Minimal polynomial algorithm
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In our textbooks we are given the following algorithm:
Let $V$ be a vector space having dimension $n$ over a field $K$ ( $mathbb R$ or $mathbb C$ ) and $A : V to V$ be a linear map. In a sequence $I,A, A^2,A^3,..,A^n$,we pick a non-zero matrix element in the first member of the sequence ( $I$ ) with which we are "eliminating" corresponding matrix elements in the other members of the sequence. Now we have $A_{11},A_{12},...,A_{1n}$ , where $$A_{1k} = A^k - beta_{1k}I , k=1,...,n$$ If $A_{11}neq 0$ , we do the same to the sequence $A_{11},A_{12},...,A_{1n}$, meaning that we again pick a non-zero matrix element in $A_{11}$ with which we are "eliminating" corresponding matrix elements in the other members of the sequence Now we have $A_{22},A_{23},...,A_{2n}$, where $$A_{2k} = A_{1k} - beta_{2k}A_{11} , k=2,...,n$$ We repeat the same process until we get $A_{jj}=0 , A_{j-1,j-1}neq0, jleq n$.As a result we get $$ I quad A quad A^2 quad A^3 quad ... quad A^n \ quad A_{11}enspace A_{12}enspace A_{13} quad... enspace A_{1n} \ qquad quad A_{22}enspace A_{23} quad... enspace A_{2n} \ hspace{90pt}. \ hspace{90pt}. \ hspace{90pt}. \ hspace{90pt}A_{jj}$$
To get our minimal polynomial we just need to "unroll" $A_{jj}$, precisely $0=A_{jj}=A_{j-1,j}-beta_{j-1,j}A_{j-1,j-1}=... $
Now it says that it's obvious that the sequence $ I,A_{11},A_{22},...,A_{j-1,j-1}$ is linearly independent, but I can't really see why though.Also, how do we know we can't get a monic polynomial $p(x)$ of less degree such that $p(A)=0$ ?
Thank you for your time.
linear-algebra vector-spaces minimal-polynomials
$endgroup$
add a comment |
$begingroup$
In our textbooks we are given the following algorithm:
Let $V$ be a vector space having dimension $n$ over a field $K$ ( $mathbb R$ or $mathbb C$ ) and $A : V to V$ be a linear map. In a sequence $I,A, A^2,A^3,..,A^n$,we pick a non-zero matrix element in the first member of the sequence ( $I$ ) with which we are "eliminating" corresponding matrix elements in the other members of the sequence. Now we have $A_{11},A_{12},...,A_{1n}$ , where $$A_{1k} = A^k - beta_{1k}I , k=1,...,n$$ If $A_{11}neq 0$ , we do the same to the sequence $A_{11},A_{12},...,A_{1n}$, meaning that we again pick a non-zero matrix element in $A_{11}$ with which we are "eliminating" corresponding matrix elements in the other members of the sequence Now we have $A_{22},A_{23},...,A_{2n}$, where $$A_{2k} = A_{1k} - beta_{2k}A_{11} , k=2,...,n$$ We repeat the same process until we get $A_{jj}=0 , A_{j-1,j-1}neq0, jleq n$.As a result we get $$ I quad A quad A^2 quad A^3 quad ... quad A^n \ quad A_{11}enspace A_{12}enspace A_{13} quad... enspace A_{1n} \ qquad quad A_{22}enspace A_{23} quad... enspace A_{2n} \ hspace{90pt}. \ hspace{90pt}. \ hspace{90pt}. \ hspace{90pt}A_{jj}$$
To get our minimal polynomial we just need to "unroll" $A_{jj}$, precisely $0=A_{jj}=A_{j-1,j}-beta_{j-1,j}A_{j-1,j-1}=... $
Now it says that it's obvious that the sequence $ I,A_{11},A_{22},...,A_{j-1,j-1}$ is linearly independent, but I can't really see why though.Also, how do we know we can't get a monic polynomial $p(x)$ of less degree such that $p(A)=0$ ?
