What does it mean for the column spaces of two matrices to span the same subspace?











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What does it mean for the column spaces of two matrices to span the same subspace?



It is equivalent to saying that the two matrices have the same image and range, but I don't really understand what the two matrices would have to have in common in order for their column space to span the same subspace.










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    What does it mean for the column spaces of two matrices to span the same subspace?



    It is equivalent to saying that the two matrices have the same image and range, but I don't really understand what the two matrices would have to have in common in order for their column space to span the same subspace.










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      up vote
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      down vote

      favorite









      up vote
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      down vote

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      What does it mean for the column spaces of two matrices to span the same subspace?



      It is equivalent to saying that the two matrices have the same image and range, but I don't really understand what the two matrices would have to have in common in order for their column space to span the same subspace.










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      What does it mean for the column spaces of two matrices to span the same subspace?



      It is equivalent to saying that the two matrices have the same image and range, but I don't really understand what the two matrices would have to have in common in order for their column space to span the same subspace.







      linear-algebra vector-spaces






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      asked Nov 22 at 4:10









      Iamanon

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          Assume we are working with real vector spaces, so matrices have real numbers as their entries, and so vector spaces (of dim > 0) have infinitely many vectors.
          And for every vector space there are infinitely many basis possible.



          Now let us take na $mtimes n$ matrix $A$ whose column space $W$ has dimension $r$. Now consider any other basis of $W$. Take $mtimes r$ matrix $B$ with this new basis vectors as its columns. Now $B$ has the same column space as $A$.



          As there are infinitely many possible bases for $W$ infinitely many choices of $B$ having same columns space as $A$.



          Example: suppose the first two column vectors $u$ and $v$ of $A$ are linearly independent. Then the new matrix $B$ with same column space as $A$ can be obtained by replacing those two vectors by $u+ v$ and $u-v$, and retaining other columns. (Or you can take $u+av, u-av$ for any nonzero number $a$).






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            I think your terms are a little confused: the column space of a matrix is, by definition, the subspace spanned by the columns of that matrix.



            For example, if you take the matrix of real numbers $A = begin{pmatrix} 1 & 1 & 5 \ 0 & 1 & 0 end{pmatrix}$, then the columns are
            $begin{pmatrix} 1 \ 0 end{pmatrix}$,
            $begin{pmatrix} 1 \ 1 end{pmatrix}$,
            and $begin{pmatrix} 5 \ 0 end{pmatrix}$.
            Thinking of these columns as vectors in the two-dimensional space $Bbb{R}^2$, the subspace they span is the set of vectors
            $t_0 begin{pmatrix} 1 \ 0 end{pmatrix} +
            t_1 begin{pmatrix} 1 \ 1 end{pmatrix} +
            t_2 begin{pmatrix} 5 \ 0 end{pmatrix}$

            for all real values $t_0$, $t_1$, $t_2$.
            In fact (exercise) this subspace is the entire space $Bbb{R}^2$.



            If we also take, say, $B = begin{pmatrix} 1 & 1 & 0 \ 0 & 1 & 0 end{pmatrix}$, we find that (exercise) the column space of $B$ is the same as the column space of $A$.



            But if we take $C = begin{pmatrix} 1 & 0 & 0 \ 0 & 0 & 0 end{pmatrix}$, the column space of $C$ is different from the other two: it's the space of columns $begin{pmatrix} t \ 0 end{pmatrix}$ for all real values $t$.






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              2 Answers
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              Assume we are working with real vector spaces, so matrices have real numbers as their entries, and so vector spaces (of dim > 0) have infinitely many vectors.
              And for every vector space there are infinitely many basis possible.



              Now let us take na $mtimes n$ matrix $A$ whose column space $W$ has dimension $r$. Now consider any other basis of $W$. Take $mtimes r$ matrix $B$ with this new basis vectors as its columns. Now $B$ has the same column space as $A$.



              As there are infinitely many possible bases for $W$ infinitely many choices of $B$ having same columns space as $A$.



              Example: suppose the first two column vectors $u$ and $v$ of $A$ are linearly independent. Then the new matrix $B$ with same column space as $A$ can be obtained by replacing those two vectors by $u+ v$ and $u-v$, and retaining other columns. (Or you can take $u+av, u-av$ for any nonzero number $a$).






              share|cite|improve this answer

























                up vote
                0
                down vote













                Assume we are working with real vector spaces, so matrices have real numbers as their entries, and so vector spaces (of dim > 0) have infinitely many vectors.
                And for every vector space there are infinitely many basis possible.



