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An experimental design including both a factorial and a central composite design allowed a reduction in the number of optimization experiments.
The causal nature of CSI combined with the increase in the number of optimization variables makes it a very challenging problem.
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In the second algorithm, we propose a modification of the first algorithm where the second projection is performed on feasible set while the first projection over C is replaced by a projection onto a tangent plane to C in order to reduce the number of optimization subproblems to be solved.
Fortunately, the continuous advances in computer hardware and software are allowing researchers to deal with these optimization problems using computational resources, as can be seen in the large number of optimization methods that have been applied to the renewable and sustainable energy field.
The reduction in the number of active diodes during optimization was effectively achieved by shortening the wavelength interval for the optimization search.
In the algorithm [21], the number of linear optimization problems that are solved over a convex set in each iteration is (O frac{c^{k-1}(lO frac{c^{k-1}arepsilog^{k-1}})), wheRe (R=max{ frac{u_{i}}{l_{i}}|i=1,ldots,k}).
It is worth mentioning that 41%, 44%and45%5% optimizations in the number of cells in addition to 15%, 25%and33%3% optimizations in the area are achieved for the proposed mod 4, mod 8 and mod 16 counters, respectively, in comparison with the best previous results.
However, early experience was that this did not result in any increase in the number of real candidates for lead optimization.
Under such circumstances, optimization in the number of data used for learning is of prime concern.
This method can be computationally expensive with an increase in the number of variables and/or constraints of the optimization problem.
In the proposed method, the iteration number of optimization is set to 50, the convergency criterion is set to ∥V new−V old∥<0.0001 and the value of δ is 0.1.
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