Thank you for your time.
linear-algebra vector-spaces minimal-polynomials
$endgroup$
1
$begingroup$
Which textbook?
$endgroup$
– lhf
Dec 22 '18 at 20:02
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@lhf It's more like a script rather than a book ( I think ) and it's written in croatian language. But it is used as a teaching material in my college. If you're still interested, take a look at link ( 21st slide/page ).
$endgroup$
– James Groon
Dec 22 '18 at 20:18
$begingroup$
One is basically doing Gaussian elimination "by rows" where each matrix is interpreted as a "column" vector (you could concatenate all columns of a matrix to make it one long column; all we care about here is linear dependency among matrices). Each time chose in your current matrix a position with nonzero entry as pivot, and clear out that position in the remainder of the row. This is not a very convenient method for computing a minimal polynomial though, and you must cater with the possibility that at some point no pivot can be found.
$endgroup$
– Marc van Leeuwen
Dec 27 '18 at 10:13
add a comment |
$begingroup$
In our textbooks we are given the following algorithm:
Let $V$ be a vector space having dimension $n$ over a field $K$ ( $mathbb R$ or $mathbb C$ ) and $A : V to V$ be a linear map. In a sequence $I,A, A^2,A^3,..,A^n$,we pick a non-zero matrix element in the first member of the sequence ( $I$ ) with which we are "eliminating" corresponding matrix elements in the other members of the sequence. Now we have $A_{11},A_{12},...,A_{1n}$ , where $$A_{1k} = A^k - beta_{1k}I , k=1,...,n$$ If $A_{11}neq 0$ , we do the same to the sequence $A_{11},A_{12},...,A_{1n}$, meaning that we again pick a non-zero matrix element in $A_{11}$ with which we are "eliminating" corresponding matrix elements in the other members of the sequence Now we have $A_{22},A_{23},...,A_{2n}$, where $$A_{2k} = A_{1k} - beta_{2k}A_{11} , k=2,...,n$$ We repeat the same process until we get $A_{jj}=0 , A_{j-1,j-1}neq0, jleq n$.As a result we get $$ I quad A quad A^2 quad A^3 quad ... quad A^n \ quad A_{11}enspace A_{12}enspace A_{13} quad... enspace A_{1n} \ qquad quad A_{22}enspace A_{23} quad... enspace A_{2n} \ hspace{90pt}. \ hspace{90pt}. \ hspace{90pt}. \ hspace{90pt}A_{jj}$$
To get our minimal polynomial we just need to "unroll" $A_{jj}$, precisely $0=A_{jj}=A_{j-1,j}-beta_{j-1,j}A_{j-1,j-1}=... $
Now it says that it's obvious that the sequence $ I,A_{11},A_{22},...,A_{j-1,j-1}$ is linearly independent, but I can't really see why though.Also, how do we know we can't get a monic polynomial $p(x)$ of less degree such that $p(A)=0$ ?
Thank you for your time.
linear-algebra vector-spaces minimal-polynomials
$endgroup$
In our textbooks we are given the following algorithm:
Let $V$ be a vector space having dimension $n$ over a field $K$ ( $mathbb R$ or $mathbb C$ ) and $A : V to V$ be a linear map. In a sequence $I,A, A^2,A^3,..,A^n$,we pick a non-zero matrix element in the first member of the sequence ( $I$ ) with which we are "eliminating" corresponding matrix elements in the other members of the sequence. Now we have $A_{11},A_{12},...,A_{1n}$ , where $$A_{1k} = A^k - beta_{1k}I , k=1,...,n$$ If $A_{11}neq 0$ , we do the same to the sequence $A_{11},A_{12},...,A_{1n}$, meaning that we again pick a non-zero matrix element in $A_{11}$ with which we are "eliminating" corresponding matrix elements in the other members of the sequence Now we have $A_{22},A_{23},...,A_{2n}$, where $$A_{2k} = A_{1k} - beta_{2k}A_{11} , k=2,...,n$$ We repeat the same process until we get $A_{jj}=0 , A_{j-1,j-1}neq0, jleq n$.As a result we get $$ I quad A quad A^2 quad A^3 quad ... quad A^n \ quad A_{11}enspace A_{12}enspace A_{13} quad... enspace A_{1n} \ qquad quad A_{22}enspace A_{23} quad... enspace A_{2n} \ hspace{90pt}. \ hspace{90pt}. \ hspace{90pt}. \ hspace{90pt}A_{jj}$$
To get our minimal polynomial we just need to "unroll" $A_{jj}$, precisely $0=A_{jj}=A_{j-1,j}-beta_{j-1,j}A_{j-1,j-1}=... $
Now it says that it's obvious that the sequence $ I,A_{11},A_{22},...,A_{j-1,j-1}$ is linearly independent, but I can't really see why though.Also, how do we know we can't get a monic polynomial $p(x)$ of less degree such that $p(A)=0$ ?
