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The parity-check matrix is so named because it provides n−k parity check equations that generate constraints between data bits and parity bits.
Kwak et al. [12] and Li et al. [13] propose v-shaped calibration target and the right-angled triangulation board, respectively, to generate constraints on the extrinsic parameters through establishing line-point correspondences.
Since we use only pathways or sub-networks that are directly extracted from data without any combinatorial optimization, it provides an efficient method to generate constraints needed to improve the stability and reliability of detailed network reconstruction approaches.
Therefore, with partial kinetic parameter knowledge, dynamic models can be used to generate constraints that reduce the solution space below that identified by constraint-based models, eliminating infeasible solutions and increasing the accuracy of simulation and optimization methods.
We also develop a new hybrid heuristic that uses the results of the sequential method to generate constraints that provide tighter bounds for the global planning problem with the goal of reaching the provable optimum solution much more quickly.
Together they give rise to the complex mapping between genotype and phenotype, and they can generate constraints on adaptation.
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In particular, we found little evidence that genetic covariances among traits in males or between the sexes generate constraint; rather, genetic covariances in males and between the sexes appear to facilitate the predicted response to selection in terms of evolvability and any constraint occurs primarily from the pattern of genetic variances.
These generated constraints may be of a form that is easily extended for general case of the underlying problem, or they may be in a more complicated form where a generalisable pattern is difficult to identify.
The generated constraints seek to implement one of four power optimization approaches: slack minimization, clock tree paring, N-terminal net colocation, and area minimization.
The tabu search, which employs dynamically generated constraints to guide the search for optimum solutions, was found to be the most efficient among three tuned tested metaheuristics.
Using the part geometry and parametric punch shape models, we automatically generated constraints on tool parameters that eliminate the possibility of part-tool interference.
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