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The effective parameters on the extraction recovery were studied and optimized utilizing two decent optimization methods; factorial design and central composite design (CCD).
Moreover, most existing MEKL utilizes the gradient decent optimization to learn classifiers, but it is time consuming for training.
To deal with this problem, an iterative refinement of the model parameters based on the gradient decent optimization has been introduced.
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Most design mechanisms are focused on perturbing the solution to a decent convex optimization problem under the theory of Empirical Risk Minimization (ERM).
Stochastic Gradient Decent (SGD) is used for optimization in our model with back propagation.
The parameters for running both methods were set similar to [ 20], that is, with QUASI-P a gradient decent method was used for optimization (start step size 0.01, stopping condition epsilon 0.01).
An optimization approach with the Gradient Decent Projection technique is proposed to calculate the predictions of the control actions.
Among the usual optimization algorithms, such as the direct decent method and conjugate gradient method, the convergence rate of Newton iterations near the solution is the fastest (see [15]).
Once the preliminary integrated design is obtained there remain several optimizing parameters that increases the plant efficiency thus, optimization is carried out using process knowledge and modified coordinate decent methodology.
By this, coordinate decent algorithms have monotone convergence in the objective function and trend to have the ability of achieving the global optimization [19].
We also explored some advanced numerical optimization methods like Adam and Adagrad, where we found the advanced methods were outperformed by the traditional stochastic gradient decent.
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