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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.
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.
When injection volumes are high, the rate is split between multiple injectors, making the optimization process time consuming for numerical simulations.
We think that this limited use of the optimization, similarly employed by others [23], [36], has a clear advantage: the initial fit to the relaxation response reduces dramatically the dimensionality of the design space, and provides good initial guesses for the parameters, reducing the computation time and making the optimization more likely to identify the best set of parameters.
FDR involves a random term in the dominator, making the optimization problem difficult.
Also, the additional tag sequence increased the Tm value of primer, making the optimization of the same cycling temperature for two-tube assay easier.
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In order to optimize the adjustable parameters, three optimization indexes are introduced to make the optimization process more adaptively.
P i, which makes the optimization problem convex.
Numerous parameters can make the optimization problems more complicated.
This is an a priori choice made here to make the optimization more tractable.
The combination of these approaches makes the optimization process more complex.
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