Get reduced costs from simplex tableau











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This is probably a dumb question... but I'm trying to find how to calculate the reduced cost for a particular variable based on the information in a simplex tableau after I've minimized a linear programming problem. I've been googling like crazy and, while I can find a million sites that explain how to interpret reduced costs and how they are useful, I can't find a single resource that tells me how to calculate them.



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    This is probably a dumb question... but I'm trying to find how to calculate the reduced cost for a particular variable based on the information in a simplex tableau after I've minimized a linear programming problem. I've been googling like crazy and, while I can find a million sites that explain how to interpret reduced costs and how they are useful, I can't find a single resource that tells me how to calculate them.



    How can this be done?










    share|cite|improve this question
























      up vote
      2
      down vote

      favorite









      up vote
      2
      down vote

      favorite











      This is probably a dumb question... but I'm trying to find how to calculate the reduced cost for a particular variable based on the information in a simplex tableau after I've minimized a linear programming problem. I've been googling like crazy and, while I can find a million sites that explain how to interpret reduced costs and how they are useful, I can't find a single resource that tells me how to calculate them.



      How can this be done?










      share|cite|improve this question













      This is probably a dumb question... but I'm trying to find how to calculate the reduced cost for a particular variable based on the information in a simplex tableau after I've minimized a linear programming problem. I've been googling like crazy and, while I can find a million sites that explain how to interpret reduced costs and how they are useful, I can't find a single resource that tells me how to calculate them.



      How can this be done?







      linear-programming






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      asked Mar 21 '14 at 19:56









      John Chrysostom

      1111




      1111






















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          After you have minimized the LP, there are no more reduced costs, e.g. they are all zero. All that you have are the dual variables. You only do have nonzero reduced costs at a local minimum (=basis = edge of the feasible polyhedron).



          Generally, the reduced cost is:



          begin{equation}
          d_j = c_j - mu^top A_j
          end{equation}



          Cost of increasing a nonbasic variable $x_j$. Nonbasic row of the coefficientmatrix $A$: $A_j$.



          With dual variable $mu$:



          begin{equation}
          mu = c_B^top B^{-1}
          end{equation}



          $B$ is the Basis of coefficient matrix $A$, sometimes $B$ is called $A_B$.



          There is an example in Bertsimas - Introduction to Linear Optimization on p.84.






          share|cite|improve this answer























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            up vote
            0
            down vote













            After you have minimized the LP, there are no more reduced costs, e.g. they are all zero. All that you have are the dual variables. You only do have nonzero reduced costs at a local minimum (=basis = edge of the feasible polyhedron).



            Generally, the reduced cost is:



            begin{equation}
            d_j = c_j - mu^top A_j
            end{equation}



            Cost of increasing a nonbasic variable $x_j$. Nonbasic row of the coefficientmatrix $A$: $A_j$.



            With dual variable $mu$:



            begin{equation}
            mu = c_B^top B^{-1}
            end{equation}



            $B$ is the Basis of coefficient matrix $A$, sometimes $B$ is called $A_B$.



            There is an example in Bertsimas - Introduction to Linear Optimization on p.84.






            share|cite|improve this answer



























              up vote
              0
              down vote













              After you have minimized the LP, there are no more reduced costs, e.g. they are all zero. All that you have are the dual variables. You only do have nonzero reduced costs at a local minimum (=basis = edge of the feasible polyhedron).



              Generally, the reduced cost is:



              begin{equation}
              d_j = c_j - mu^top A_j
              end{equation}



              Cost of increasing a nonbasic variable $x_j$. Nonbasic row of the coefficientmatrix $A$: $A_j$.



              With dual variable $mu$:



              begin{equation}
              mu = c_B^top B^{-1}
              end{equation}



              $B$ is the Basis of coefficient matrix $A$, sometimes $B$ is called $A_B$.



              There is an example in Bertsimas - Introduction to Linear Optimization on p.84.






              share|cite|improve this answer

























                up vote
                0
                down vote










                up vote
                0
                down vote









                After you have minimized the LP, there are no more reduced costs, e.g. they are all zero. All that you have are the dual variables. You only do have nonzero reduced costs at a local minimum (=basis = edge of the feasible polyhedron).



                Generally, the reduced cost is:



                begin{equation}
                d_j = c_j - mu^top A_j
                end{equation}



                Cost of increasing a nonbasic variable $x_j$. Nonbasic row of the coefficientmatrix $A$: $A_j$.



                With dual variable $mu$:



                begin{equation}
                mu = c_B^top B^{-1}
                end{equation}



                $B$ is the Basis of coefficient matrix $A$, sometimes $B$ is called $A_B$.



                There is an example in Bertsimas - Introduction to Linear Optimization on p.84.






                share|cite|improve this answer














                After you have minimized the LP, there are no more reduced costs, e.g. they are all zero. All that you have are the dual variables. You only do have nonzero reduced costs at a local minimum (=basis = edge of the feasible polyhedron).



                Generally, the reduced cost is:



                begin{equation}
                d_j = c_j - mu^top A_j
                end{equation}



                Cost of increasing a nonbasic variable $x_j$. Nonbasic row of the coefficientmatrix $A$: $A_j$.



                With dual variable $mu$:



                begin{equation}
                mu = c_B^top B^{-1}
                end{equation}



                $B$ is the Basis of coefficient matrix $A$, sometimes $B$ is called $A_B$.



                There is an example in Bertsimas - Introduction to Linear Optimization on p.84.







                share|cite|improve this answer














                share|cite|improve this answer



                share|cite|improve this answer








                edited May 14 '14 at 14:06

























                answered May 14 '14 at 13:54









                JaBe

                1707




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