How to construct a matrix given the null basis of A?

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Construct a 4x4 matrix A such that ((1,2,3,4),(1,1,2,2)) is a basis of N(A).



So i know that A will have two pivot columns and two free columns, but beyond this I'm not sure how to approach/solve.







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  • Try taking an general $4times 4$-matrix $A$ and establish what it means for bother vectors to be in the null space. This will gives you a system of equations over the variables of the matrix. See if you can continue from there.
    – zzuussee
    Aug 10 at 23:49










  • These two null space columns would be used to eliminate the free columns. Can I attempt to perform elimination on the null space columns?
    – Alan Dennison
    Aug 11 at 0:03










  • I'm not sure what elimination on the null space columns should achieve. @Cleric posted a nice answer in connection with my comment. Maybe this clears some things.
    – zzuussee
    Aug 11 at 0:07










  • If you need to find all matrices satisfying that condition, then you can use the approach you were given. If you only need one such matrix as indicated by the "a 4x4 matrix", then there are shortcuts that you can take to produce just one example. Let $v_1,v_2$ be those two vectors given. Let $v$ be an unknown vector. Consider the system of two equations $vcdot v_1=0$ and $vcdot v_2=0$, in the $4$ unknown coordinates of $v$. You can do row reduction and find two linearly independent solutions $u_1,u_2$. Then the matrix formed by putting $u_1,u_2,u_1,u_2$ as rows will solve the problem.
    – user583012
    Aug 11 at 0:10











  • Why would this solve the problem? On one hand, then you multiply the matrix by $v_1$, the components of the result will be dot products of the rows of the matrix and $v_1$. But $u_1,u_2$ were chosen such that those products are zero. Likewise for $v_2$. Now, since you chose $u_1,u_2$ linearly independent, then the rank of the matrix is $2$. Since $v_1,v_2$ are also linearly independent, then the nullity of the matrix is at least $2$. But since the rank is $2$, the nullity must be exactly $4-2=2$.
    – user583012
    Aug 11 at 0:14














up vote
1
down vote

favorite












Construct a 4x4 matrix A such that ((1,2,3,4),(1,1,2,2)) is a basis of N(A).



So i know that A will have two pivot columns and two free columns, but beyond this I'm not sure how to approach/solve.







share|cite|improve this question






















  • Try taking an general $4times 4$-matrix $A$ and establish what it means for bother vectors to be in the null space. This will gives you a system of equations over the variables of the matrix. See if you can continue from there.
    – zzuussee
    Aug 10 at 23:49










  • These two null space columns would be used to eliminate the free columns. Can I attempt to perform elimination on the null space columns?
    – Alan Dennison
    Aug 11 at 0:03










  • I'm not sure what elimination on the null space columns should achieve. @Cleric posted a nice answer in connection with my comment. Maybe this clears some things.
    – zzuussee
    Aug 11 at 0:07










  • If you need to find all matrices satisfying that condition, then you can use the approach you were given. If you only need one such matrix as indicated by the "a 4x4 matrix", then there are shortcuts that you can take to produce just one example. Let $v_1,v_2$ be those two vectors given. Let $v$ be an unknown vector. Consider the system of two equations $vcdot v_1=0$ and $vcdot v_2=0$, in the $4$ unknown coordinates of $v$. You can do row reduction and find two linearly independent solutions $u_1,u_2$. Then the matrix formed by putting $u_1,u_2,u_1,u_2$ as rows will solve the problem.
    – user583012
    Aug 11 at 0:10











  • Why would this solve the problem? On one hand, then you multiply the matrix by $v_1$, the components of the result will be dot products of the rows of the matrix and $v_1$. But $u_1,u_2$ were chosen such that those products are zero. Likewise for $v_2$. Now, since you chose $u_1,u_2$ linearly independent, then the rank of the matrix is $2$. Since $v_1,v_2$ are also linearly independent, then the nullity of the matrix is at least $2$. But since the rank is $2$, the nullity must be exactly $4-2=2$.
    – user583012
    Aug 11 at 0:14












up vote
1
down vote

favorite









up vote
1
down vote

favorite











Construct a 4x4 matrix A such that ((1,2,3,4),(1,1,2,2)) is a basis of N(A).



So i know that A will have two pivot columns and two free columns, but beyond this I'm not sure how to approach/solve.







share|cite|improve this question














Construct a 4x4 matrix A such that ((1,2,3,4),(1,1,2,2)) is a basis of N(A).



So i know that A will have two pivot columns and two free columns, but beyond this I'm not sure how to approach/solve.









share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited Aug 11 at 18:52

























asked Aug 10 at 23:44









Alan Dennison

144




144











  • Try taking an general $4times 4$-matrix $A$ and establish what it means for bother vectors to be in the null space. This will gives you a system of equations over the variables of the matrix. See if you can continue from there.
    – zzuussee
    Aug 10 at 23:49










  • These two null space columns would be used to eliminate the free columns. Can I attempt to perform elimination on the null space columns?
    – Alan Dennison
    Aug 11 at 0:03










  • I'm not sure what elimination on the null space columns should achieve. @Cleric posted a nice answer in connection with my comment. Maybe this clears some things.
    – zzuussee
    Aug 11 at 0:07










  • If you need to find all matrices satisfying that condition, then you can use the approach you were given. If you only need one such matrix as indicated by the "a 4x4 matrix", then there are shortcuts that you can take to produce just one example. Let $v_1,v_2$ be those two vectors given. Let $v$ be an unknown vector. Consider the system of two equations $vcdot v_1=0$ and $vcdot v_2=0$, in the $4$ unknown coordinates of $v$. You can do row reduction and find two linearly independent solutions $u_1,u_2$. Then the matrix formed by putting $u_1,u_2,u_1,u_2$ as rows will solve the problem.
    – user583012
    Aug 11 at 0:10











