Solving overdetermined system by QR decomposition The 2019 Stack Overflow Developer Survey Results Are In Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)What does QR decomposition have to do with least squares method?Check Whether an Overdetermined Linear Equation System is Consistent: General Approach?Is the least squares solution to an overdetermined system a triangle center?Solving a feasible system of linear equations using Linear ProgrammingProving unique solution exists for a system of equationsSolve an overdetermined system of linear equationsSolving overdetermined linear system with $3$ equations in $2$ unknownsOverdetermined System Ax=bSolving a system by using Cholesky Decomposition $(LDL^T)$Solving $Ax = b$ using least squares (minimize $||Ax -b||_2$)How to do Given's rotation for $3x2$ matrix? (QR decomposition)

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Solving overdetermined system by QR decomposition



The 2019 Stack Overflow Developer Survey Results Are In
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)What does QR decomposition have to do with least squares method?Check Whether an Overdetermined Linear Equation System is Consistent: General Approach?Is the least squares solution to an overdetermined system a triangle center?Solving a feasible system of linear equations using Linear ProgrammingProving unique solution exists for a system of equationsSolve an overdetermined system of linear equationsSolving overdetermined linear system with $3$ equations in $2$ unknownsOverdetermined System Ax=bSolving a system by using Cholesky Decomposition $(LDL^T)$Solving $Ax = b$ using least squares (minimize $||Ax -b||_2$)How to do Given's rotation for $3x2$ matrix? (QR decomposition)










3












$begingroup$


I need to solve $Ax=b$ in lots of ways using QR decomposition.



$$A = beginbmatrix
1 & 1 \
-1 & 1 \
1 & 2
endbmatrix, b = beginbmatrix
1 \
0 \
1
endbmatrix$$



This is an overdetermined system. That is, it has more equations than needed for a unique solution.



I need to find $min ||Ax-b||$. How should I solve it using QR?



I know that QR can be used to reduce the problem to
$$Vert Ax - b Vert = Vert QRx - b Vert = Vert Rx - Q^-1b Vert.$$



but what do I do after this?










share|cite|improve this question









$endgroup$
















    3












    $begingroup$


    I need to solve $Ax=b$ in lots of ways using QR decomposition.



    $$A = beginbmatrix
    1 & 1 \
    -1 & 1 \
    1 & 2
    endbmatrix, b = beginbmatrix
    1 \
    0 \
    1
    endbmatrix$$



    This is an overdetermined system. That is, it has more equations than needed for a unique solution.



    I need to find $min ||Ax-b||$. How should I solve it using QR?



    I know that QR can be used to reduce the problem to
    $$Vert Ax - b Vert = Vert QRx - b Vert = Vert Rx - Q^-1b Vert.$$



    but what do I do after this?










    share|cite|improve this question









    $endgroup$














      3












      3








      3





      $begingroup$


      I need to solve $Ax=b$ in lots of ways using QR decomposition.



      $$A = beginbmatrix
      1 & 1 \
      -1 & 1 \
      1 & 2
      endbmatrix, b = beginbmatrix
      1 \
      0 \
      1
      endbmatrix$$



      This is an overdetermined system. That is, it has more equations than needed for a unique solution.



      I need to find $min ||Ax-b||$. How should I solve it using QR?



      I know that QR can be used to reduce the problem to
      $$Vert Ax - b Vert = Vert QRx - b Vert = Vert Rx - Q^-1b Vert.$$



      but what do I do after this?










      share|cite|improve this question









      $endgroup$




      I need to solve $Ax=b$ in lots of ways using QR decomposition.



      $$A = beginbmatrix
      1 & 1 \
      -1 & 1 \
      1 & 2
      endbmatrix, b = beginbmatrix
      1 \
      0 \
      1
      endbmatrix$$



      This is an overdetermined system. That is, it has more equations than needed for a unique solution.



      I need to find $min ||Ax-b||$. How should I solve it using QR?



