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We first present definitions and optimality conditions as well as some single-objective formulations of MOP, parameterized with respect to some reference point in the space of objective functions.
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This method minimizes an objective functional in the space of the logarithmed and non-logarithmed model parameters (log(h), log(a), log k), and θ) successively by the steepest descent (SD) and Gauss-Newton (GN) techniques in order to essentially maintain the stability and convergence of this inverse method.
As application, we show that, for every system of vector quasi-equilibrium problems, there exists at least one essential component of the set of its solutions in the uniform topological space of objective functions and constraint mappings.
As application of Theorem 3.1, now we will show that, for every system of vector quasi-equilibrium problems, there exists at least one essential component of the set of its solutions in the uniform topological space of objective functions and constraint mappings.
Everything – in the space of one match.
All in the space of a minute.
In the GSO algorithm, a group of glowworms are initialized randomly in the solution space of the objective function and each of them has the same value of luciferen.
F is called the values space of objective functions in problem (1).
The MOMCMC method is designed to address the "multi-objective sampling" problem, which is not only of interest in exploring diversified solutions at the Pareto optimal front in the function space of multiple objective functions, but also those near the front.
MODENA explores the approximate set of weak Pareto optimal solutions in the objective function space of 2 objective functions, a structure stability score and structure similarity score.
Subclinical seizures (SCSs) are characterized by paroxysmal rhythmic epileptiform discharges that evolve in time and space in the absence of objective clinical manifestation or report of a seizure.
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