Additionally, a new simple yet efficient optimization method that can jointly optimize the magnitude of the perturbations for a large number of regions is proposed.
By choosing the nonlinear optimization approach, we can jointly optimize both for (mathcal {R}_{r}^) and the transformation which relates the calibration pattern coordinate system and the camera coordinate system, thereby achieving a more accurate refinement transformation.
Nu-support vector regression (Nu-SVR) is employed as modeling method of multi-step-ahead prediction, and a combinational optimization algorithm is proposed to jointly optimize feature selection and hyperparameter selection for prediction model.
On this basis, the deep model is fine-tuned through back-propagation to jointly optimize both feature and classifier, so that the optimal overall classification performance could be achieved.
The objective of the paper is to jointly optimize the parameters of all layers in a decentralized optimization problem and decompose joint QoS optimization into three sub problems at fabric layer, collective layer and application layer.
Recent developments in optimal feedforward control are combined with feedback control to jointly optimize a single performance criterion.
We also propose to jointly optimize the prediction filters by using an algorithm that alternates between filter optimization and weight computation.
Related to this fact, we propose to jointly optimize the prediction filters by using an algorithm that alternates between the optimization of the filters and the computation of the weights.
They formulated a combinatorial optimization problem using the graph theory and proposed a heuristic algorithm to jointly optimize subcarrier assignment, transmission mode selection and relay selection for the system total throughput maximization.
Next, we shall discuss how to jointly optimize the resampling parameters.
By carefully choosing system parameters, we can jointly optimize the system performance and computational complexity.
