is the equilibrium concentration of unbound HSA in solution, Qm is the maximum capacity of the affinity polymer and Kd is the dissociation constant.
