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With sample average approximation method, we propose stochastic distributed learning algorithms to help secondary users satisfy the constraints with the feedback information from primary links when maximizing the utilities.
Secondary users check the feasibility of the constraints with the observed outage probability to see whether α > N k i ( n ) n. Change Step 4 as: If α > N k i ( n ) n, secondary users regard that the constraint is satisfied and would increase the power level for the next iteration.
We are then looking for a solution in the set of matrices 𝒫𝒜={ P ∈𝒫 : ∀(i, j )∈1, N \𝒜, P ij =0}, and it is then natural to look for the permutation compatible with the constraints with the largest number of conserved interactions, i.e. to solve: (2) Balanced GNA.
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The problem can be solved by relaxing the constraint with the ℓ 1-norm and by using convex optimization [12].
The weighting factor (gamma(t)) should be a function ranging between 0 and 1 accounting for the importance given to the constraints with respect to the destination.
When the constraints with either the larger P ¯ D or the smaller I ¯ is applied, primary signals are supposed to be more protected.
Releasing the constraints with DTT restored the wild-type (SecAΔcys) behaviour: tight binding of SecYEG causing stimulation of the ATPase activity.
For feasible operating points the sensitivity of the purity constraints with respect to the operating parameters has been calculated.
The proposed clustering algorithm can satisfy the connectivity constraints with fewer clusters because the proposed clustering algorithm is designed considering the connectivity constraint by the similarity modeling.
Finally, in column (6) we instrument the financial constraints with the instruments of Hall and Jones (1999).
Obviously, P4 is still a SIP problem, so we need to transform it into an equivalent optimization problem limited by the finite constraints with the worst-case approach.
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