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We establish convergence conditions and the guarantees to their feasibility.
The convergence conditions are given in LMI formulation.
Analytical convergence conditions for the proposed DPCA are also established.
Convergence of the set of no-variation genes was achieved by intersection of the sets found during 10 additional steps under convergence conditions.
A proof of the convergence conditions for the reinforcement synthesis algorithm is presented.
In particular, we prove convergence and exponential convergence conditions around zero under general Markovian strategies.
Therefore MLP could be useful statistical models, as long as convergence conditions are respected.
The ssngh automatically and dynamically reflects swarm convergence conditions and frees its settings.
These optimization processes are repeated until the given convergence conditions are satisfied.
Moreover, it presents constraints to the input spectrum which ensure that the convergence conditions are satisfied.
The new convergence conditions are much more relaxed than ever before.
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