The existence of an orthonormal matrix











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Let $U$ be an orthonormal matrix. The dimension of $U$ is equal to $n times n$. The first row of $U$ has the following form:
$$bigg( frac{1}{sqrt{n}}, ldots , frac{1}{sqrt{n}} bigg). tag{1}$$
How can I prove that $U$ with property (1) exists?










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    Let $U$ be an orthonormal matrix. The dimension of $U$ is equal to $n times n$. The first row of $U$ has the following form:
    $$bigg( frac{1}{sqrt{n}}, ldots , frac{1}{sqrt{n}} bigg). tag{1}$$
    How can I prove that $U$ with property (1) exists?










    share|cite|improve this question
























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      Let $U$ be an orthonormal matrix. The dimension of $U$ is equal to $n times n$. The first row of $U$ has the following form:
      $$bigg( frac{1}{sqrt{n}}, ldots , frac{1}{sqrt{n}} bigg). tag{1}$$
      How can I prove that $U$ with property (1) exists?










      share|cite|improve this question













      Let $U$ be an orthonormal matrix. The dimension of $U$ is equal to $n times n$. The first row of $U$ has the following form:
      $$bigg( frac{1}{sqrt{n}}, ldots , frac{1}{sqrt{n}} bigg). tag{1}$$
      How can I prove that $U$ with property (1) exists?







      linear-algebra






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      asked Nov 20 at 17:40









      Hendrra

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          Take your row vector (call it $v_1$) and extend this set to a basis of $mathbb{R}^n$. Then orthonormalize this basis, (using Gram-Schmidt), obtaining {$v_1, dots, v_n$}. Then, the matrix U such that its jth row is $v_j$ is an orthonormal matrix.






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            $u:=(frac{1}{sqrt{n}},ldots,frac{1}{sqrt{n}})$ is a unit vector in your vector space. You can extend it to a basis ${u,v_{2},ldots,v_{n}}$ of $V$ and then apply the Gram-Schmidt algorithm on this basis to obtain an orthonormal basis: ${e_{1},e_{2},ldots,e_{n}}$ of $V$, where $e_{1}=u$. The matrix with rows $e_{1},ldots e_{n}$ will then be a unitary matrix.






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              Consider the vectors $v_1=e_1+dots+e_n$, $v_i=e_i$ for $i=2,dots,n$ and apply Gram-Schmidt to them. Then you obtain an orthonormal basis whose first vector is $(e_1+dots+e_n)/sqrt n$, which is precisely the vector you want.






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                Can the downvoter explain please? This answer is correct and was also the first to be posted
                – Federico
                Nov 20 at 18:15











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              3 Answers
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              3 Answers
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              active

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              up vote
              1
              down vote



              accepted










              Take your row vector (call it $v_1$) and extend this set to a basis of $mathbb{R}^n$. Then orthonormalize this basis, (using Gram-Schmidt), obtaining {$v_1, dots, v_n$}. Then, the matrix U such that its jth row is $v_j$ is an orthonormal matrix.






              share|cite|improve this answer

























                up vote
                1
                down vote



                accepted










                Take your row vector (call it $v_1$) and extend this set to a basis of $mathbb{R}^n$. Then orthonormalize this basis, (using Gram-Schmidt), obtaining {$v_1, dots, v_n$}. Then, the matrix U such that its jth row is $v_j$ is an orthonormal matrix.






                share|cite|improve this answer























                  up vote
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                  accepted







                  up vote
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                  down vote



                  accepted






                  Take your row vector (call it $v_1$) and extend this set to a basis of $mathbb{R}^n$. Then orthonormalize this basis, (using Gram-Schmidt), obtaining {$v_1, dots, v_n$}. Then, the matrix U such that its jth row is $v_j$ is an orthonormal matrix.






                  share|cite|improve this answer












                  Take your row vector (call it $v_1$) and extend this set to a basis of $mathbb{R}^n$. Then orthonormalize this basis, (using Gram-Schmidt), obtaining {$v_1, dots, v_n$}. Then, the matrix U such that its jth row is $v_j$ is an orthonormal matrix.