  • Why would this solve the problem? On one hand, then you multiply the matrix by $v_1$, the components of the result will be dot products of the rows of the matrix and $v_1$. But $u_1,u_2$ were chosen such that those products are zero. Likewise for $v_2$. Now, since you chose $u_1,u_2$ linearly independent, then the rank of the matrix is $2$. Since $v_1,v_2$ are also linearly independent, then the nullity of the matrix is at least $2$. But since the rank is $2$, the nullity must be exactly $4-2=2$.
    – user583012
    Aug 11 at 0:14
















  • Try taking an general $4times 4$-matrix $A$ and establish what it means for bother vectors to be in the null space. This will gives you a system of equations over the variables of the matrix. See if you can continue from there.
    – zzuussee
    Aug 10 at 23:49










  • These two null space columns would be used to eliminate the free columns. Can I attempt to perform elimination on the null space columns?
    – Alan Dennison
    Aug 11 at 0:03










  • I'm not sure what elimination on the null space columns should achieve. @Cleric posted a nice answer in connection with my comment. Maybe this clears some things.
    – zzuussee
    Aug 11 at 0:07










  • If you need to find all matrices satisfying that condition, then you can use the approach you were given. If you only need one such matrix as indicated by the "a 4x4 matrix", then there are shortcuts that you can take to produce just one example. Let $v_1,v_2$ be those two vectors given. Let $v$ be an unknown vector. Consider the system of two equations $vcdot v_1=0$ and $vcdot v_2=0$, in the $4$ unknown coordinates of $v$. You can do row reduction and find two linearly independent solutions $u_1,u_2$. Then the matrix formed by putting $u_1,u_2,u_1,u_2$ as rows will solve the problem.
    – user583012
    Aug 11 at 0:10











  • Why would this solve the problem? On one hand, then you multiply the matrix by $v_1$, the components of the result will be dot products of the rows of the matrix and $v_1$. But $u_1,u_2$ were chosen such that those products are zero. Likewise for $v_2$. Now, since you chose $u_1,u_2$ linearly independent, then the rank of the matrix is $2$. Since $v_1,v_2$ are also linearly independent, then the nullity of the matrix is at least $2$. But since the rank is $2$, the nullity must be exactly $4-2=2$.
    – user583012
    Aug 11 at 0:14















Try taking an general $4times 4$-matrix $A$ and establish what it means for bother vectors to be in the null space. This will gives you a system of equations over the variables of the matrix. See if you can continue from there.
– zzuussee
Aug 10 at 23:49




Try taking an general $4times 4$-matrix $A$ and establish what it means for bother vectors to be in the null space. This will gives you a system of equations over the variables of the matrix. See if you can continue from there.
– zzuussee
Aug 10 at 23:49












These two null space columns would be used to eliminate the free columns. Can I attempt to perform elimination on the null space columns?
– Alan Dennison
Aug 11 at 0:03




These two null space columns would be used to eliminate the free columns. Can I attempt to perform elimination on the null space columns?
– Alan Dennison
Aug 11 at 0:03












I'm not sure what elimination on the null space columns should achieve. @Cleric posted a nice answer in connection with my comment. Maybe this clears some things.
– zzuussee
Aug 11 at 0:07




I'm not sure what elimination on the null space columns should achieve. @Cleric posted a nice answer in connection with my comment. Maybe this clears some things.
– zzuussee
Aug 11 at 0:07












If you need to find all matrices satisfying that condition, then you can use the approach you were given. If you only need one such matrix as indicated by the "a 4x4 matrix", then there are shortcuts that you can take to produce just one example. Let $v_1,v_2$ be those two vectors given. Let $v$ be an unknown vector. Consider the system of two equations $vcdot v_1=0$ and $vcdot v_2=0$, in the $4$ unknown coordinates of $v$. You can do row reduction and find two linearly independent solutions $u_1,u_2$. Then the matrix formed by putting $u_1,u_2,u_1,u_2$ as rows will solve the problem.
– user583012
Aug 11 at 0:10





If you need to find all matrices satisfying that condition, then you can use the approach you were given. If you only need one such matrix as indicated by the "a 4x4 matrix", then there are shortcuts that you can take to produce just one example. Let $v_1,v_2$ be those two vectors given. Let $v$ be an unknown vector. Consider the system of two equations $vcdot v_1=0$ and $vcdot v_2=0$, in the $4$ unknown coordinates of $v$. You can do row reduction and find two linearly independent solutions $u_1,u_2$. Then the matrix formed by putting $u_1,u_2,u_1,u_2$ as rows will solve the problem.
– user583012
Aug 11 at 0:10













Why would this solve the problem? On one hand, then you multiply the matrix by $v_1$, the components of the result will be dot products of the rows of the matrix and $v_1$. But $u_1,u_2$ were chosen such that those products are zero. Likewise for $v_2$. Now, since you chose $u_1,u_2$ linearly independent, then the rank of the matrix is $2$. Since $v_1,v_2$ are also linearly independent, then the nullity of the matrix is at least $2$. But since the rank is $2$, the nullity must be exactly $4-2=2$.
– user583012
Aug 11 at 0:14




Why would this solve the problem? On one hand, then you multiply the matrix by $v_1$, the components of the result will be dot products of the rows of the matrix and $v_1$. But $u_1,u_2$ were chosen such that those products are zero. Likewise for $v_2$. Now, since you chose $u_1,u_2$ linearly independent, then the rank of the matrix is $2$. Since $v_1,v_2$ are also linearly independent, then the nullity of the matrix is at least $2$. But since the rank is $2$, the nullity must be exactly $4-2=2$.
– user583012
Aug 11 at 0:14










5 Answers
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The row space of a matrix is the orthogonal complement of its null space. So, you can construct the required matrix by finding a basis for this orthogonal complement. In this case, this will give you two of the rows, and the other two rows can be any linear combinations of those two rows, including rows of all zeros.