Thank you for your time.
linear-algebra vector-spaces minimal-polynomials
linear-algebra vector-spaces minimal-polynomials
edited Dec 22 '18 at 20:33
James Groon
asked Dec 22 '18 at 19:49
James GroonJames Groon
687
687
1
$begingroup$
Which textbook?
$endgroup$
– lhf
Dec 22 '18 at 20:02
$begingroup$
@lhf It's more like a script rather than a book ( I think ) and it's written in croatian language. But it is used as a teaching material in my college. If you're still interested, take a look at link ( 21st slide/page ).
$endgroup$
– James Groon
Dec 22 '18 at 20:18
$begingroup$
One is basically doing Gaussian elimination "by rows" where each matrix is interpreted as a "column" vector (you could concatenate all columns of a matrix to make it one long column; all we care about here is linear dependency among matrices). Each time chose in your current matrix a position with nonzero entry as pivot, and clear out that position in the remainder of the row. This is not a very convenient method for computing a minimal polynomial though, and you must cater with the possibility that at some point no pivot can be found.
$endgroup$
– Marc van Leeuwen
Dec 27 '18 at 10:13
add a comment |
1
$begingroup$
Which textbook?
$endgroup$
– lhf
Dec 22 '18 at 20:02
$begingroup$
@lhf It's more like a script rather than a book ( I think ) and it's written in croatian language. But it is used as a teaching material in my college. If you're still interested, take a look at link ( 21st slide/page ).
$endgroup$
– James Groon
Dec 22 '18 at 20:18
$begingroup$
One is basically doing Gaussian elimination "by rows" where each matrix is interpreted as a "column" vector (you could concatenate all columns of a matrix to make it one long column; all we care about here is linear dependency among matrices). Each time chose in your current matrix a position with nonzero entry as pivot, and clear out that position in the remainder of the row. This is not a very convenient method for computing a minimal polynomial though, and you must cater with the possibility that at some point no pivot can be found.
$endgroup$
– Marc van Leeuwen
Dec 27 '18 at 10:13
1
1
$begingroup$
Which textbook?
$endgroup$
– lhf
Dec 22 '18 at 20:02
$begingroup$
Which textbook?
$endgroup$
– lhf
Dec 22 '18 at 20:02
$begingroup$
@lhf It's more like a script rather than a book ( I think ) and it's written in croatian language. But it is used as a teaching material in my college. If you're still interested, take a look at link ( 21st slide/page ).
$endgroup$
– James Groon
Dec 22 '18 at 20:18
$begingroup$
@lhf It's more like a script rather than a book ( I think ) and it's written in croatian language. But it is used as a teaching material in my college. If you're still interested, take a look at link ( 21st slide/page ).
$endgroup$
– James Groon
Dec 22 '18 at 20:18
$begingroup$
One is basically doing Gaussian elimination "by rows" where each matrix is interpreted as a "column" vector (you could concatenate all columns of a matrix to make it one long column; all we care about here is linear dependency among matrices). Each time chose in your current matrix a position with nonzero entry as pivot, and clear out that position in the remainder of the row. This is not a very convenient method for computing a minimal polynomial though, and you must cater with the possibility that at some point no pivot can be found.
