Recalling this must be non zero we can solve for γ In turn, we discover for non-basic variables Solving and interpreting LP Models Cost Ranging for basic variables Example Suppose in our example problem we want to alter the objective function on Xfancy so it equals 2,000 + . The setup then is and note the order that variables appear in the TB vector reflects the order they appear in the basis so Xfine is first then Xfancy So for the non-basic variables reduced cost equals which implies -300< < 125 or that the basis is optimal for any objective function value for Xfancy between 2125 and 1700. This shows a range of prices for Xfancy for which optimal level is constant. Solving and interpreting LP Models A1J Ranging
Recalling this must be non zero we can solve for γ In turn, we discover for non-basic variables Solving and interpreting LP Models Cost Ranging for basic variables Example Suppose in our example problem we want to alter the objective function on Xfancy so it equals 2,000 + . The setup then is and note the order that variables appear in the TB vector reflects the order they appear in the basis so Xfine is first then Xfancy So for the non-basic variables reduced cost equals which implies -300< < 125 or that the basis is optimal for any objective function value for Xfancy between 2125 and 1700. This shows a range of prices for Xfancy for which optimal level is constant. Solving and interpreting LP Models A1J Ranging