Calling the two given vectors $mathbf n_1$ and $mathbf n_2$, the orthogonal complement of their span is the set of all vectors $mathbf x$ that satisfy $mathbf n_1cdotmathbf x=mathbf n_2cdotmathbf x=0$. This is a pair of homogeneous linear equations in the components of $mathbf x$, so $mathscr N(A)^perp$ is the null space of the matrix $smallbeginbmatrixmathbf n_1 & mathbf n_2endbmatrix^T$. I’m sure you know how to compute that.






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  • This was super helpful!
    – Alan Dennison
    Aug 11 at 20:10

















up vote
2
down vote













You could for instance say that you have a matrix
beginequation
A
=
beginbmatrix
a_11 & a_12 & a_13 & a_14 \
a_21 & a_22 & a_23 & a_24 \
a_31 & a_32 & a_33 & a_34 \
a_41 & a_42 & a_43 & a_44 \
endbmatrix
endequation
such that
beginequation
AX = 0
endequation
where
beginequation
X =
beginbmatrix
1 & 1 \
2 & 1 \
3 & 2\
4 & 2
endbmatrix
endequation
Hence we have to find $a_ij$'s such that
beginequation
beginbmatrix
a_11 & a_12 & a_13 & a_14 \
a_21 & a_22 & a_23 & a_24 \
a_31 & a_32 & a_33 & a_34 \
a_41 & a_42 & a_43 & a_44 \
endbmatrix
beginbmatrix
1 & 1 \
2 & 1 \
3 & 2\
4 & 2
endbmatrix
=
0
endequation
i.e. we have the following system to solve:
beginalign
a_11 + 2a_12 + 3a_13 + 4a_14 & = 0 \
a_21 + 2a_22 + 3a_23 + 4a_24 & = 0 \
a_31 + 2a_32 + 3a_33 + 4a_34 & = 0 \
a_41 + 2a_42 + 3a_43 + 4a_44 & = 0 \
a_11 + a_12 + 2a_13 + 2a_14 & = 0 \
a_21 + a_22 + 2a_23 + 2a_24 & = 0 \
a_31 + a_32 + 2a_33 + 2a_34 & = 0 \
a_41 + a_42 + 2a_43 + 2a_44 & = 0
endalign
This is exhaustive as it is a system of 8 equations in 16 unknowns. Therefore, we will have infinitely many solutions. \
For example, you could construct a projector matrix $P_X$ that spans the columns of $X$, i.e.
beginequation
P_X = X(X^TX)^-1X^T
=
beginbmatrix
0.5455 & -0.0909 & 0.4545 & -0.1818\
-0.0909 & 0.1818 & 0.0909 & 0.3636\
0.4545 & 0.0909 & 0.5455 & 0.1818\
-0.1818 & 0.3636 & 0.1818 & 0.7273
endbmatrix
endequation
And then you can say that my matrix $A$ spans the null space of $P_X$, i.e.
beginequation
A = I - P_X
=
beginbmatrix
0.4545 & 0.0909 & -0.4545 & 0.1818\
0.0909 & 0.8182 & -0.0909 & -0.3636\
-0.4545 & -0.0909 & 0.4545& -0.1818\
0.1818 & -0.3636 & -0.1818 & 0.2727
endbmatrix
endequation
Now check,



beginequation
AX =(I - P_X)X = X - P_XX = X - X(X^TX)^-1X^TX = X-X = 0
endequation
and voila there you have a matrix with nullspace being the columns of $X$.






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    1
    down vote













    Let me show you how it would go. Call $v_1=(1,2,3,4)$ and $v_2=(1,1,2,2)$.



    Let $v=(x_1,x_2,x_3,x_4)$. We want $vcdot v_1=0$ and $vcdot v_2=0$. This gives us the system of equations $$beginalignx_1+2x_2+3x_3+4x_4&=0\x_1+x_2+2x_3+2x_4&=0endalign$$



    Subtracting the first equation from the second we get: $$beginalignx_1+2x_2+3x_3+4x_4&=0\phantomx_1-x_2-x_3-2x_4&=0endalign$$



    Now the system is in echelon form. In order to get two linearly independent solutions we can put first $x_3=1$, $x_4=0$ and solve for $x_1,x_2$, and then put $x_3=0$, $x_4=1$ and solve for $x_1,x_2$.



    Doing that gives us two vectors $$beginalignu_1&=(-1,-1,1,0)\u_2&=(0,-2,0,1)endalign$$



    Therefore, one matrix satisfying the conditions of the problem would be $$beginpmatrix-1,&-1,&1,&0\0,&-2,&0,&1\-1,&-1,&1,&0\0,&-2,&0,&1endpmatrix$$



    obtained by putting $u_1,u_2$ and again $u_1,u_2$ as rows.






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    • This was super helpful!
      – Alan Dennison
      Aug 11 at 20:10

















    up vote
    0
    down vote













    Here is a more detailed tip based on zzuussee's comment:



    Let



    $$A=beginbmatrixa_11&a_12&a_13&a_14\a_21&a_22&a_23&a_24\a_31&a_32&a_33&a_34\a_41&a_42&a_43&a_44endbmatrix.$$



    What could we say about each $a_ij$ if we require that



    $$Abeginbmatrix1\2\3\4endbmatrix=Abeginbmatrix1\1\2\2endbmatrix=beginbmatrix0\0\0\0endbmatrix?$$



    Can you choose values for each $a_ij$ that satisfy the above requirements?






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      up vote
      0
      down vote













      It's well-known that a matrix is determined by its effect on a basis. So let's complete our null basis to a basis for $mathbb R^4$. For instance, we could use $e_1=(1,0,0,0)$ and $e_2=(0,1,0,0)$.



      Now, if we call our basis $beta$, then $[T]_beta^beta=beginpmatrix1&0&0&0\0&1&0&0\0&0&0&0\0&0&0&0endpmatrix$ would be a matrix with the right null space (actually the first two columns could be any two vectors that are linearly independent...)



      Meanwhile, the change of basis matrix from $beta$ to the standard basis is: $P=beginpmatrix1&0&1&1\0&1&2&1\0&0&3&2\0&0&4&2endpmatrix$.



