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As mentioned, QC methods are computationally expensive: the scaling of the computer effort and storage of high-level computations with the size of the system (N) is harsh, reaching, for example, N7, for the most expensive and most accurate wavefunction-based methods, such as Coupled Cluster [10 12].
Even if MDO is a very large optimization problem, our approach reduces considerably the computer effort.
This selective inversion preserves the sparse structure of the problem and leads to substantial savings in computer effort.
This means that the mesh must be continuously updated and controlled, rendering a large demand of computer effort.
The quasi-stationary state approximation makes it possible to solve problems that would require an extremely large computer effort with conventional models.
By means of different numerical examples, it is shown that more than 50% of the computer effort can be saved by using Latin hypercubes instead of simple Monte Carlo in importance sampling.
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