Linear programming, affine scaling
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I'm having some trouble understanding the intuition behind the theory of affine scaling(algorithm used in linear programming)
$D_k$ is a diagonal matrix with $x_k$ on the diagonal
the vector of dual variables:
$w^k = (AD^2_kA^T)^-1AD^2_kc$
we then compute the vector of reduced costs
$r^k = c-A^Tw^k$
then the theory states (this part I don't quite understand)
that if $r^k > 0$ and $1^TD_kr^k < epsilon$
that the current solution $x_k$ is optimal. why is that?
I am looking at this: https://en.wikipedia.org/wiki/Affine_scaling
thank you for any help
optimization linear-programming affine-geometry
add a comment |Â
up vote
0
down vote
favorite
I'm having some trouble understanding the intuition behind the theory of affine scaling(algorithm used in linear programming)
$D_k$ is a diagonal matrix with $x_k$ on the diagonal
the vector of dual variables:
$w^k = (AD^2_kA^T)^-1AD^2_kc$
we then compute the vector of reduced costs
$r^k = c-A^Tw^k$
then the theory states (this part I don't quite understand)
that if $r^k > 0$ and $1^TD_kr^k < epsilon$
that the current solution $x_k$ is optimal. why is that?
I am looking at this: https://en.wikipedia.org/wiki/Affine_scaling
thank you for any help
optimization linear-programming affine-geometry
1
why us a fake profile photo? topbeautyful.blogspot.com/2015/02/…
– LinAlg
Aug 2 at 15:36
@LinAlg: Gets more attention, presumably. Opportunity for mansplaining.
– copper.hat
Aug 2 at 16:46
add a comment |Â
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I'm having some trouble understanding the intuition behind the theory of affine scaling(algorithm used in linear programming)
$D_k$ is a diagonal matrix with $x_k$ on the diagonal
the vector of dual variables:
$w^k = (AD^2_kA^T)^-1AD^2_kc$
we then compute the vector of reduced costs
$r^k = c-A^Tw^k$
then the theory states (this part I don't quite understand)
that if $r^k > 0$ and $1^TD_kr^k < epsilon$
that the current solution $x_k$ is optimal. why is that?
I am looking at this: https://en.wikipedia.org/wiki/Affine_scaling
thank you for any help
optimization linear-programming affine-geometry
I'm having some trouble understanding the intuition behind the theory of affine scaling(algorithm used in linear programming)
$D_k$ is a diagonal matrix with $x_k$ on the diagonal
the vector of dual variables:
$w^k = (AD^2_kA^T)^-1AD^2_kc$
we then compute the vector of reduced costs
$r^k = c-A^Tw^k$
then the theory states (this part I don't quite understand)
that if $r^k > 0$ and $1^TD_kr^k < epsilon$
that the current solution $x_k$ is optimal. why is that?
I am looking at this: https://en.wikipedia.org/wiki/Affine_scaling
thank you for any help
optimization linear-programming affine-geometry
asked Aug 2 at 15:01
user581239
1
why us a fake profile photo? topbeautyful.blogspot.com/2015/02/…
– LinAlg
Aug 2 at 15:36
@LinAlg: Gets more attention, presumably. Opportunity for mansplaining.
– copper.hat
Aug 2 at 16:46
add a comment |Â
1
why us a fake profile photo? topbeautyful.blogspot.com/2015/02/…
– LinAlg
Aug 2 at 15:36
@LinAlg: Gets more attention, presumably. Opportunity for mansplaining.
– copper.hat
Aug 2 at 16:46
1
1
why us a fake profile photo? topbeautyful.blogspot.com/2015/02/…
– LinAlg
Aug 2 at 15:36
why us a fake profile photo? topbeautyful.blogspot.com/2015/02/…
– LinAlg
Aug 2 at 15:36
@LinAlg: Gets more attention, presumably. Opportunity for mansplaining.
– copper.hat
Aug 2 at 16:46
@LinAlg: Gets more attention, presumably. Opportunity for mansplaining.
– copper.hat
Aug 2 at 16:46
add a comment |Â
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1
why us a fake profile photo? topbeautyful.blogspot.com/2015/02/…
– LinAlg
Aug 2 at 15:36
@LinAlg: Gets more attention, presumably. Opportunity for mansplaining.
– copper.hat
Aug 2 at 16:46