                Now let us take na $mtimes n$ matrix $A$ whose column space $W$ has dimension $r$. Now consider any other basis of $W$. Take $mtimes r$ matrix $B$ with this new basis vectors as its columns. Now $B$ has the same column space as $A$.



                As there are infinitely many possible bases for $W$ infinitely many choices of $B$ having same columns space as $A$.



                Example: suppose the first two column vectors $u$ and $v$ of $A$ are linearly independent. Then the new matrix $B$ with same column space as $A$ can be obtained by replacing those two vectors by $u+ v$ and $u-v$, and retaining other columns. (Or you can take $u+av, u-av$ for any nonzero number $a$).






                share|cite|improve this answer























                  up vote
                  0
                  down vote










                  up vote
                  0
                  down vote









                  Assume we are working with real vector spaces, so matrices have real numbers as their entries, and so vector spaces (of dim > 0) have infinitely many vectors.
                  And for every vector space there are infinitely many basis possible.



                  Now let us take na $mtimes n$ matrix $A$ whose column space $W$ has dimension $r$. Now consider any other basis of $W$. Take $mtimes r$ matrix $B$ with this new basis vectors as its columns. Now $B$ has the same column space as $A$.



                  As there are infinitely many possible bases for $W$ infinitely many choices of $B$ having same columns space as $A$.



                  Example: suppose the first two column vectors $u$ and $v$ of $A$ are linearly independent. Then the new matrix $B$ with same column space as $A$ can be obtained by replacing those two vectors by $u+ v$ and $u-v$, and retaining other columns. (Or you can take $u+av, u-av$ for any nonzero number $a$).






                  share|cite|improve this answer












                  Assume we are working with real vector spaces, so matrices have real numbers as their entries, and so vector spaces (of dim > 0) have infinitely many vectors.
                  And for every vector space there are infinitely many basis possible.



                  Now let us take na $mtimes n$ matrix $A$ whose column space $W$ has dimension $r$. Now consider any other basis of $W$. Take $mtimes r$ matrix $B$ with this new basis vectors as its columns. Now $B$ has the same column space as $A$.



                  As there are infinitely many possible bases for $W$ infinitely many choices of $B$ having same columns space as $A$.



                  Example: suppose the first two column vectors $u$ and $v$ of $A$ are linearly independent. Then the new matrix $B$ with same column space as $A$ can be obtained by replacing those two vectors by $u+ v$ and $u-v$, and retaining other columns. (Or you can take $u+av, u-av$ for any nonzero number $a$).







                  share|cite|improve this answer












                  share|cite|improve this answer



                  share|cite|improve this answer










                  answered Nov 22 at 6:04









                  P Vanchinathan

                  14.7k12036




                  14.7k12036






















                      up vote
                      0
                      down vote













                      I think your terms are a little confused: the column space of a matrix is, by definition, the subspace spanned by the columns of that matrix.



                      For example, if you take the matrix of real numbers $A = begin{pmatrix} 1 & 1 & 5 \ 0 & 1 & 0 end{pmatrix}$, then the columns are
                      $begin{pmatrix} 1 \ 0 end{pmatrix}$,
                      $begin{pmatrix} 1 \ 1 end{pmatrix}$,
                      and $begin{pmatrix} 5 \ 0 end{pmatrix}$.
                      Thinking of these columns as vectors in the two-dimensional space $Bbb{R}^2$, the subspace they span is the set of vectors
                      $t_0 begin{pmatrix} 1 \ 0 end{pmatrix} +
                      t_1 begin{pmatrix} 1 \ 1 end{pmatrix} +
                      t_2 begin{pmatrix} 5 \ 0 end{pmatrix}$

                      for all real values $t_0$, $t_1$, $t_2$.
                      In fact (exercise) this subspace is the entire space $Bbb{R}^2$.



                      If we also take, say, $B = begin{pmatrix} 1 & 1 & 0 \ 0 & 1 & 0 end{pmatrix}$, we find that (exercise) the column space of $B$ is the same as the column space of $A$.



                      But if we take $C = begin{pmatrix} 1 & 0 & 0 \ 0 & 0 & 0 end{pmatrix}$, the column space of $C$ is different from the other two: it's the space of columns $begin{pmatrix} t \ 0 end{pmatrix}$ for all real values $t$.






                      share|cite|improve this answer

























                        up vote
                        0
                        down vote













                        I think your terms are a little confused: the column space of a matrix is, by definition, the subspace spanned by the columns of that matrix.