$endgroup$
– Marc van Leeuwen
Dec 27 '18 at 10:13
$begingroup$
One is basically doing Gaussian elimination "by rows" where each matrix is interpreted as a "column" vector (you could concatenate all columns of a matrix to make it one long column; all we care about here is linear dependency among matrices). Each time chose in your current matrix a position with nonzero entry as pivot, and clear out that position in the remainder of the row. This is not a very convenient method for computing a minimal polynomial though, and you must cater with the possibility that at some point no pivot can be found.
$endgroup$
– Marc van Leeuwen
Dec 27 '18 at 10:13
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
For convenience, we define $A_{00}=I.$ The matrices $A_{00},A_{11},A_{22},ldots,A_{j-1,j-1}$ are linearly independent, because each matrix $A_{kk}$ has a non-zero element at a position $(a_k,b_k)$ where all matrices $A_{rs},,sgeq r>k$ have a zero element.
Now let us take a look at the sum
$$
S = c_0A_{00}+c_1A_{11}+ldots+c_{j-1}A_{j-1,j-1}
$$
In order to make this sum become the zero matrix, the entry of $S$ at position $(a_0,b_0)$ must be $0.$ Therefore, $c_0=0,$ because the other addends do not change anything at this position of the matrix. The entry of $S$ at position $(a_1,b_1)$ must also be $0$. Therefore, $c_1=0.$ By induction, we can show that $c_0 = ldots = c_{j-1} = 0,$ from which it follows that the matrices $A_{00},A_{11},A_{22},ldots,A_{j-1,j-1}$ are linearly independent.
For each $kin{0,ldots,j-1},$ we know that the matrices $A_{00},A_{11},A_{22},ldots,A_{kk}$ are linear combinations of the matrices $I,A,A^2,ldots,A^{k}.$ If the minimal polynomial $p$ had a degree less than $j,$ the equation $p(A)=0$ would induce a non-trivial linear combination of $A_{00},A_{11},A_{22},ldots,A_{kk}$ with $k<j$ which equals the zero matrix. This contradicts the fact that the matrices $A_{00},A_{11},A_{22},ldots,A_{kk}$ are linearly independent.
$endgroup$
$begingroup$
Thank you for your answer, but I still have a couple of questions though. 1.) What is the middle section for ? Shouldn't the fact that linearly dependent ordered set ${a_1,a_2,...,a_k}$ implies the existence of $1 leq p leq k$ such that $a_p=c_1 a_1+c_2 a_2+...+c_p-1 a_p-1$ be sufficient for linear independency ? ( if we ordered our set like this: ${A_{j-1,j-1},...,A_{00}}$ by all means). 2.) I'm not sure how does a non-trivial linear combination of $I,A,A^2,ldots,A^{k}$ which equals $0$ would induce a non-trivial combination of $A_{00},A_{11},A_{22},ldots,A_{kk}$ which also equals $0$ ?
$endgroup$
– James Groon
Dec 24 '18 at 12:03
$begingroup$
1) To really make the linear independence obvious, I preferred to use a standard argument: The elements are linear independent, if the only linear combination with the result of $0$ is the trivial linear combination with all coefficients being $0.$ 2) We have $A^k=A_{kk} + sum_{j=1}^{k}beta_{jk}A_{j-1,j-1}.$ Plug this into the equation $p(A)=0,$ rearrange in order to combine the coefficients of the $A_{jj}$ and observe that the coefficient of $A_{jj}$ with the greatest $j$ is $1.$
$endgroup$
– Reinhard Meier
Dec 27 '18 at 11:02
$begingroup$
I see , that explains it all. Really appreciated.
$endgroup$
– James Groon
Dec 27 '18 at 11:50
add a comment |
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$begingroup$
For convenience, we define $A_{00}=I.$ The matrices $A_{00},A_{11},A_{22},ldots,A_{j-1,j-1}$ are linearly independent, because each matrix $A_{kk}$ has a non-zero element at a position $(a_k,b_k)$ where all matrices $A_{rs},,sgeq r>k$ have a zero element.
