They developed a probabilistic polynomial time algorithm based on linear programming in order to maximize overall network throughput.
Furthermore, a novel piecewise linear representation of the system is presented allowing a systematic design procedure, based on Linear Matrix Inequalities, in order to compute the control parameters.
In [ 3], a linear dynamical system was considered to represent a gene regulatory network, and an approach, based on Linear Programming, was proposed in order to obtain the sparsest network structure from genetic perturbation experiments.
In order to obtain a criterion flexible enough to validate interesting programs using higher-order primitives, we introduce a notion of polynomial quasi-interpretations, coupled with a simple termination criterion based on linear types and path-like orders.
Although the results are based on linear theory and obviously neglect higher order effects, the method is unmatched when comparing and optimizing designs.
Next, we overcome the formidable numerical challenges posed by the model by designing a robust semi-implicit method based on linear asymptotic behavior of the leading order terms at small scales, by exploiting the exponential structure of global (integral) operators, and by parallelizing the non-linear optimization problem.
Furthermore, these cost-savings are only first-order estimates based on linear mass-cost relationships, and thus detailed engineering and economic analysis are recommended.
Performing differences between these pairs of variates allows then to consider a change detection analysis based on linear combinations of the original variables, ordered by correlation (similarity) between pairs [12].
This study had the objective of testing subpixel classification techniques based on linear and nonlinear spectral mixture models in order to identify the best possible classification technique for repeatable mapping of saltcedar canopy cover along the Forgotten River reach of the Rio Grande.
Protein length is not significantly correlated with protein interaction degree for yeast and worm based on linear correlation, but a more robust rank-order test does show weak but significant positive correlation.
