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.
How can this be done?
linear-programming
asked Mar 21 '14 at 19:56
John Chrysostom
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bumped to the homepage by Community♦ 12 hours ago
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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?
linear-programming
asked Mar 21 '14 at 19:56
John Chrysostom
1111
bumped to the homepage by Community♦ 12 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
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?
linear-programming
asked Mar 21 '14 at 19:56
John Chrysostom
1111
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
linear-programming
asked Mar 21 '14 at 19:56
John Chrysostom
1111
asked Mar 21 '14 at 19:56
John Chrysostom
1111
asked Mar 21 '14 at 19:56
John Chrysostom
1111
asked Mar 21 '14 at 19:56
John Chrysostom
1111
asked Mar 21 '14 at 19:56
John Chrysostom
1111
1111
bumped to the homepage by Community♦ 12 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 12 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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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.
answered May 14 '14 at 13:54
JaBe
1707
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1 Answer
1
active
oldest
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
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.
answered May 14 '14 at 13:54
JaBe
1707
add a comment |
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.
answered May 14 '14 at 13:54
JaBe
1707
add a comment |
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.
answered May 14 '14 at 13:54
JaBe
1707
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.
answered May 14 '14 at 13:54
JaBe
1707
edited May 14 '14 at 14:06
answered May 14 '14 at 13:54
JaBe
1707
answered May 14 '14 at 13:54
JaBe
1707
answered May 14 '14 at 13:54
JaBe
1707
1707
add a comment |
add a comment |
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