Now let us take a look at the sum
$$
S = c_0A_{00}+c_1A_{11}+ldots+c_{j-1}A_{j-1,j-1}
$$
In order to make this sum become the zero matrix, the entry of $S$ at position $(a_0,b_0)$ must be $0.$ Therefore, $c_0=0,$ because the other addends do not change anything at this position of the matrix. The entry of $S$ at position $(a_1,b_1)$ must also be $0$. Therefore, $c_1=0.$ By induction, we can show that $c_0 = ldots = c_{j-1} = 0,$ from which it follows that the matrices $A_{00},A_{11},A_{22},ldots,A_{j-1,j-1}$ are linearly independent.
For each $kin{0,ldots,j-1},$ we know that the matrices $A_{00},A_{11},A_{22},ldots,A_{kk}$ are linear combinations of the matrices $I,A,A^2,ldots,A^{k}.$ If the minimal polynomial $p$ had a degree less than $j,$ the equation $p(A)=0$ would induce a non-trivial linear combination of $A_{00},A_{11},A_{22},ldots,A_{kk}$ with $k<j$ which equals the zero matrix. This contradicts the fact that the matrices $A_{00},A_{11},A_{22},ldots,A_{kk}$ are linearly independent.
$endgroup$
$begingroup$
Thank you for your answer, but I still have a couple of questions though. 1.) What is the middle section for ? Shouldn't the fact that linearly dependent ordered set ${a_1,a_2,...,a_k}$ implies the existence of $1 leq p leq k$ such that $a_p=c_1 a_1+c_2 a_2+...+c_p-1 a_p-1$ be sufficient for linear independency ? ( if we ordered our set like this: ${A_{j-1,j-1},...,A_{00}}$ by all means). 2.) I'm not sure how does a non-trivial linear combination of $I,A,A^2,ldots,A^{k}$ which equals $0$ would induce a non-trivial combination of $A_{00},A_{11},A_{22},ldots,A_{kk}$ which also equals $0$ ?
$endgroup$
– James Groon
Dec 24 '18 at 12:03
$begingroup$
1) To really make the linear independence obvious, I preferred to use a standard argument: The elements are linear independent, if the only linear combination with the result of $0$ is the trivial linear combination with all coefficients being $0.$ 2) We have $A^k=A_{kk} + sum_{j=1}^{k}beta_{jk}A_{j-1,j-1}.$ Plug this into the equation $p(A)=0,$ rearrange in order to combine the coefficients of the $A_{jj}$ and observe that the coefficient of $A_{jj}$ with the greatest $j$ is $1.$
$endgroup$
– Reinhard Meier
Dec 27 '18 at 11:02
$begingroup$
I see , that explains it all. Really appreciated.
$endgroup$
– James Groon
Dec 27 '18 at 11:50
add a comment |
$begingroup$
For convenience, we define $A_{00}=I.$ The matrices $A_{00},A_{11},A_{22},ldots,A_{j-1,j-1}$ are linearly independent, because each matrix $A_{kk}$ has a non-zero element at a position $(a_k,b_k)$ where all matrices $A_{rs},,sgeq r>k$ have a zero element.
Now let us take a look at the sum
$$
S = c_0A_{00}+c_1A_{11}+ldots+c_{j-1}A_{j-1,j-1}
$$
In order to make this sum become the zero matrix, the entry of $S$ at position $(a_0,b_0)$ must be $0.$ Therefore, $c_0=0,$ because the other addends do not change anything at this position of the matrix. The entry of $S$ at position $(a_1,b_1)$ must also be $0$. Therefore, $c_1=0.$ By induction, we can show that $c_0 = ldots = c_{j-1} = 0,$ from which it follows that the matrices $A_{00},A_{11},A_{22},ldots,A_{j-1,j-1}$ are linearly independent.