      So, we could let $A=P[T]_beta^betaP^-1$.



      I get $A=beginpmatrix1&0&-1&frac12\0&1&0&-frac12\0&0&0&0\0&0&0&0endpmatrix$.






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        5 Answers
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        active

        oldest

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        5 Answers
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        active

        oldest

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        active

        oldest

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        active

        oldest

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        up vote
        2
        down vote













        The row space of a matrix is the orthogonal complement of its null space. So, you can construct the required matrix by finding a basis for this orthogonal complement. In this case, this will give you two of the rows, and the other two rows can be any linear combinations of those two rows, including rows of all zeros.



        Calling the two given vectors $mathbf n_1$ and $mathbf n_2$, the orthogonal complement of their span is the set of all vectors $mathbf x$ that satisfy $mathbf n_1cdotmathbf x=mathbf n_2cdotmathbf x=0$. This is a pair of homogeneous linear equations in the components of $mathbf x$, so $mathscr N(A)^perp$ is the null space of the matrix $smallbeginbmatrixmathbf n_1 & mathbf n_2endbmatrix^T$. I’m sure you know how to compute that.






        share|cite|improve this answer




















        • This was super helpful!
          – Alan Dennison
          Aug 11 at 20:10














        up vote
        2
        down vote













        The row space of a matrix is the orthogonal complement of its null space. So, you can construct the required matrix by finding a basis for this orthogonal complement. In this case, this will give you two of the rows, and the other two rows can be any linear combinations of those two rows, including rows of all zeros.



        Calling the two given vectors $mathbf n_1$ and $mathbf n_2$, the orthogonal complement of their span is the set of all vectors $mathbf x$ that satisfy $mathbf n_1cdotmathbf x=mathbf n_2cdotmathbf x=0$. This is a pair of homogeneous linear equations in the components of $mathbf x$, so $mathscr N(A)^perp$ is the null space of the matrix $smallbeginbmatrixmathbf n_1 & mathbf n_2endbmatrix^T$. I’m sure you know how to compute that.






        share|cite|improve this answer




















        • This was super helpful!
          – Alan Dennison
          Aug 11 at 20:10












        up vote
        2
        down vote










        up vote
        2
        down vote









        The row space of a matrix is the orthogonal complement of its null space. So, you can construct the required matrix by finding a basis for this orthogonal complement. In this case, this will give you two of the rows, and the other two rows can be any linear combinations of those two rows, including rows of all zeros.



        Calling the two given vectors $mathbf n_1$ and $mathbf n_2$, the orthogonal complement of their span is the set of all vectors $mathbf x$ that satisfy $mathbf n_1cdotmathbf x=mathbf n_2cdotmathbf x=0$. This is a pair of homogeneous linear equations in the components of $mathbf x$, so $mathscr N(A)^perp$ is the null space of the matrix $smallbeginbmatrixmathbf n_1 & mathbf n_2endbmatrix^T$. I’m sure you know how to compute that.






        share|cite|improve this answer












        The row space of a matrix is the orthogonal complement of its null space. So, you can construct the required matrix by finding a basis for this orthogonal complement. In this case, this will give you two of the rows, and the other two rows can be any linear combinations of those two rows, including rows of all zeros.



        Calling the two given vectors $mathbf n_1$ and $mathbf n_2$, the orthogonal complement of their span is the set of all vectors $mathbf x$ that satisfy $mathbf n_1cdotmathbf x=mathbf n_2cdotmathbf x=0$. This is a pair of homogeneous linear equations in the components of $mathbf x$, so $mathscr N(A)^perp$ is the null space of the matrix $smallbeginbmatrixmathbf n_1 & mathbf n_2endbmatrix^T$. I’m sure you know how to compute that.







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        answered Aug 11 at 1:19









        amd

        26.1k2944




        26.1k2944











        • This was super helpful!
          – Alan Dennison
          Aug 11 at 20:10
















        • This was super helpful!
          – Alan Dennison
          Aug 11 at 20:10















        This was super helpful!
        – Alan Dennison
        Aug 11 at 20:10




        This was super helpful!
        – Alan Dennison
        Aug 11 at 20:10










        up vote
        2
        down vote













        You could for instance say that you have a matrix
        beginequation
        A
        =
        beginbmatrix
        a_11 & a_12 & a_13 & a_14 \
        a_21 & a_22 & a_23 & a_24 \
        a_31 & a_32 & a_33 & a_34 \
        a_41 & a_42 & a_43 & a_44 \
        endbmatrix
        endequation
        such that
        beginequation
        AX = 0
        endequation
        where
        beginequation
        X =
        beginbmatrix
        1 & 1 \
        2 & 1 \
        3 & 2\
        4 & 2
        endbmatrix
        endequation
        Hence we have to find $a_ij$'s such that
        beginequation
        beginbmatrix
        a_11 & a_12 & a_13 & a_14 \
        a_21 & a_22 & a_23 & a_24 \
        a_31 & a_32 & a_33 & a_34 \
        a_41 & a_42 & a_43 & a_44 \
        endbmatrix
        beginbmatrix
        1 & 1 \
        2 & 1 \
        3 & 2\
        4 & 2
        endbmatrix
        =
        0
        endequation
        i.e. we have the following system to solve:
        beginalign
        a_11 + 2a_12 + 3a_13 + 4a_14 & = 0 \
        a_21 + 2a_22 + 3a_23 + 4a_24 & = 0 \
        a_31 + 2a_32 + 3a_33 + 4a_34 & = 0 \
        a_41 + 2a_42 + 3a_43 + 4a_44 & = 0 \
        a_11 + a_12 + 2a_13 + 2a_14 & = 0 \
        a_21 + a_22 + 2a_23 + 2a_24 & = 0 \
        a_31 + a_32 + 2a_33 + 2a_34 & = 0 \
        a_41 + a_42 + 2a_43 + 2a_44 & = 0
        endalign
        This is exhaustive as it is a system of 8 equations in 16 unknowns. Therefore, we will have infinitely many solutions. \
        For example, you could construct a projector matrix $P_X$ that spans the columns of $X$, i.e.
        beginequation
        P_X = X(X^TX)^-1X^T
        =
        beginbmatrix
        0.5455 & -0.0909 & 0.4545 & -0.1818\
        -0.0909 & 0.1818 & 0.0909 & 0.3636\
        0.4545 & 0.0909 & 0.5455 & 0.1818\
        -0.1818 & 0.3636 & 0.1818 & 0.7273
        endbmatrix
        endequation
        And then you can say that my matrix $A$ spans the null space of $P_X$, i.e.
        beginequation
        A = I - P_X
        =
        beginbmatrix
        0.4545 & 0.0909 & -0.4545 & 0.1818\
        0.0909 & 0.8182 & -0.0909 & -0.3636\
        -0.4545 & -0.0909 & 0.4545& -0.1818\
        0.1818 & -0.3636 & -0.1818 & 0.2727
        endbmatrix
        endequation
        Now check,