      I know that QR can be used to reduce the problem to
      $$Vert Ax - b Vert = Vert QRx - b Vert = Vert Rx - Q^-1b Vert.$$



      but what do I do after this?







      linear-algebra numerical-methods numerical-linear-algebra






      share|cite|improve this question













      share|cite|improve this question











      share|cite|improve this question




      share|cite|improve this question










      asked yesterday









      Guerlando OCsGuerlando OCs

      10821856




      10821856




















          2 Answers
          2






          active

          oldest

          votes


















          7












          $begingroup$

          The most straightforward way I know is to pass through the normal equations:



          $$A^T A x = A^T b$$



          and substitute in the $QR$ decomposition of $A$ (with the convention $Q in mathbbR^m times n,R in mathbbR^n times n$). Thus you get



          $$R^T Q^T Q R x = R^T Q^T b.$$



          But $Q^T Q=I_n$. (Note that in this convention $Q$ isn't an orthogonal matrix, so $Q Q^T neq I_m$, but this doesn't matter here.) Thus:



          $$R^T R x = R^T Q^T b.$$



          If $A$ has linearly independent columns (as is usually the case with overdetermined systems), then $R^T$ is injective, so by multiplying both sides by the left inverse of $R^T$ you get



          $$Rx=Q^T b.$$



          This system is now easy to solve numerically.



          For numerical purposes it's important that the removal of $Q^T Q$ and $R^T$ from the problem is done analytically, and in particular $A^T A$ is never constructed numerically.






          share|cite|improve this answer











          $endgroup$








          • 1




            $begingroup$
            Is there any reason to make this so convoluted? From $Ax=b$ you have $QRx=b$, multiply by $Q^T$ on the left.
            $endgroup$
            – Martin Argerami
            yesterday










          • $begingroup$
            @MartinArgerami Because actually the least squares solution usually does not satisfy $Ax=b$. This simple perspective only shows you that this approach gives you a solution when a solution exists. Now you could argue directly that multiplying both sides by $Q^T$ furnishes an equation whose solution is the least squares solution. (Such an argument would resemble the usual geometric argument for deriving the normal equations.) This would make a good alternative answer to mine.
            $endgroup$
            – Ian
            yesterday



















          2












          $begingroup$

          Note that $Rx$ has the form
          $$Rx = beginbmatrix y_1 \ y_2 \ 0endbmatrix $$
          , so if $$ Q^-1b = beginbmatrix z_1 \ z_2 \ z_3endbmatrix$$
          then $|| Rx - Q^-1b||$ will be minimal for $y_1 = z_1$, $y_2=z_2$. This set of equation is no longer overdetermined.



          Using matrix notation, if tou write $R = beginbmatrix R_1 \ 0endbmatrix$ and intoduce $P=beginbmatrix1 & 0 & 0 \ 0 & 1& 0endbmatrix$, then you have
          $$ R_1x = PQ^-1b$$
          $$ x = (R_1)^-1PQ^-1b$$






          share|cite|improve this answer









          $endgroup$








          • 1




            $begingroup$
            The key trick in this answer is that by the orthogonality, $| Ax - b | = | Rx - Q^T b |$.
            $endgroup$
            – Ian
            yesterday










          • $begingroup$
            @Ian, That's something that OP has alredy obtained on his own (since $Q$ is orthogonal, $Q^-1=Q^T$).
            $endgroup$
            – Adam Latosiński
            16 hours ago












          Your Answer








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          2 Answers
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          active

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          2 Answers
          2






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          7












          $begingroup$

          The most straightforward way I know is to pass through the normal equations:



          $$A^T A x = A^T b$$



          and substitute in the $QR$ decomposition of $A$ (with the convention $Q in mathbbR^m times n,R in mathbbR^n times n$). Thus you get



          $$R^T Q^T Q R x = R^T Q^T b.$$



          But $Q^T Q=I_n$. (Note that in this convention $Q$ isn't an orthogonal matrix, so $Q Q^T neq I_m$, but this doesn't matter here.) Thus:



          $$R^T R x = R^T Q^T b.$$



          If $A$ has linearly independent columns (as is usually the case with overdetermined systems), then $R^T$ is injective, so by multiplying both sides by the left inverse of $R^T$ you get



          $$Rx=Q^T b.$$



          This system is now easy to solve numerically.



