Sensitivity analysis is a technique for examining the effects of changes in model parameters on the optimal solution. The analysis enables you to examine the size of a perturbation to the right-hand-side or objective vector by an arbitrary change vector for which the basis of the current optimal solution remains optimal.
Note: When sensitivity analysis is performed on integer-constrained problems, the integer variables are fixed at the value they obtained in the integer optimal solution. Therefore, care must be used when interpreting the results of such analyses. Care must also be taken when preprocessing is enabled, because preprocessing usually alters the original formulation.
Consider the problem :
where is a right-hand-side change vector.
Let denote an optimal basic feasible solution to . PROC LP can be used to examine the effects of changes in on the solution of problem . For the basic solution , let be the matrix composed of the basic columns of and let be the matrix composed of the nonbasic columns of . For the basis matrix , the basic components of , written as , can be expressed as
Furthermore, because is feasible,
where is a column vector of the lower bounds on the structural basic variables, and is a column vector of the upper bounds on the structural basic variables. For each right-hand-side change vector identified in the RHSSEN statement, PROC LP finds an interval such that
for . Furthermore, because changes in the right-hand side do not affect the reduced costs, for
is optimal in .
For and , PROC LP reports the following:
the names of the leaving variables
the value of the optimal objective in the modified problems
the RHS values in the modified problems
the solution status, reduced costs and activities in the modified problems
The leaving variable identifies the basic variable that first reaches either the lower bound or the upper bound as reaches or . This is the basic variable that would leave the basis to maintain primal feasibility. Multiple RHSSEN variables can appear in a problem data set.
Consider the problem :
where is a price change vector.
Let denote an optimal basic feasible solution to . PROC LP can be used to examine the effects of changes in on the solution of problem . For the basic solution , let be the matrix composed of the basic columns of and let be the matrix composed of the nonbasic columns of . For basis matrix , the reduced cost associated with the th variable can be written as
where and is a partition of the vector of price coefficients into nonbasic and basic components. Because is optimal in , the reduced costs satisfy
if the nonbasic variable in column is at its lower bound, and
if the nonbasic variable in column is at its upper bound.
For each price coefficient change vector identified with the keyword PRICESEN in the TYPE variable, PROC LP finds an interval such that for ,
if the nonbasic variable in column is at its lower bound, and
if the nonbasic variable in column is at its upper bound. Because changes in the price coefficients do not affect feasibility, for , is optimal in . For and , PROC LP reports the following:
the names of entering variables
the value of the optimal objective in the modified problems
the price coefficients in the modified problems
the solution status, reduced costs, and activities in the modified problems
The entering variable identifies the variable whose reduced cost first goes to zero as reaches or . This is the nonbasic variable that would enter the basis to maintain optimality (dual feasibility). Multiple PRICESEN variables may appear in a problem data set.