        beginequation
        AX =(I - P_X)X = X - P_XX = X - X(X^TX)^-1X^TX = X-X = 0
        endequation
        and voila there you have a matrix with nullspace being the columns of $X$.






        share|cite|improve this answer
























          up vote
          2
          down vote













          You could for instance say that you have a matrix
          beginequation
          A
          =
          beginbmatrix
          a_11 & a_12 & a_13 & a_14 \
          a_21 & a_22 & a_23 & a_24 \
          a_31 & a_32 & a_33 & a_34 \
          a_41 & a_42 & a_43 & a_44 \
          endbmatrix
          endequation
          such that
          beginequation
          AX = 0
          endequation
          where
          beginequation
          X =
          beginbmatrix
          1 & 1 \
          2 & 1 \
          3 & 2\
          4 & 2
          endbmatrix
          endequation
          Hence we have to find $a_ij$'s such that
          beginequation
          beginbmatrix
          a_11 & a_12 & a_13 & a_14 \
          a_21 & a_22 & a_23 & a_24 \
          a_31 & a_32 & a_33 & a_34 \
          a_41 & a_42 & a_43 & a_44 \
          endbmatrix
          beginbmatrix
          1 & 1 \
          2 & 1 \
          3 & 2\
          4 & 2
          endbmatrix
          =
          0
          endequation
          i.e. we have the following system to solve:
          beginalign
          a_11 + 2a_12 + 3a_13 + 4a_14 & = 0 \
          a_21 + 2a_22 + 3a_23 + 4a_24 & = 0 \
          a_31 + 2a_32 + 3a_33 + 4a_34 & = 0 \
          a_41 + 2a_42 + 3a_43 + 4a_44 & = 0 \
          a_11 + a_12 + 2a_13 + 2a_14 & = 0 \
          a_21 + a_22 + 2a_23 + 2a_24 & = 0 \
          a_31 + a_32 + 2a_33 + 2a_34 & = 0 \
          a_41 + a_42 + 2a_43 + 2a_44 & = 0
          endalign
          This is exhaustive as it is a system of 8 equations in 16 unknowns. Therefore, we will have infinitely many solutions. \
          For example, you could construct a projector matrix $P_X$ that spans the columns of $X$, i.e.
          beginequation
          P_X = X(X^TX)^-1X^T
          =
          beginbmatrix
          0.5455 & -0.0909 & 0.4545 & -0.1818\
          -0.0909 & 0.1818 & 0.0909 & 0.3636\
          0.4545 & 0.0909 & 0.5455 & 0.1818\
          -0.1818 & 0.3636 & 0.1818 & 0.7273
          endbmatrix
          endequation
          And then you can say that my matrix $A$ spans the null space of $P_X$, i.e.
          beginequation
          A = I - P_X
          =
          beginbmatrix
          0.4545 & 0.0909 & -0.4545 & 0.1818\
          0.0909 & 0.8182 & -0.0909 & -0.3636\
          -0.4545 & -0.0909 & 0.4545& -0.1818\
          0.1818 & -0.3636 & -0.1818 & 0.2727
          endbmatrix
          endequation
          Now check,



          beginequation
          AX =(I - P_X)X = X - P_XX = X - X(X^TX)^-1X^TX = X-X = 0
          endequation
          and voila there you have a matrix with nullspace being the columns of $X$.






          share|cite|improve this answer






















            up vote
            2
            down vote










            up vote
            2
            down vote









            You could for instance say that you have a matrix
            beginequation
            A
            =
            beginbmatrix
            a_11 & a_12 & a_13 & a_14 \
            a_21 & a_22 & a_23 & a_24 \
            a_31 & a_32 & a_33 & a_34 \
            a_41 & a_42 & a_43 & a_44 \
            endbmatrix
            endequation
            such that
            beginequation
            AX = 0
            endequation
            where
            beginequation
            X =
            beginbmatrix
            1 & 1 \
            2 & 1 \
            3 & 2\
            4 & 2
            endbmatrix
            endequation
            Hence we have to find $a_ij$'s such that
            beginequation
            beginbmatrix
            a_11 & a_12 & a_13 & a_14 \
            a_21 & a_22 & a_23 & a_24 \
            a_31 & a_32 & a_33 & a_34 \
            a_41 & a_42 & a_43 & a_44 \
            endbmatrix
            beginbmatrix
            1 & 1 \
            2 & 1 \
            3 & 2\
            4 & 2
            endbmatrix
            =
            0
            endequation
            i.e. we have the following system to solve:
            beginalign
            a_11 + 2a_12 + 3a_13 + 4a_14 & = 0 \
            a_21 + 2a_22 + 3a_23 + 4a_24 & = 0 \
            a_31 + 2a_32 + 3a_33 + 4a_34 & = 0 \
            a_41 + 2a_42 + 3a_43 + 4a_44 & = 0 \
            a_11 + a_12 + 2a_13 + 2a_14 & = 0 \
            a_21 + a_22 + 2a_23 + 2a_24 & = 0 \
            a_31 + a_32 + 2a_33 + 2a_34 & = 0 \
            a_41 + a_42 + 2a_43 + 2a_44 & = 0
            endalign
            This is exhaustive as it is a system of 8 equations in 16 unknowns. Therefore, we will have infinitely many solutions. \
            For example, you could construct a projector matrix $P_X$ that spans the columns of $X$, i.e.
            beginequation
            P_X = X(X^TX)^-1X^T
            =
            beginbmatrix
            0.5455 & -0.0909 & 0.4545 & -0.1818\
            -0.0909 & 0.1818 & 0.0909 & 0.3636\
            0.4545 & 0.0909 & 0.5455 & 0.1818\
            -0.1818 & 0.3636 & 0.1818 & 0.7273
            endbmatrix
            endequation
            And then you can say that my matrix $A$ spans the null space of $P_X$, i.e.
            beginequation
            A = I - P_X
            =
            beginbmatrix
            0.4545 & 0.0909 & -0.4545 & 0.1818\
            0.0909 & 0.8182 & -0.0909 & -0.3636\
            -0.4545 & -0.0909 & 0.4545& -0.1818\
            0.1818 & -0.3636 & -0.1818 & 0.2727
            endbmatrix
            endequation
            Now check,