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                  answered Nov 20 at 17:56









                  M. Santos

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                      $u:=(frac{1}{sqrt{n}},ldots,frac{1}{sqrt{n}})$ is a unit vector in your vector space. You can extend it to a basis ${u,v_{2},ldots,v_{n}}$ of $V$ and then apply the Gram-Schmidt algorithm on this basis to obtain an orthonormal basis: ${e_{1},e_{2},ldots,e_{n}}$ of $V$, where $e_{1}=u$. The matrix with rows $e_{1},ldots e_{n}$ will then be a unitary matrix.






                      share|cite|improve this answer

























                        up vote
                        2
                        down vote













                        $u:=(frac{1}{sqrt{n}},ldots,frac{1}{sqrt{n}})$ is a unit vector in your vector space. You can extend it to a basis ${u,v_{2},ldots,v_{n}}$ of $V$ and then apply the Gram-Schmidt algorithm on this basis to obtain an orthonormal basis: ${e_{1},e_{2},ldots,e_{n}}$ of $V$, where $e_{1}=u$. The matrix with rows $e_{1},ldots e_{n}$ will then be a unitary matrix.






                        share|cite|improve this answer























                          up vote
                          2
                          down vote










                          up vote
                          2
                          down vote









                          $u:=(frac{1}{sqrt{n}},ldots,frac{1}{sqrt{n}})$ is a unit vector in your vector space. You can extend it to a basis ${u,v_{2},ldots,v_{n}}$ of $V$ and then apply the Gram-Schmidt algorithm on this basis to obtain an orthonormal basis: ${e_{1},e_{2},ldots,e_{n}}$ of $V$, where $e_{1}=u$. The matrix with rows $e_{1},ldots e_{n}$ will then be a unitary matrix.






                          share|cite|improve this answer












                          $u:=(frac{1}{sqrt{n}},ldots,frac{1}{sqrt{n}})$ is a unit vector in your vector space. You can extend it to a basis ${u,v_{2},ldots,v_{n}}$ of $V$ and then apply the Gram-Schmidt algorithm on this basis to obtain an orthonormal basis: ${e_{1},e_{2},ldots,e_{n}}$ of $V$, where $e_{1}=u$. The matrix with rows $e_{1},ldots e_{n}$ will then be a unitary matrix.







                          share|cite|improve this answer












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                          answered Nov 20 at 17:52









                          ervx

                          10.3k31338




                          10.3k31338






















                              up vote
                              0
                              down vote













                              Consider the vectors $v_1=e_1+dots+e_n$, $v_i=e_i$ for $i=2,dots,n$ and apply Gram-Schmidt to them. Then you obtain an orthonormal basis whose first vector is $(e_1+dots+e_n)/sqrt n$, which is precisely the vector you want.






                              share|cite|improve this answer

















                              • 1




                                Can the downvoter explain please? This answer is correct and was also the first to be posted
                                – Federico
                                Nov 20 at 18:15















                              up vote
                              0
                              down vote













                              Consider the vectors $v_1=e_1+dots+e_n$, $v_i=e_i$ for $i=2,dots,n$ and apply Gram-Schmidt to them. Then you obtain an orthonormal basis whose first vector is $(e_1+dots+e_n)/sqrt n$, which is precisely the vector you want.






                              share|cite|improve this answer

















                              • 1




                                Can the downvoter explain please? This answer is correct and was also the first to be posted
                                – Federico
                                Nov 20 at 18:15













                              up vote
                              0
                              down vote










                              up vote
                              0
                              down vote









                              Consider the vectors $v_1=e_1+dots+e_n$, $v_i=e_i$ for $i=2,dots,n$ and apply Gram-Schmidt to them. Then you obtain an orthonormal basis whose first vector is $(e_1+dots+e_n)/sqrt n$, which is precisely the vector you want.






                              share|cite|improve this answer












                              Consider the vectors $v_1=e_1+dots+e_n$, $v_i=e_i$ for $i=2,dots,n$ and apply Gram-Schmidt to them. Then you obtain an orthonormal basis whose first vector is $(e_1+dots+e_n)/sqrt n$, which is precisely the vector you want.







                              share|cite|improve this answer












                              share|cite|improve this answer



                              share|cite|improve this answer










                              answered Nov 20 at 17:51









                              Federico

                              2,910511




                              2,910511








                              • 1




                                Can the downvoter explain please? This answer is correct and was also the first to be posted
                                – Federico
                                Nov 20 at 18:15














                              • 1




                                Can the downvoter explain please? This answer is correct and was also the first to be posted
                                – Federico
                                Nov 20 at 18:15








                              1




                              1




                              Can the downvoter explain please? This answer is correct and was also the first to be posted
                              – Federico
                              Nov 20 at 18:15




                              Can the downvoter explain please? This answer is correct and was also the first to be posted
                              – Federico
                              Nov 20 at 18:15


















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