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Accordingly, utilizing the Gradient Projection for Sparse Reconstruction (GPSR) method (Figueiredo et al. 2007), we minimized the objective function J(m) to obtain the sparse estimates.
Age-at-harvest models converged to values that minimized the objective function (as indicated by positive definite Hessian matrices output from AD Model Builder) in >96% of the simulations for each scenario.
We have minimized the objective function in Equation 5 by using the same nonlinear solver as we have used for kinetic modeling.
The optimal solution that minimized the objective function (1) is x1=2, x2=1, and x3=2, t am =16, and t pm =19.
We configured SimRunner for the above optimization problem, which in turn ran the validated model for all possible parameter values in the constraints, using a sophisticated algorithm, until it found the solution that globally minimized the objective function (1).
Therefore, for a CDR-SOB score at study entry and its progression as a function of time, the modeling exercise essentially consisted of identifying a DOT for each individual that minimized the objective function given the population disease trajectory.
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According to learning theory [ 26], minimizing 1 n w T (X T P X ) w corresponds to empirical risk minimization, whereas minimizing the objective in Eq. (10) corresponds to structural risk minimization that improves the generalizability of the resultant model.
The goal is to find k representative objects that minimize the objective function, which is the sum of the dissimilarities of all observations to their closest medoid.
The optimization algorithm used to minimize the objective function and damage detection procedures are explained.
A simulated annealing and particle swarm optimization algorithm are separately applied to minimize the objective function.
The Lagrange multipliers were optimized by minimizing the objective function of Eq. (31a) with b = 0.
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CEO of Professional Science Editing for Scientists @ prosciediting.com