Class SensitivityAnalysis
java.lang.Object
com.eqixiac.equinix.design.solve.SensitivityAnalysis
Sensitivity of a recommendation, computed exactly from a
SolveRun by re-ranking the portfolios
the solver already evaluated with the solver's own Ranking rules; nothing is searched again.
- Weight ranges. For each objective, its share
tof the total weight is varied from 0 to 1 while the other weights keep their proportions. Normalised scores do not depend on weights, so each option's weighted score is linear intand the range over which the recommendation stays best is found in closed form; the option that takes over just outside each end is named. - Nearest alternative. The other frontier option closest in normalised-score space, with the raw per-objective difference.
- Binding constraints. For each constraint, the feasible set plus the portfolios that failed only that constraint is re-normalised and re-ranked: the constraint is binding when the recommendation changes.
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Method Summary
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Method Details
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analyse
- Parameters:
run- the solver's evidence- Returns:
- the report, or
nullwhen nothing was feasible
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