Difference between revisions of "LpRhsSa"
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− | + | = LpRhsSa(lp,constraint) = | |
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+ | Computes the range over which a right-hand-side constraint coefficient can vary in a linear or quadratic optimization program without change the dual value (aka shadow price) of the optimal solution. See [[LpShadow]] for more information on what the dual value is. | ||
+ | |||
+ | «lp» is an LP or QP returned from a previous call to [[LpDefine]] or [[QpDefine]]. This function cannot be applied to an NLP. | ||
+ | |||
+ | «constraint» identifies which constraint coefficient you want the sensitivity for. You may provide your problem's ''constraint index'' for this parameter to get the sensitivities for all RHS coefficients. | ||
+ | |||
+ | = See Also = | ||
+ | |||
+ | * [[LpShadow]] | ||
+ | * [[LpSlack]] | ||
+ | * [[LpDefine]], [[QpDefine]] | ||
+ | * [[LpObjSa]] | ||
+ | * [[LpReducedCost]] |
Revision as of 19:19, 28 January 2010
LpRhsSa(lp,constraint)
Computes the range over which a right-hand-side constraint coefficient can vary in a linear or quadratic optimization program without change the dual value (aka shadow price) of the optimal solution. See LpShadow for more information on what the dual value is.
«lp» is an LP or QP returned from a previous call to LpDefine or QpDefine. This function cannot be applied to an NLP.
«constraint» identifies which constraint coefficient you want the sensitivity for. You may provide your problem's constraint index for this parameter to get the sensitivities for all RHS coefficients.
See Also
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