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Optimizing small-size problems can be performed using optimization software.
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Generally, architectural and geometry level synthesis for biochip design is performed using optimization techniques.
The optimization was performed using optimization of rotamers by iterative techniques (ORBIT), a protein design program that utilizes a physically based force field and the Dead-End Elimination theorem to compute sequences that are optimal for a given protein scaffold.
An analytical meta-model is then fit to this data and optimization can be performed using different optimization cost functions or optimization algorithms with very little computational effort.
The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models.
As a result, the problem size of the superstructure can be substantially reduced and optimization can be performed using commercially available process simulators and optimization solvers.
The design of reactive distillation systems can be performed using single and multi-objective optimization approaches.
This optimization could be performed using topological information inferred from the packet exchanges of neighboring nodes.
Phylogenetic analyses of DNA sequence data were performed using direct optimization [ 70], a heuristic approximation to the optimal tree alignment method [ 71- 75], under parsimony optimality criterion.
The calibration of a model is performed using an optimization software tool using the experimental data.
Training was performed using Adam optimization [15] for 200 epoch with learning rate 10e-3, momentum 0.9, and batch size 8.
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