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This problem can be relaxed by allowing a limited number of free riders so that, by sacrificing some coverage accuracy, the transmission cost is reduced.
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Fortunately, if the vector is sufficiently sparse the problem can be relaxed replacing the l0-norm by a l1-norm, defined as ∥x∥1 = ∑ i |x i |, leading to a convex optimization problem with a lower computational burden.
In turn, this problem can be relaxed into a sequence of convex Semi-Definite Programs (SDPs) by resorting to moments-based polynomial optimization techniques.
Furthermore, by introducing a new variable z=x T x to linearize constraint (39c), the above problem can be relaxed to a standard SDP problem which yields the result of (14).
Moreover, this binary optimization problem can be relaxed to a linear programming (LP) problem that has a solution z ∈ {0, 1}KΓ × 1 (see Appendix 1).
Fortunately, under certain approximations, the optimization problem can be relaxed to convex problem and therefore, the optimal or near-optimal solutions of problem can be found with low complexity.
The applied stress can be relaxed by grain boundary diffusion, by grain boundary wetting or by a combination of both.
The condition that is a continuous mapping can be relaxed by the following condition: is continuous at a point.
where the quadratic expression in the objective function can be relaxed by making the expansion suggested in [10].
The condition can be relaxed by fully synchronizing the acquisition with the pulse cycle [ 25].
This condition can be relaxed by coupling the cell model to previously developed VEGF models [ 39, 40].
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