          For numerical purposes it's important that the removal of $Q^T Q$ and $R^T$ from the problem is done analytically, and in particular $A^T A$ is never constructed numerically.






          share|cite|improve this answer











          $endgroup$








          • 1




            $begingroup$
            Is there any reason to make this so convoluted? From $Ax=b$ you have $QRx=b$, multiply by $Q^T$ on the left.
            $endgroup$
            – Martin Argerami
            yesterday










          • $begingroup$
            @MartinArgerami Because actually the least squares solution usually does not satisfy $Ax=b$. This simple perspective only shows you that this approach gives you a solution when a solution exists. Now you could argue directly that multiplying both sides by $Q^T$ furnishes an equation whose solution is the least squares solution. (Such an argument would resemble the usual geometric argument for deriving the normal equations.) This would make a good alternative answer to mine.
            $endgroup$
            – Ian
            yesterday
















          7












          $begingroup$

          The most straightforward way I know is to pass through the normal equations:



          $$A^T A x = A^T b$$



          and substitute in the $QR$ decomposition of $A$ (with the convention $Q in mathbbR^m times n,R in mathbbR^n times n$). Thus you get



          $$R^T Q^T Q R x = R^T Q^T b.$$



          But $Q^T Q=I_n$. (Note that in this convention $Q$ isn't an orthogonal matrix, so $Q Q^T neq I_m$, but this doesn't matter here.) Thus:



          $$R^T R x = R^T Q^T b.$$



          If $A$ has linearly independent columns (as is usually the case with overdetermined systems), then $R^T$ is injective, so by multiplying both sides by the left inverse of $R^T$ you get



          $$Rx=Q^T b.$$



          This system is now easy to solve numerically.



          For numerical purposes it's important that the removal of $Q^T Q$ and $R^T$ from the problem is done analytically, and in particular $A^T A$ is never constructed numerically.






          share|cite|improve this answer











          $endgroup$








          • 1




            $begingroup$
            Is there any reason to make this so convoluted? From $Ax=b$ you have $QRx=b$, multiply by $Q^T$ on the left.
            $endgroup$
            – Martin Argerami
            yesterday










          • $begingroup$
            @MartinArgerami Because actually the least squares solution usually does not satisfy $Ax=b$. This simple perspective only shows you that this approach gives you a solution when a solution exists. Now you could argue directly that multiplying both sides by $Q^T$ furnishes an equation whose solution is the least squares solution. (Such an argument would resemble the usual geometric argument for deriving the normal equations.) This would make a good alternative answer to mine.
            $endgroup$
            – Ian
            yesterday














          7












          7








          7





          $begingroup$

          The most straightforward way I know is to pass through the normal equations:



          $$A^T A x = A^T b$$



          and substitute in the $QR$ decomposition of $A$ (with the convention $Q in mathbbR^m times n,R in mathbbR^n times n$). Thus you get



          $$R^T Q^T Q R x = R^T Q^T b.$$



          But $Q^T Q=I_n$. (Note that in this convention $Q$ isn't an orthogonal matrix, so $Q Q^T neq I_m$, but this doesn't matter here.) Thus:



          $$R^T R x = R^T Q^T b.$$



          If $A$ has linearly independent columns (as is usually the case with overdetermined systems), then $R^T$ is injective, so by multiplying both sides by the left inverse of $R^T$ you get



          $$Rx=Q^T b.$$



          This system is now easy to solve numerically.



          For numerical purposes it's important that the removal of $Q^T Q$ and $R^T$ from the problem is done analytically, and in particular $A^T A$ is never constructed numerically.






          share|cite|improve this answer











          $endgroup$



          The most straightforward way I know is to pass through the normal equations:



          $$A^T A x = A^T b$$



          and substitute in the $QR$ decomposition of $A$ (with the convention $Q in mathbbR^m times n,R in mathbbR^n times n$). Thus you get



          $$R^T Q^T Q R x = R^T Q^T b.$$



          But $Q^T Q=I_n$. (Note that in this convention $Q$ isn't an orthogonal matrix, so $Q Q^T neq I_m$, but this doesn't matter here.) Thus:



          $$R^T R x = R^T Q^T b.$$



          If $A$ has linearly independent columns (as is usually the case with overdetermined systems), then $R^T$ is injective, so by multiplying both sides by the left inverse of $R^T$ you get



          $$Rx=Q^T b.$$



          This system is now easy to solve numerically.



