To demonstrate these techniques, we focus on two large-scale constraint-satisfaction domains, Sudoku and circle packing.
We have developed a large-scale, constraint-based network model of the metabolic partnership between astrocytes and glutamatergic neurons that allows for a quantitative appraisal of the extent to which stoichiometry alone drives the energetics of the system.
Comparing with other optimization algorithms, simulations suggest that RNN has strong ability to search for exact solutions under the condition of large scale constraints.
In conjunction with the adjoint-variable sensitivity information, the enhanced aggregation method is utilized to efficiently reduce the computational effort arisen from large-scale strength constraints.
To solve large-scale problems, tightening constraints, and simplified formulations are proposed.
MicrobesFlux converts the constraint-based flux analysis model on the reconstructed network to a large-scale optimization problem with constraints, and employs IPOPT (an optimization solver based on an interior-point algorithm) to find an optimal solution [ 24].
However, in large-scale analyses, often grid constraints are neglected.
Finally, secondary infections are successfully predicted, demonstrating that the model is useful in aiding the design and optimization of large-scale systems where economic constraints are more prevalent.
This paper is concerned with the sensor-network-based distributed stabilization of nonlinear large-scale systems with energy constraints and random sensor faults.
Quite spectacularly, Figure 4 demonstrates that the VPG results are markedly close to the bar-PG results simply by suppressing fluctuations within edges, regardless of the large-scale heterogeneity of where constraints are distributed.
They should form an integral part of any outcome monitoring efforts but are often omitted from large-scale registries because of constraints on the amount of data these registries can collect.