For each $kin{0,ldots,j-1},$ we know that the matrices $A_{00},A_{11},A_{22},ldots,A_{kk}$ are linear combinations of the matrices $I,A,A^2,ldots,A^{k}.$ If the minimal polynomial $p$ had a degree less than $j,$ the equation $p(A)=0$ would induce a non-trivial linear combination of $A_{00},A_{11},A_{22},ldots,A_{kk}$ with $k<j$ which equals the zero matrix. This contradicts the fact that the matrices $A_{00},A_{11},A_{22},ldots,A_{kk}$ are linearly independent.
$endgroup$
$begingroup$
Thank you for your answer, but I still have a couple of questions though. 1.) What is the middle section for ? Shouldn't the fact that linearly dependent ordered set ${a_1,a_2,...,a_k}$ implies the existence of $1 leq p leq k$ such that $a_p=c_1 a_1+c_2 a_2+...+c_p-1 a_p-1$ be sufficient for linear independency ? ( if we ordered our set like this: ${A_{j-1,j-1},...,A_{00}}$ by all means). 2.) I'm not sure how does a non-trivial linear combination of $I,A,A^2,ldots,A^{k}$ which equals $0$ would induce a non-trivial combination of $A_{00},A_{11},A_{22},ldots,A_{kk}$ which also equals $0$ ?
$endgroup$
– James Groon
Dec 24 '18 at 12:03
$begingroup$
1) To really make the linear independence obvious, I preferred to use a standard argument: The elements are linear independent, if the only linear combination with the result of $0$ is the trivial linear combination with all coefficients being $0.$ 2) We have $A^k=A_{kk} + sum_{j=1}^{k}beta_{jk}A_{j-1,j-1}.$ Plug this into the equation $p(A)=0,$ rearrange in order to combine the coefficients of the $A_{jj}$ and observe that the coefficient of $A_{jj}$ with the greatest $j$ is $1.$
$endgroup$
– Reinhard Meier
Dec 27 '18 at 11:02
$begingroup$
I see , that explains it all. Really appreciated.
$endgroup$
– James Groon
Dec 27 '18 at 11:50
add a comment |
$begingroup$
For convenience, we define $A_{00}=I.$ The matrices $A_{00},A_{11},A_{22},ldots,A_{j-1,j-1}$ are linearly independent, because each matrix $A_{kk}$ has a non-zero element at a position $(a_k,b_k)$ where all matrices $A_{rs},,sgeq r>k$ have a zero element.
Now let us take a look at the sum
$$
S = c_0A_{00}+c_1A_{11}+ldots+c_{j-1}A_{j-1,j-1}
$$
In order to make this sum become the zero matrix, the entry of $S$ at position $(a_0,b_0)$ must be $0.$ Therefore, $c_0=0,$ because the other addends do not change anything at this position of the matrix. The entry of $S$ at position $(a_1,b_1)$ must also be $0$. Therefore, $c_1=0.$ By induction, we can show that $c_0 = ldots = c_{j-1} = 0,$ from which it follows that the matrices $A_{00},A_{11},A_{22},ldots,A_{j-1,j-1}$ are linearly independent.
For each $kin{0,ldots,j-1},$ we know that the matrices $A_{00},A_{11},A_{22},ldots,A_{kk}$ are linear combinations of the matrices $I,A,A^2,ldots,A^{k}.$ If the minimal polynomial $p$ had a degree less than $j,$ the equation $p(A)=0$ would induce a non-trivial linear combination of $A_{00},A_{11},A_{22},ldots,A_{kk}$ with $k<j$ which equals the zero matrix. This contradicts the fact that the matrices $A_{00},A_{11},A_{22},ldots,A_{kk}$ are linearly independent.
$endgroup$
For convenience, we define $A_{00}=I.$ The matrices $A_{00},A_{11},A_{22},ldots,A_{j-1,j-1}$ are linearly independent, because each matrix $A_{kk}$ has a non-zero element at a position $(a_k,b_k)$ where all matrices $A_{rs},,sgeq r>k$ have a zero element.