            beginequation
            AX =(I - P_X)X = X - P_XX = X - X(X^TX)^-1X^TX = X-X = 0
            endequation
            and voila there you have a matrix with nullspace being the columns of $X$.






            share|cite|improve this answer












            You could for instance say that you have a matrix
            beginequation
            A
            =
            beginbmatrix
            a_11 & a_12 & a_13 & a_14 \
            a_21 & a_22 & a_23 & a_24 \
            a_31 & a_32 & a_33 & a_34 \
            a_41 & a_42 & a_43 & a_44 \
            endbmatrix
            endequation
            such that
            beginequation
            AX = 0
            endequation
            where
            beginequation
            X =
            beginbmatrix
            1 & 1 \
            2 & 1 \
            3 & 2\
            4 & 2
            endbmatrix
            endequation
            Hence we have to find $a_ij$'s such that
            beginequation
            beginbmatrix
            a_11 & a_12 & a_13 & a_14 \
            a_21 & a_22 & a_23 & a_24 \
            a_31 & a_32 & a_33 & a_34 \
            a_41 & a_42 & a_43 & a_44 \
            endbmatrix
            beginbmatrix
            1 & 1 \
            2 & 1 \
            3 & 2\
            4 & 2
            endbmatrix
            =
            0
            endequation
            i.e. we have the following system to solve:
            beginalign
            a_11 + 2a_12 + 3a_13 + 4a_14 & = 0 \
            a_21 + 2a_22 + 3a_23 + 4a_24 & = 0 \
            a_31 + 2a_32 + 3a_33 + 4a_34 & = 0 \
            a_41 + 2a_42 + 3a_43 + 4a_44 & = 0 \
            a_11 + a_12 + 2a_13 + 2a_14 & = 0 \
            a_21 + a_22 + 2a_23 + 2a_24 & = 0 \
            a_31 + a_32 + 2a_33 + 2a_34 & = 0 \
            a_41 + a_42 + 2a_43 + 2a_44 & = 0
            endalign
            This is exhaustive as it is a system of 8 equations in 16 unknowns. Therefore, we will have infinitely many solutions. \
            For example, you could construct a projector matrix $P_X$ that spans the columns of $X$, i.e.
            beginequation
            P_X = X(X^TX)^-1X^T
            =
            beginbmatrix
            0.5455 & -0.0909 & 0.4545 & -0.1818\
            -0.0909 & 0.1818 & 0.0909 & 0.3636\
            0.4545 & 0.0909 & 0.5455 & 0.1818\
            -0.1818 & 0.3636 & 0.1818 & 0.7273
            endbmatrix
            endequation
            And then you can say that my matrix $A$ spans the null space of $P_X$, i.e.
            beginequation
            A = I - P_X
            =
            beginbmatrix
            0.4545 & 0.0909 & -0.4545 & 0.1818\
            0.0909 & 0.8182 & -0.0909 & -0.3636\
            -0.4545 & -0.0909 & 0.4545& -0.1818\
            0.1818 & -0.3636 & -0.1818 & 0.2727
            endbmatrix
            endequation
            Now check,



            beginequation
            AX =(I - P_X)X = X - P_XX = X - X(X^TX)^-1X^TX = X-X = 0
            endequation
            and voila there you have a matrix with nullspace being the columns of $X$.







            share|cite|improve this answer












            share|cite|improve this answer



            share|cite|improve this answer










            answered Aug 11 at 1:26









            Ahmad Bazzi

            3,0601419




            3,0601419




















                up vote
                1
                down vote













                Let me show you how it would go. Call $v_1=(1,2,3,4)$ and $v_2=(1,1,2,2)$.



                Let $v=(x_1,x_2,x_3,x_4)$. We want $vcdot v_1=0$ and $vcdot v_2=0$. This gives us the system of equations $$beginalignx_1+2x_2+3x_3+4x_4&=0\x_1+x_2+2x_3+2x_4&=0endalign$$



                Subtracting the first equation from the second we get: $$beginalignx_1+2x_2+3x_3+4x_4&=0\phantomx_1-x_2-x_3-2x_4&=0endalign$$



                Now the system is in echelon form. In order to get two linearly independent solutions we can put first $x_3=1$, $x_4=0$ and solve for $x_1,x_2$, and then put $x_3=0$, $x_4=1$ and solve for $x_1,x_2$.



