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But, correct global optimal solutions can be assembled from among the Pareto-optimal sets of several problems involving a softer constraint on Mn,f.
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To balance the contributions from the structural prior and optical measurements, "softer" constraints, in the form of regularizations, have been investigated.
There are now four constraints on each atom, the valence sum rule being a hard constraint, and the valence vector sum rule (eq 44) providing three somewhat softer constraints.
The third soft constraint is also fully satisfied.
The second soft constraint is not fully satisfied.
However, when a certain threshold k (e.g., three days) is exceeded, then these soft constraints behave like hard constraints, i.e. the soft constraint remains unfulfilled.
However, the formal verification of soft constraint becomes more difficult, because the soft constraint conditions may be mathematical expressions representing QoS properties.
Treating the constraint δ(A x )≤Tmax as a soft constraint is also considered.
We discuss the optimal input policy with soft constraint and hard constraint separately.
We consider soft constraint problems where some of the preferences may be unspecified.
However, if a soft constraint is not met, the model will not be infeasible.
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