          For numerical purposes it's important that the removal of $Q^T Q$ and $R^T$ from the problem is done analytically, and in particular $A^T A$ is never constructed numerically.







          share|cite|improve this answer














          share|cite|improve this answer



          share|cite|improve this answer








          edited yesterday

























          answered yesterday









          IanIan

          69.2k25392




          69.2k25392







          • 1




            $begingroup$
            Is there any reason to make this so convoluted? From $Ax=b$ you have $QRx=b$, multiply by $Q^T$ on the left.
            $endgroup$
            – Martin Argerami
            yesterday










          • $begingroup$
            @MartinArgerami Because actually the least squares solution usually does not satisfy $Ax=b$. This simple perspective only shows you that this approach gives you a solution when a solution exists. Now you could argue directly that multiplying both sides by $Q^T$ furnishes an equation whose solution is the least squares solution. (Such an argument would resemble the usual geometric argument for deriving the normal equations.) This would make a good alternative answer to mine.
            $endgroup$
            – Ian
            yesterday













          • 1




            $begingroup$
            Is there any reason to make this so convoluted? From $Ax=b$ you have $QRx=b$, multiply by $Q^T$ on the left.
            $endgroup$
            – Martin Argerami
            yesterday










          • $begingroup$
            @MartinArgerami Because actually the least squares solution usually does not satisfy $Ax=b$. This simple perspective only shows you that this approach gives you a solution when a solution exists. Now you could argue directly that multiplying both sides by $Q^T$ furnishes an equation whose solution is the least squares solution. (Such an argument would resemble the usual geometric argument for deriving the normal equations.) This would make a good alternative answer to mine.
            $endgroup$
            – Ian
            yesterday








          1




          1




          $begingroup$
          Is there any reason to make this so convoluted? From $Ax=b$ you have $QRx=b$, multiply by $Q^T$ on the left.
          $endgroup$
          – Martin Argerami
          yesterday




          $begingroup$
          Is there any reason to make this so convoluted? From $Ax=b$ you have $QRx=b$, multiply by $Q^T$ on the left.
          $endgroup$
          – Martin Argerami
          yesterday












          $begingroup$
          @MartinArgerami Because actually the least squares solution usually does not satisfy $Ax=b$. This simple perspective only shows you that this approach gives you a solution when a solution exists. Now you could argue directly that multiplying both sides by $Q^T$ furnishes an equation whose solution is the least squares solution. (Such an argument would resemble the usual geometric argument for deriving the normal equations.) This would make a good alternative answer to mine.
          $endgroup$
          – Ian
          yesterday





          $begingroup$
          @MartinArgerami Because actually the least squares solution usually does not satisfy $Ax=b$. This simple perspective only shows you that this approach gives you a solution when a solution exists. Now you could argue directly that multiplying both sides by $Q^T$ furnishes an equation whose solution is the least squares solution. (Such an argument would resemble the usual geometric argument for deriving the normal equations.) This would make a good alternative answer to mine.
          $endgroup$
          – Ian
          yesterday












          2












          $begingroup$

          Note that $Rx$ has the form
          $$Rx = beginbmatrix y_1 \ y_2 \ 0endbmatrix $$
          , so if $$ Q^-1b = beginbmatrix z_1 \ z_2 \ z_3endbmatrix$$
          then $|| Rx - Q^-1b||$ will be minimal for $y_1 = z_1$, $y_2=z_2$. This set of equation is no longer overdetermined.



          Using matrix notation, if tou write $R = beginbmatrix R_1 \ 0endbmatrix$ and intoduce $P=beginbmatrix1 & 0 & 0 \ 0 & 1& 0endbmatrix$, then you have
          $$ R_1x = PQ^-1b$$
          $$ x = (R_1)^-1PQ^-1b$$






          share|cite|improve this answer









          $endgroup$








          • 1




            $begingroup$
            The key trick in this answer is that by the orthogonality, $| Ax - b | = | Rx - Q^T b |$.
            $endgroup$
            – Ian
            yesterday










          • $begingroup$
            @Ian, That's something that OP has alredy obtained on his own (since $Q$ is orthogonal, $Q^-1=Q^T$).
            $endgroup$
            – Adam Latosiński
            16 hours ago
















          2












          $begingroup$

          Note that $Rx$ has the form
          $$Rx = beginbmatrix y_1 \ y_2 \ 0endbmatrix $$
          , so if $$ Q^-1b = beginbmatrix z_1 \ z_2 \ z_3endbmatrix$$
          then $|| Rx - Q^-1b||$ will be minimal for $y_1 = z_1$, $y_2=z_2$. This set of equation is no longer overdetermined.