                Doing that gives us two vectors $$beginalignu_1&=(-1,-1,1,0)\u_2&=(0,-2,0,1)endalign$$



                Therefore, one matrix satisfying the conditions of the problem would be $$beginpmatrix-1,&-1,&1,&0\0,&-2,&0,&1\-1,&-1,&1,&0\0,&-2,&0,&1endpmatrix$$



                obtained by putting $u_1,u_2$ and again $u_1,u_2$ as rows.






                share|cite|improve this answer




















                • This was super helpful!
                  – Alan Dennison
                  Aug 11 at 20:10














                up vote
                1
                down vote













                Let me show you how it would go. Call $v_1=(1,2,3,4)$ and $v_2=(1,1,2,2)$.



                Let $v=(x_1,x_2,x_3,x_4)$. We want $vcdot v_1=0$ and $vcdot v_2=0$. This gives us the system of equations $$beginalignx_1+2x_2+3x_3+4x_4&=0\x_1+x_2+2x_3+2x_4&=0endalign$$



                Subtracting the first equation from the second we get: $$beginalignx_1+2x_2+3x_3+4x_4&=0\phantomx_1-x_2-x_3-2x_4&=0endalign$$



                Now the system is in echelon form. In order to get two linearly independent solutions we can put first $x_3=1$, $x_4=0$ and solve for $x_1,x_2$, and then put $x_3=0$, $x_4=1$ and solve for $x_1,x_2$.



                Doing that gives us two vectors $$beginalignu_1&=(-1,-1,1,0)\u_2&=(0,-2,0,1)endalign$$



                Therefore, one matrix satisfying the conditions of the problem would be $$beginpmatrix-1,&-1,&1,&0\0,&-2,&0,&1\-1,&-1,&1,&0\0,&-2,&0,&1endpmatrix$$



                obtained by putting $u_1,u_2$ and again $u_1,u_2$ as rows.






                share|cite|improve this answer




















                • This was super helpful!
                  – Alan Dennison
                  Aug 11 at 20:10












                up vote
                1
                down vote










                up vote
                1
                down vote









                Let me show you how it would go. Call $v_1=(1,2,3,4)$ and $v_2=(1,1,2,2)$.



                Let $v=(x_1,x_2,x_3,x_4)$. We want $vcdot v_1=0$ and $vcdot v_2=0$. This gives us the system of equations $$beginalignx_1+2x_2+3x_3+4x_4&=0\x_1+x_2+2x_3+2x_4&=0endalign$$



                Subtracting the first equation from the second we get: $$beginalignx_1+2x_2+3x_3+4x_4&=0\phantomx_1-x_2-x_3-2x_4&=0endalign$$



                Now the system is in echelon form. In order to get two linearly independent solutions we can put first $x_3=1$, $x_4=0$ and solve for $x_1,x_2$, and then put $x_3=0$, $x_4=1$ and solve for $x_1,x_2$.



                Doing that gives us two vectors $$beginalignu_1&=(-1,-1,1,0)\u_2&=(0,-2,0,1)endalign$$



                Therefore, one matrix satisfying the conditions of the problem would be $$beginpmatrix-1,&-1,&1,&0\0,&-2,&0,&1\-1,&-1,&1,&0\0,&-2,&0,&1endpmatrix$$



                obtained by putting $u_1,u_2$ and again $u_1,u_2$ as rows.






                share|cite|improve this answer












                Let me show you how it would go. Call $v_1=(1,2,3,4)$ and $v_2=(1,1,2,2)$.



                Let $v=(x_1,x_2,x_3,x_4)$. We want $vcdot v_1=0$ and $vcdot v_2=0$. This gives us the system of equations $$beginalignx_1+2x_2+3x_3+4x_4&=0\x_1+x_2+2x_3+2x_4&=0endalign$$



                Subtracting the first equation from the second we get: $$beginalignx_1+2x_2+3x_3+4x_4&=0\phantomx_1-x_2-x_3-2x_4&=0endalign$$



                Now the system is in echelon form. In order to get two linearly independent solutions we can put first $x_3=1$, $x_4=0$ and solve for $x_1,x_2$, and then put $x_3=0$, $x_4=1$ and solve for $x_1,x_2$.



                Doing that gives us two vectors $$beginalignu_1&=(-1,-1,1,0)\u_2&=(0,-2,0,1)endalign$$



                Therefore, one matrix satisfying the conditions of the problem would be $$beginpmatrix-1,&-1,&1,&0\0,&-2,&0,&1\-1,&-1,&1,&0\0,&-2,&0,&1endpmatrix$$



                obtained by putting $u_1,u_2$ and again $u_1,u_2$ as rows.







                share|cite|improve this answer












                share|cite|improve this answer



                share|cite|improve this answer










                answered Aug 11 at 0:42







                user583012


















                • This was super helpful!
                  – Alan Dennison
                  Aug 11 at 20:10
















                • This was super helpful!
                  – Alan Dennison
                  Aug 11 at 20:10















                This was super helpful!
                – Alan Dennison
                Aug 11 at 20:10




                This was super helpful!
                – Alan Dennison
                Aug 11 at 20:10










                up vote
                0
                down vote













                Here is a more detailed tip based on zzuussee's comment:



                Let



                $$A=beginbmatrixa_11&a_12&a_13&a_14\a_21&a_22&a_23&a_24\a_31&a_32&a_33&a_34\a_41&a_42&a_43&a_44endbmatrix.$$



                What could we say about each $a_ij$ if we require that



                $$Abeginbmatrix1\2\3\4endbmatrix=Abeginbmatrix1\1\2\2endbmatrix=beginbmatrix0\0\0\0endbmatrix?$$



                Can you choose values for each $a_ij$ that satisfy the above requirements?






                share|cite|improve this answer
























                  up vote
                  0
                  down vote













                  Here is a more detailed tip based on zzuussee's comment:



                  Let



                  $$A=beginbmatrixa_11&a_12&a_13&a_14\a_21&a_22&a_23&a_24\a_31&a_32&a_33&a_34\a_41&a_42&a_43&a_44endbmatrix.$$



