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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          2017 IndyCar Series Contents Series news Teams and drivers Schedule Season summary Footnotes References External links Navigation menu"INDYCAR: Initial 2018 bodywork concepts unveiled"the original"IndyCar confirms switch to Performance Friction brakes in 2017""AJ Foyt Racing will switch to Chevy"the original"Carlos Munoz, Conor Daly will drive for AJ Foyt Racing""Zach Veach's Indy 500 Debut Confirmed with Foyt""No mass exodus from Honda after Ganassi switch""Ex-F1 driver Sato joins Andretti Autosport for 2017 IndyCar season""IndyCar's Ryan Hunter-Reay, sponsor DHL paired through 2020""hhgregg and Andretti Autosport announce partnership for key races in 2016""INDYCAR: Rossi re-signs with Andretti"the original"McLaren Formula 1 - Fernando Alonso to race at Indy 500 with McLaren, Honda and Andretti Autosport""Shank will finally take part in Indy 500 with Harvey, Andretti | MotorSportsTalk""Andretti adds Jack Harvey to Indy 500 field""Ganassi switches to Honda power for 2017""INDYCAR: Chilton returns to Ganassi"the original"IndyCar silly season: Who's going where in 2017?""INDYCAR: Kanaan, NTT Data return to Ganassi"the original"Kimball to remain at Ganassi for 2017""Coyne confirms Bourdais for 2017 IndyCar season""Davison to sub for Bourdais in Indy 500"the original"Gutierrez confirmed for Detroit IndyCar debut""Gutierrez returns with Coyne for rest of 2017 season""Vautier to drive for Coyne at Texas"the original"INDYCAR: Coyne confirms Jones for 2017"the original"Pippa Mann returns to Coyne for Indy 500""Karam, Dreyer & Reinbold teaming up again for Indianapolis 500""Pigot to return to Ed Carpenter Racing""Hildebrand confirmed as full-time Ed Carpenter driver""Veach to replace injured Hildebrand at Barber"the originalNew Team Harding Racing Enters Chaves for 101st Indianapolis 500"Juncos Racing Announces Entry in 101st Running of the Indianapolis 500 :: Juncos Racing""Juncos confirms Pigot for Indy 500""Saavedra confirmed in Juncos' second 500 entry"the original"Lazier confirms Indy 500 run after son's USF2000 debut"the original"Claman DeMelo to race for RLLR at Sonoma"the original"Rahal signs Servia and ace engineer for 2017""IndyCar: Aleshin returns with Schmidt"the original"Aleshin replaced by Saavedra for Toronto""Jack Harvey will pilot SPM No. 7 car at Watkins Glen, Sonoma""Jay Howard confirmed in Tony Stewart's supported SPM Indy entry""INDYCAR: Newgarden to wave the flag at Penske"the original"Pagenaud opts for No. 1 in 2017"the original"Penske confirms Newgarden for 2017""Montoya to stay with Team Penske in 2017""Target leaving IndyCar after 27 seasons with Chip Ganassi""Cavin: IndyCar could see complete driver/team shakeup in 2017""End of the road for KV Racing?""KV Racing confirms closure, equipment sold to Juncos""Juncos confirms IndyCar Series entry"the original"Juncos readies IndyCar program, aims for '17 500"the original"Harding Racing to add Texas, Pocono to schedule"the original"Sato signs with Andretti Autosport for 2017""INDYCAR: Aleshin in Doubt at SPM"the original"Long Beach notebook: JR Hildebrand breaks hand""Hildebrand cleared to return at Phoenix"the original"Bourdais to undergo surgery on multiple fractures""Aleshin loses Schmidt Peterson IndyCar ride""Saavedra in at SPM for Pocono, Gateway"the original"Bourdais to make return at Gateway"the original"The IndyCar Grand Prix no longer is sponsored by Angie's List""2017 IndyCar Series rulebook""2017 Verizon IndyCar Series Official Rulebook"Official websiteeeeee