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Exact(22)
In order to optimize the adjustable parameters, three optimization indexes are introduced to make the optimization process more adaptively.
Moreover, the more accurate transformation helps make the optimization more reliable and engineering applicable.
A linearization technique is applied to make the optimization problem convex and able to be solved at operational timescales.
Such characteristics make the optimization problems really difficult to be solved by deterministic methods.
For very ill-conditioned problems, we use regularization to make the optimization algorithm robust.
The proposed ISCO approach can improve bioremediation designs by providing adjustable schedules for operating wells from stage to stage, which will make the optimization more realistic.
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We formulated the optimality conditions rigorously in the continuum before deriving finite element discretization, thereby making the optimization independent of discretization choice.
At contact points, the dynamics switches, making the optimization problem nonsmooth and highly nonconvex.
However, the complexity of GPU system makes the optimization of even a simple algorithm difficult.
The inclusion of shape and topology variables in addition to size variables makes the optimization problem highly complex.
Using the adjoint method, the gradient of the cost function can be computed fast, at the expense of few function evaluations, making the optimization process very efficient.
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