                        For example, if you take the matrix of real numbers $A = begin{pmatrix} 1 & 1 & 5 \ 0 & 1 & 0 end{pmatrix}$, then the columns are
                        $begin{pmatrix} 1 \ 0 end{pmatrix}$,
                        $begin{pmatrix} 1 \ 1 end{pmatrix}$,
                        and $begin{pmatrix} 5 \ 0 end{pmatrix}$.
                        Thinking of these columns as vectors in the two-dimensional space $Bbb{R}^2$, the subspace they span is the set of vectors
                        $t_0 begin{pmatrix} 1 \ 0 end{pmatrix} +
                        t_1 begin{pmatrix} 1 \ 1 end{pmatrix} +
                        t_2 begin{pmatrix} 5 \ 0 end{pmatrix}$

                        for all real values $t_0$, $t_1$, $t_2$.
                        In fact (exercise) this subspace is the entire space $Bbb{R}^2$.



                        If we also take, say, $B = begin{pmatrix} 1 & 1 & 0 \ 0 & 1 & 0 end{pmatrix}$, we find that (exercise) the column space of $B$ is the same as the column space of $A$.



                        But if we take $C = begin{pmatrix} 1 & 0 & 0 \ 0 & 0 & 0 end{pmatrix}$, the column space of $C$ is different from the other two: it's the space of columns $begin{pmatrix} t \ 0 end{pmatrix}$ for all real values $t$.






                        share|cite|improve this answer























                          up vote
                          0
                          down vote










                          up vote
                          0
                          down vote









                          I think your terms are a little confused: the column space of a matrix is, by definition, the subspace spanned by the columns of that matrix.



                          For example, if you take the matrix of real numbers $A = begin{pmatrix} 1 & 1 & 5 \ 0 & 1 & 0 end{pmatrix}$, then the columns are
                          $begin{pmatrix} 1 \ 0 end{pmatrix}$,
                          $begin{pmatrix} 1 \ 1 end{pmatrix}$,
                          and $begin{pmatrix} 5 \ 0 end{pmatrix}$.
                          Thinking of these columns as vectors in the two-dimensional space $Bbb{R}^2$, the subspace they span is the set of vectors
                          $t_0 begin{pmatrix} 1 \ 0 end{pmatrix} +
                          t_1 begin{pmatrix} 1 \ 1 end{pmatrix} +
                          t_2 begin{pmatrix} 5 \ 0 end{pmatrix}$

                          for all real values $t_0$, $t_1$, $t_2$.
                          In fact (exercise) this subspace is the entire space $Bbb{R}^2$.



                          If we also take, say, $B = begin{pmatrix} 1 & 1 & 0 \ 0 & 1 & 0 end{pmatrix}$, we find that (exercise) the column space of $B$ is the same as the column space of $A$.



                          But if we take $C = begin{pmatrix} 1 & 0 & 0 \ 0 & 0 & 0 end{pmatrix}$, the column space of $C$ is different from the other two: it's the space of columns $begin{pmatrix} t \ 0 end{pmatrix}$ for all real values $t$.






                          share|cite|improve this answer












                          I think your terms are a little confused: the column space of a matrix is, by definition, the subspace spanned by the columns of that matrix.



                          For example, if you take the matrix of real numbers $A = begin{pmatrix} 1 & 1 & 5 \ 0 & 1 & 0 end{pmatrix}$, then the columns are
                          $begin{pmatrix} 1 \ 0 end{pmatrix}$,
                          $begin{pmatrix} 1 \ 1 end{pmatrix}$,
                          and $begin{pmatrix} 5 \ 0 end{pmatrix}$.
                          Thinking of these columns as vectors in the two-dimensional space $Bbb{R}^2$, the subspace they span is the set of vectors
                          $t_0 begin{pmatrix} 1 \ 0 end{pmatrix} +
                          t_1 begin{pmatrix} 1 \ 1 end{pmatrix} +
                          t_2 begin{pmatrix} 5 \ 0 end{pmatrix}$

                          for all real values $t_0$, $t_1$, $t_2$.
                          In fact (exercise) this subspace is the entire space $Bbb{R}^2$.



                          If we also take, say, $B = begin{pmatrix} 1 & 1 & 0 \ 0 & 1 & 0 end{pmatrix}$, we find that (exercise) the column space of $B$ is the same as the column space of $A$.



                          But if we take $C = begin{pmatrix} 1 & 0 & 0 \ 0 & 0 & 0 end{pmatrix}$, the column space of $C$ is different from the other two: it's the space of columns $begin{pmatrix} t \ 0 end{pmatrix}$ for all real values $t$.







                          share|cite|improve this answer












                          share|cite|improve this answer



                          share|cite|improve this answer










                          answered Nov 22 at 6:09









                          Hew Wolff

                          2,163716




                          2,163716






























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