                  What could we say about each $a_ij$ if we require that



                  $$Abeginbmatrix1\2\3\4endbmatrix=Abeginbmatrix1\1\2\2endbmatrix=beginbmatrix0\0\0\0endbmatrix?$$



                  Can you choose values for each $a_ij$ that satisfy the above requirements?






                  share|cite|improve this answer






















                    up vote
                    0
                    down vote










                    up vote
                    0
                    down vote









                    Here is a more detailed tip based on zzuussee's comment:



                    Let



                    $$A=beginbmatrixa_11&a_12&a_13&a_14\a_21&a_22&a_23&a_24\a_31&a_32&a_33&a_34\a_41&a_42&a_43&a_44endbmatrix.$$



                    What could we say about each $a_ij$ if we require that



                    $$Abeginbmatrix1\2\3\4endbmatrix=Abeginbmatrix1\1\2\2endbmatrix=beginbmatrix0\0\0\0endbmatrix?$$



                    Can you choose values for each $a_ij$ that satisfy the above requirements?






                    share|cite|improve this answer












                    Here is a more detailed tip based on zzuussee's comment:



                    Let



                    $$A=beginbmatrixa_11&a_12&a_13&a_14\a_21&a_22&a_23&a_24\a_31&a_32&a_33&a_34\a_41&a_42&a_43&a_44endbmatrix.$$



                    What could we say about each $a_ij$ if we require that



                    $$Abeginbmatrix1\2\3\4endbmatrix=Abeginbmatrix1\1\2\2endbmatrix=beginbmatrix0\0\0\0endbmatrix?$$



                    Can you choose values for each $a_ij$ that satisfy the above requirements?







                    share|cite|improve this answer












                    share|cite|improve this answer



                    share|cite|improve this answer










                    answered Aug 11 at 0:02









                    Cleric

                    3,06632463




                    3,06632463




















                        up vote
                        0
                        down vote













                        It's well-known that a matrix is determined by its effect on a basis. So let's complete our null basis to a basis for $mathbb R^4$. For instance, we could use $e_1=(1,0,0,0)$ and $e_2=(0,1,0,0)$.



                        Now, if we call our basis $beta$, then $[T]_beta^beta=beginpmatrix1&0&0&0\0&1&0&0\0&0&0&0\0&0&0&0endpmatrix$ would be a matrix with the right null space (actually the first two columns could be any two vectors that are linearly independent...)



                        Meanwhile, the change of basis matrix from $beta$ to the standard basis is: $P=beginpmatrix1&0&1&1\0&1&2&1\0&0&3&2\0&0&4&2endpmatrix$.



                        So, we could let $A=P[T]_beta^betaP^-1$.



                        I get $A=beginpmatrix1&0&-1&frac12\0&1&0&-frac12\0&0&0&0\0&0&0&0endpmatrix$.






                        share|cite|improve this answer


























                          up vote
                          0
                          down vote













                          It's well-known that a matrix is determined by its effect on a basis. So let's complete our null basis to a basis for $mathbb R^4$. For instance, we could use $e_1=(1,0,0,0)$ and $e_2=(0,1,0,0)$.



                          Now, if we call our basis $beta$, then $[T]_beta^beta=beginpmatrix1&0&0&0\0&1&0&0\0&0&0&0\0&0&0&0endpmatrix$ would be a matrix with the right null space (actually the first two columns could be any two vectors that are linearly independent...)



                          Meanwhile, the change of basis matrix from $beta$ to the standard basis is: $P=beginpmatrix1&0&1&1\0&1&2&1\0&0&3&2\0&0&4&2endpmatrix$.



                          So, we could let $A=P[T]_beta^betaP^-1$.



                          I get $A=beginpmatrix1&0&-1&frac12\0&1&0&-frac12\0&0&0&0\0&0&0&0endpmatrix$.






                          share|cite|improve this answer
























                            up vote
                            0
                            down vote










                            up vote
                            0
                            down vote









                            It's well-known that a matrix is determined by its effect on a basis. So let's complete our null basis to a basis for $mathbb R^4$. For instance, we could use $e_1=(1,0,0,0)$ and $e_2=(0,1,0,0)$.



                            Now, if we call our basis $beta$, then $[T]_beta^beta=beginpmatrix1&0&0&0\0&1&0&0\0&0&0&0\0&0&0&0endpmatrix$ would be a matrix with the right null space (actually the first two columns could be any two vectors that are linearly independent...)



                            Meanwhile, the change of basis matrix from $beta$ to the standard basis is: $P=beginpmatrix1&0&1&1\0&1&2&1\0&0&3&2\0&0&4&2endpmatrix$.



                            So, we could let $A=P[T]_beta^betaP^-1$.



                            I get $A=beginpmatrix1&0&-1&frac12\0&1&0&-frac12\0&0&0&0\0&0&0&0endpmatrix$.






                            share|cite|improve this answer














                            It's well-known that a matrix is determined by its effect on a basis. So let's complete our null basis to a basis for $mathbb R^4$. For instance, we could use $e_1=(1,0,0,0)$ and $e_2=(0,1,0,0)$.



                            Now, if we call our basis $beta$, then $[T]_beta^beta=beginpmatrix1&0&0&0\0&1&0&0\0&0&0&0\0&0&0&0endpmatrix$ would be a matrix with the right null space (actually the first two columns could be any two vectors that are linearly independent...)



                            Meanwhile, the change of basis matrix from $beta$ to the standard basis is: $P=beginpmatrix1&0&1&1\0&1&2&1\0&0&3&2\0&0&4&2endpmatrix$.



                            So, we could let $A=P[T]_beta^betaP^-1$.



                            I get $A=beginpmatrix1&0&-1&frac12\0&1&0&-frac12\0&0&0&0\0&0&0&0endpmatrix$.







                            share|cite|improve this answer














                            share|cite|improve this answer



                            share|cite|improve this answer








                            edited Aug 16 at 19:37

























                            answered Aug 11 at 1:03









                            Chris Custer

                            5,6912622




                            5,6912622






















                                 

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