Now let us take a look at the sum
$$
S = c_0A_{00}+c_1A_{11}+ldots+c_{j-1}A_{j-1,j-1}
$$
In order to make this sum become the zero matrix, the entry of $S$ at position $(a_0,b_0)$ must be $0.$ Therefore, $c_0=0,$ because the other addends do not change anything at this position of the matrix. The entry of $S$ at position $(a_1,b_1)$ must also be $0$. Therefore, $c_1=0.$ By induction, we can show that $c_0 = ldots = c_{j-1} = 0,$ from which it follows that the matrices $A_{00},A_{11},A_{22},ldots,A_{j-1,j-1}$ are linearly independent.
For each $kin{0,ldots,j-1},$ we know that the matrices $A_{00},A_{11},A_{22},ldots,A_{kk}$ are linear combinations of the matrices $I,A,A^2,ldots,A^{k}.$ If the minimal polynomial $p$ had a degree less than $j,$ the equation $p(A)=0$ would induce a non-trivial linear combination of $A_{00},A_{11},A_{22},ldots,A_{kk}$ with $k<j$ which equals the zero matrix. This contradicts the fact that the matrices $A_{00},A_{11},A_{22},ldots,A_{kk}$ are linearly independent.
answered Dec 24 '18 at 1:19
Reinhard MeierReinhard Meier
3,022310
3,022310
$begingroup$
Thank you for your answer, but I still have a couple of questions though. 1.) What is the middle section for ? Shouldn't the fact that linearly dependent ordered set ${a_1,a_2,...,a_k}$ implies the existence of $1 leq p leq k$ such that $a_p=c_1 a_1+c_2 a_2+...+c_p-1 a_p-1$ be sufficient for linear independency ? ( if we ordered our set like this: ${A_{j-1,j-1},...,A_{00}}$ by all means). 2.) I'm not sure how does a non-trivial linear combination of $I,A,A^2,ldots,A^{k}$ which equals $0$ would induce a non-trivial combination of $A_{00},A_{11},A_{22},ldots,A_{kk}$ which also equals $0$ ?
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– James Groon
Dec 24 '18 at 12:03
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1) To really make the linear independence obvious, I preferred to use a standard argument: The elements are linear independent, if the only linear combination with the result of $0$ is the trivial linear combination with all coefficients being $0.$ 2) We have $A^k=A_{kk} + sum_{j=1}^{k}beta_{jk}A_{j-1,j-1}.$ Plug this into the equation $p(A)=0,$ rearrange in order to combine the coefficients of the $A_{jj}$ and observe that the coefficient of $A_{jj}$ with the greatest $j$ is $1.$
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– Reinhard Meier
Dec 27 '18 at 11:02
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I see , that explains it all. Really appreciated.
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– James Groon
Dec 27 '18 at 11:50
add a comment |
$begingroup$
Thank you for your answer, but I still have a couple of questions though. 1.) What is the middle section for ? Shouldn't the fact that linearly dependent ordered set ${a_1,a_2,...,a_k}$ implies the existence of $1 leq p leq k$ such that $a_p=c_1 a_1+c_2 a_2+...+c_p-1 a_p-1$ be sufficient for linear independency ? ( if we ordered our set like this: ${A_{j-1,j-1},...,A_{00}}$ by all means). 2.) I'm not sure how does a non-trivial linear combination of $I,A,A^2,ldots,A^{k}$ which equals $0$ would induce a non-trivial combination of $A_{00},A_{11},A_{22},ldots,A_{kk}$ which also equals $0$ ?
$endgroup$
– James Groon
Dec 24 '18 at 12:03
$begingroup$
1) To really make the linear independence obvious, I preferred to use a standard argument: The elements are linear independent, if the only linear combination with the result of $0$ is the trivial linear combination with all coefficients being $0.$ 2) We have $A^k=A_{kk} + sum_{j=1}^{k}beta_{jk}A_{j-1,j-1}.$ Plug this into the equation $p(A)=0,$ rearrange in order to combine the coefficients of the $A_{jj}$ and observe that the coefficient of $A_{jj}$ with the greatest $j$ is $1.$
$endgroup$
– Reinhard Meier
Dec 27 '18 at 11:02
$begingroup$
I see , that explains it all. Really appreciated.
