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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.
Following the model selection, the N′ tail (KEE) was added in an extended conformation (φ/ψ of ±135°) and docked with softer constraints to produce 1000 additional structures, from which the top-scoring model was selected.
Weightings used were 100, which in this case effectively make the rest of the objective function irrelevant for illegal solutions, and lower weightings were used in different treatments to give softer constraints, viz.
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.
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Hard constraints and soft constraints are considered.
Soft constraints are very flexible and expressive.
We first handle rate constraints within a soft constraints framework.
Both hard and soft constraints can be implemented.
These implications of soft constraints are tested in two experiments.
Soft constraints extend classical constraints to deal with non-functional requirements, over-constrained problems and preferences.
In these problems, goals are modeled as soft constraints with utility values, and actions have costs.
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