          Using matrix notation, if tou write $R = beginbmatrix R_1 \ 0endbmatrix$ and intoduce $P=beginbmatrix1 & 0 & 0 \ 0 & 1& 0endbmatrix$, then you have
          $$ R_1x = PQ^-1b$$
          $$ x = (R_1)^-1PQ^-1b$$






          share|cite|improve this answer









          $endgroup$








          • 1




            $begingroup$
            The key trick in this answer is that by the orthogonality, $| Ax - b | = | Rx - Q^T b |$.
            $endgroup$
            – Ian
            yesterday










          • $begingroup$
            @Ian, That's something that OP has alredy obtained on his own (since $Q$ is orthogonal, $Q^-1=Q^T$).
            $endgroup$
            – Adam Latosiński
            16 hours ago














          2












          2








          2





          $begingroup$

          Note that $Rx$ has the form
          $$Rx = beginbmatrix y_1 \ y_2 \ 0endbmatrix $$
          , so if $$ Q^-1b = beginbmatrix z_1 \ z_2 \ z_3endbmatrix$$
          then $|| Rx - Q^-1b||$ will be minimal for $y_1 = z_1$, $y_2=z_2$. This set of equation is no longer overdetermined.



          Using matrix notation, if tou write $R = beginbmatrix R_1 \ 0endbmatrix$ and intoduce $P=beginbmatrix1 & 0 & 0 \ 0 & 1& 0endbmatrix$, then you have
          $$ R_1x = PQ^-1b$$
          $$ x = (R_1)^-1PQ^-1b$$






          share|cite|improve this answer









          $endgroup$



          Note that $Rx$ has the form
          $$Rx = beginbmatrix y_1 \ y_2 \ 0endbmatrix $$
          , so if $$ Q^-1b = beginbmatrix z_1 \ z_2 \ z_3endbmatrix$$
          then $|| Rx - Q^-1b||$ will be minimal for $y_1 = z_1$, $y_2=z_2$. This set of equation is no longer overdetermined.



          Using matrix notation, if tou write $R = beginbmatrix R_1 \ 0endbmatrix$ and intoduce $P=beginbmatrix1 & 0 & 0 \ 0 & 1& 0endbmatrix$, then you have
          $$ R_1x = PQ^-1b$$
          $$ x = (R_1)^-1PQ^-1b$$







          share|cite|improve this answer












          share|cite|improve this answer



          share|cite|improve this answer










          answered yesterday









          Adam LatosińskiAdam Latosiński

          6068




          6068







          • 1




            $begingroup$
            The key trick in this answer is that by the orthogonality, $| Ax - b | = | Rx - Q^T b |$.
            $endgroup$
            – Ian
            yesterday










          • $begingroup$
            @Ian, That's something that OP has alredy obtained on his own (since $Q$ is orthogonal, $Q^-1=Q^T$).
            $endgroup$
            – Adam Latosiński
            16 hours ago













          • 1




            $begingroup$
            The key trick in this answer is that by the orthogonality, $| Ax - b | = | Rx - Q^T b |$.
            $endgroup$
            – Ian
            yesterday










          • $begingroup$
            @Ian, That's something that OP has alredy obtained on his own (since $Q$ is orthogonal, $Q^-1=Q^T$).
            $endgroup$
            – Adam Latosiński
            16 hours ago








          1




          1




          $begingroup$
          The key trick in this answer is that by the orthogonality, $| Ax - b | = | Rx - Q^T b |$.
          $endgroup$
          – Ian
          yesterday




          $begingroup$
          The key trick in this answer is that by the orthogonality, $| Ax - b | = | Rx - Q^T b |$.
          $endgroup$
          – Ian
          yesterday












          $begingroup$
          @Ian, That's something that OP has alredy obtained on his own (since $Q$ is orthogonal, $Q^-1=Q^T$).
          $endgroup$
          – Adam Latosiński
          16 hours ago





          $begingroup$
          @Ian, That's something that OP has alredy obtained on his own (since $Q$ is orthogonal, $Q^-1=Q^T$).
          $endgroup$
          – Adam Latosiński
          16 hours ago


















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