Converting a quadratic constraint into a second-order cone constraint












0












$begingroup$


This is straight from the book: Optimization Methods in Finance.



I'm trying to gain understanding of how the author derived the cone constraints from the the following quadratic constraint:



$$x^TQx + 2p^T x + γ ≤ 0$$



Assuming Matrix $Q$ is positive definite, there exists an invertible matrix, say $R$, satisfying $Q = RR^T$



This allows us to rearrange the equation to:



$$(R^Tx)^T(R^Tx) + 2p^Tx + γ ≤ 0 $$



This is fine and makes sense. However the next step I have had issues understanding:



Define $$ y = (y_1, . . . , y_k)^T = R^T x + R^{−1}p $$



Then we have:



$$ y^Ty = (R^Tx)^T(R^Tx) + 2p^Tx + p^TQ^{-1}p $$



Pictures from the text below show the entire derivation. Any suggestions or clarifications on how the last two lines are derived? Perhaps some linear algebra trick I am not aware of? Thanks!



part1
part2










share|cite|improve this question











$endgroup$








  • 4




    $begingroup$
    No tricks involved. Just the use of basic identities and the fact that $p^Tx=x^Tp$ (since that’s just the dot product of $x$ and $p$).
    $endgroup$
    – amd
    Nov 26 '18 at 0:04










  • $begingroup$
    Thank you, using the dot product rule of x and p did not come to mind!
    $endgroup$
    – user3547551
    Nov 27 '18 at 15:48
















0












$begingroup$


This is straight from the book: Optimization Methods in Finance.



I'm trying to gain understanding of how the author derived the cone constraints from the the following quadratic constraint:



$$x^TQx + 2p^T x + γ ≤ 0$$



Assuming Matrix $Q$ is positive definite, there exists an invertible matrix, say $R$, satisfying $Q = RR^T$



This allows us to rearrange the equation to:



$$(R^Tx)^T(R^Tx) + 2p^Tx + γ ≤ 0 $$



This is fine and makes sense. However the next step I have had issues understanding:



Define $$ y = (y_1, . . . , y_k)^T = R^T x + R^{−1}p $$



Then we have:



$$ y^Ty = (R^Tx)^T(R^Tx) + 2p^Tx + p^TQ^{-1}p $$



Pictures from the text below show the entire derivation. Any suggestions or clarifications on how the last two lines are derived? Perhaps some linear algebra trick I am not aware of? Thanks!



part1
part2










share|cite|improve this question











$endgroup$








  • 4




    $begingroup$
    No tricks involved. Just the use of basic identities and the fact that $p^Tx=x^Tp$ (since that’s just the dot product of $x$ and $p$).
    $endgroup$
    – amd
    Nov 26 '18 at 0:04










  • $begingroup$
    Thank you, using the dot product rule of x and p did not come to mind!
    $endgroup$
    – user3547551
    Nov 27 '18 at 15:48














0












0








0





$begingroup$


This is straight from the book: Optimization Methods in Finance.



I'm trying to gain understanding of how the author derived the cone constraints from the the following quadratic constraint:



$$x^TQx + 2p^T x + γ ≤ 0$$



Assuming Matrix $Q$ is positive definite, there exists an invertible matrix, say $R$, satisfying $Q = RR^T$



This allows us to rearrange the equation to:



$$(R^Tx)^T(R^Tx) + 2p^Tx + γ ≤ 0 $$



This is fine and makes sense. However the next step I have had issues understanding:



Define $$ y = (y_1, . . . , y_k)^T = R^T x + R^{−1}p $$



Then we have:



$$ y^Ty = (R^Tx)^T(R^Tx) + 2p^Tx + p^TQ^{-1}p $$



Pictures from the text below show the entire derivation. Any suggestions or clarifications on how the last two lines are derived? Perhaps some linear algebra trick I am not aware of? Thanks!



part1
part2










share|cite|improve this question











$endgroup$




This is straight from the book: Optimization Methods in Finance.