$endgroup$
– James Groon
Dec 27 '18 at 11:50
$begingroup$
Thank you for your answer, but I still have a couple of questions though. 1.) What is the middle section for ? Shouldn't the fact that linearly dependent ordered set ${a_1,a_2,...,a_k}$ implies the existence of $1 leq p leq k$ such that $a_p=c_1 a_1+c_2 a_2+...+c_p-1 a_p-1$ be sufficient for linear independency ? ( if we ordered our set like this: ${A_{j-1,j-1},...,A_{00}}$ by all means). 2.) I'm not sure how does a non-trivial linear combination of $I,A,A^2,ldots,A^{k}$ which equals $0$ would induce a non-trivial combination of $A_{00},A_{11},A_{22},ldots,A_{kk}$ which also equals $0$ ?
$endgroup$
– James Groon
Dec 24 '18 at 12:03
$begingroup$
Thank you for your answer, but I still have a couple of questions though. 1.) What is the middle section for ? Shouldn't the fact that linearly dependent ordered set ${a_1,a_2,...,a_k}$ implies the existence of $1 leq p leq k$ such that $a_p=c_1 a_1+c_2 a_2+...+c_p-1 a_p-1$ be sufficient for linear independency ? ( if we ordered our set like this: ${A_{j-1,j-1},...,A_{00}}$ by all means). 2.) I'm not sure how does a non-trivial linear combination of $I,A,A^2,ldots,A^{k}$ which equals $0$ would induce a non-trivial combination of $A_{00},A_{11},A_{22},ldots,A_{kk}$ which also equals $0$ ?
$endgroup$
– James Groon
Dec 24 '18 at 12:03
$begingroup$
1) To really make the linear independence obvious, I preferred to use a standard argument: The elements are linear independent, if the only linear combination with the result of $0$ is the trivial linear combination with all coefficients being $0.$ 2) We have $A^k=A_{kk} + sum_{j=1}^{k}beta_{jk}A_{j-1,j-1}.$ Plug this into the equation $p(A)=0,$ rearrange in order to combine the coefficients of the $A_{jj}$ and observe that the coefficient of $A_{jj}$ with the greatest $j$ is $1.$
$endgroup$
– Reinhard Meier
Dec 27 '18 at 11:02
$begingroup$
1) To really make the linear independence obvious, I preferred to use a standard argument: The elements are linear independent, if the only linear combination with the result of $0$ is the trivial linear combination with all coefficients being $0.$ 2) We have $A^k=A_{kk} + sum_{j=1}^{k}beta_{jk}A_{j-1,j-1}.$ Plug this into the equation $p(A)=0,$ rearrange in order to combine the coefficients of the $A_{jj}$ and observe that the coefficient of $A_{jj}$ with the greatest $j$ is $1.$
$endgroup$
– Reinhard Meier
Dec 27 '18 at 11:02
$begingroup$
I see , that explains it all. Really appreciated.
$endgroup$
– James Groon
Dec 27 '18 at 11:50
$begingroup$
I see , that explains it all. Really appreciated.
$endgroup$
– James Groon
Dec 27 '18 at 11:50
add a comment |
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Which textbook?
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– lhf
Dec 22 '18 at 20:02
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@lhf It's more like a script rather than a book ( I think ) and it's written in croatian language. But it is used as a teaching material in my college. If you're still interested, take a look at link ( 21st slide/page ).
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– James Groon
Dec 22 '18 at 20:18
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One is basically doing Gaussian elimination "by rows" where each matrix is interpreted as a "column" vector (you could concatenate all columns of a matrix to make it one long column; all we care about here is linear dependency among matrices). Each time chose in your current matrix a position with nonzero entry as pivot, and clear out that position in the remainder of the row. This is not a very convenient method for computing a minimal polynomial though, and you must cater with the possibility that at some point no pivot can be found.
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– Marc van Leeuwen
Dec 27 '18 at 10:13