Optimization to low water activity and low temperature may be more difficult than optimization to low temperature alone.
It calls for the solution of more difficult parametric optimization problems with linear differential equations embedded, whose critical regions are potentially nonconvex.
However, while the method performs reliably for many bacteria, the MALDI-TOF MS-based identification of fungi is often more difficult and requires optimization of extraction and data acquisition protocols.
Furthermore, the method performs reliably for many bacteria, while the MALDI-TOF MS-based identification of fungi is often more difficult and requires optimization of extraction and data acquisition protocols.
The design of actuators is transformed into an easier size optimization from a more difficult shape and topology optimization.
The original more difficult shape and topology optimization driven by the Hamilton Jacobi partial differential equation (PDE) is transferred into a relatively easier parametric (size) optimization, to which many well-founded optimization algorithms can be applied.
In doing so, the originally more difficult shape and topology optimization, driven by the temporal and spatial Hamilton Jacobi partial differential equation (PDE), is transformed into a relatively easier size optimization of the expansion coefficients of the basis functions.
In general, it is much more difficult to solve the discrete optimization problem than to solve the continuous version of the optimization problem.
Distributed droop control enables a completely decentralized architecture but, the energy storage optimization becomes more difficult.