I'm trying to gain understanding of how the author derived the cone constraints from the the following quadratic constraint:



$$x^TQx + 2p^T x + γ ≤ 0$$



Assuming Matrix $Q$ is positive definite, there exists an invertible matrix, say $R$, satisfying $Q = RR^T$



This allows us to rearrange the equation to:



$$(R^Tx)^T(R^Tx) + 2p^Tx + γ ≤ 0 $$



This is fine and makes sense. However the next step I have had issues understanding:



Define $$ y = (y_1, . . . , y_k)^T = R^T x + R^{−1}p $$



Then we have:



$$ y^Ty = (R^Tx)^T(R^Tx) + 2p^Tx + p^TQ^{-1}p $$



Pictures from the text below show the entire derivation. Any suggestions or clarifications on how the last two lines are derived? Perhaps some linear algebra trick I am not aware of? Thanks!



part1
part2







optimization






share|cite|improve this question















share|cite|improve this question













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share|cite|improve this question








edited Nov 25 '18 at 22:32







user3547551

















asked Nov 25 '18 at 22:23









user3547551user3547551

12




12








  • 4




    $begingroup$
    No tricks involved. Just the use of basic identities and the fact that $p^Tx=x^Tp$ (since that’s just the dot product of $x$ and $p$).
    $endgroup$
    – amd
    Nov 26 '18 at 0:04










  • $begingroup$
    Thank you, using the dot product rule of x and p did not come to mind!
    $endgroup$
    – user3547551
    Nov 27 '18 at 15:48














  • 4




    $begingroup$
    No tricks involved. Just the use of basic identities and the fact that $p^Tx=x^Tp$ (since that’s just the dot product of $x$ and $p$).
    $endgroup$
    – amd
    Nov 26 '18 at 0:04










  • $begingroup$
    Thank you, using the dot product rule of x and p did not come to mind!
    $endgroup$
    – user3547551
    Nov 27 '18 at 15:48








4




4




$begingroup$
No tricks involved. Just the use of basic identities and the fact that $p^Tx=x^Tp$ (since that’s just the dot product of $x$ and $p$).
$endgroup$
– amd
Nov 26 '18 at 0:04




$begingroup$
No tricks involved. Just the use of basic identities and the fact that $p^Tx=x^Tp$ (since that’s just the dot product of $x$ and $p$).
$endgroup$
– amd
Nov 26 '18 at 0:04












$begingroup$
Thank you, using the dot product rule of x and p did not come to mind!
$endgroup$
– user3547551
Nov 27 '18 at 15:48




$begingroup$
Thank you, using the dot product rule of x and p did not come to mind!
$endgroup$
– user3547551
Nov 27 '18 at 15:48










1 Answer
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$begingroup$

If $y=R^Tx$ then



$$|y|_2^2=y^Ty=(R^Tx)^T(R^Tx)=x^TRR^Tx=x^TQx$$



so you can equivalently write your quadratic problem in conic form as



$$t+2p^Tx+gammaleq 0,quad tgeq |y|_2^2.$$



The second constraint is a rotated quadratic cone.






share|cite|improve this answer









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    0












    $begingroup$

    If $y=R^Tx$ then



    $$|y|_2^2=y^Ty=(R^Tx)^T(R^Tx)=x^TRR^Tx=x^TQx$$



    so you can equivalently write your quadratic problem in conic form as



    $$t+2p^Tx+gammaleq 0,quad tgeq |y|_2^2.$$



    The second constraint is a rotated quadratic cone.






    share|cite|improve this answer









    $endgroup$


















      0












      $begingroup$

      If $y=R^Tx$ then



      $$|y|_2^2=y^Ty=(R^Tx)^T(R^Tx)=x^TRR^Tx=x^TQx$$



      so you can equivalently write your quadratic problem in conic form as



      $$t+2p^Tx+gammaleq 0,quad tgeq |y|_2^2.$$



      The second constraint is a rotated quadratic cone.






      share|cite|improve this answer









      $endgroup$
















        0












        0








        0





        $begingroup$

        If $y=R^Tx$ then



        $$|y|_2^2=y^Ty=(R^Tx)^T(R^Tx)=x^TRR^Tx=x^TQx$$



        so you can equivalently write your quadratic problem in conic form as



        $$t+2p^Tx+gammaleq 0,quad tgeq |y|_2^2.$$



        The second constraint is a rotated quadratic cone.






        share|cite|improve this answer









        $endgroup$



        If $y=R^Tx$ then



        $$|y|_2^2=y^Ty=(R^Tx)^T(R^Tx)=x^TRR^Tx=x^TQx$$



        so you can equivalently write your quadratic problem in conic form as



        $$t+2p^Tx+gammaleq 0,quad tgeq |y|_2^2.$$



        The second constraint is a rotated quadratic cone.







        share|cite|improve this answer












        share|cite|improve this answer



        share|cite|improve this answer










        answered Nov 26 '18 at 6:59









        Michal AdamaszekMichal Adamaszek

        2,08148




        2,08148






























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