how does addition of identity matrix to a square matrix changes determinant?
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Suppose there is $n times n$ matrix $A$. If we form matrix $B = A+I$ where $I$ is $n times n$ identity matrix, how does $|B|$ - determinant of $B$ - change compared to $|A|$? And what about the case where $B = A - I$?
linear-algebra matrices determinant
$endgroup$
add a comment |
$begingroup$
Suppose there is $n times n$ matrix $A$. If we form matrix $B = A+I$ where $I$ is $n times n$ identity matrix, how does $|B|$ - determinant of $B$ - change compared to $|A|$? And what about the case where $B = A - I$?
linear-algebra matrices determinant
$endgroup$
add a comment |
$begingroup$
Suppose there is $n times n$ matrix $A$. If we form matrix $B = A+I$ where $I$ is $n times n$ identity matrix, how does $|B|$ - determinant of $B$ - change compared to $|A|$? And what about the case where $B = A - I$?
linear-algebra matrices determinant
$endgroup$
Suppose there is $n times n$ matrix $A$. If we form matrix $B = A+I$ where $I$ is $n times n$ identity matrix, how does $|B|$ - determinant of $B$ - change compared to $|A|$? And what about the case where $B = A - I$?
linear-algebra matrices determinant
linear-algebra matrices determinant
edited Mar 12 '18 at 8:20
darij grinberg
10.5k33062
10.5k33062
asked Dec 12 '12 at 9:08
DDRDDR
3112
3112
add a comment |
add a comment |
6 Answers
6
active
oldest
votes
$begingroup$
In case you are interested, there is a result which expresses $det(A+B)$ in terms of $det(A)$ and $det (B)$, it is given by following $$det (A+B)=det(A)+det(B)+sum_{i=1}^{n-1}Gamma_n^idet(A/B^i)$$
Where $Gamma_n^idet(A/B^i)$ is defined as a sum of the combination
of determinants, in which the $i$ rows of $A$ are substituted
by the corresponding rows of $B$. You can find the proof in this IEEE article: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=262036&userType=inst
$endgroup$
1
$begingroup$
Note: This formula is just a consequence of applying the multilinearity of the determinant once to each row (consecutively, so you end up with a sum of $2^n $ addends).
$endgroup$
– darij grinberg
Mar 12 '18 at 8:20
add a comment |
$begingroup$
There is no simple answer. $P(lambda) = det(A-lambda I)$ is the characteristic polynomial of $A$. This is a polynomial of degree $n$: the coefficient of $lambda^n$ is $(-1)^n$ and the coefficient of $lambda^0$ is $det(A)$. The coefficients of other powers of $lambda$ are various functions of the entries of $A$. $det(A+I)$ and $det(A-I)$ are $P(-1)$ and $P(1)$; that's about all there is to say about them.
$endgroup$
add a comment |
$begingroup$
As others already have pointed out, there is no simple relation. Here is one answer more for the intuition.
Consider the (restricting) codition, that $A_{n times n}$ is diagonalizable, then $$det(A) = lambda_0 cdot lambda_1 cdot lambda_2 cdot cdots lambda _{n-1} $$
Now consider you add the identity matrix. The determinant changes to
$$det(B) = (lambda_0+1) cdot (lambda_1+1) cdot (lambda_2+1) cdot cdots (lambda _{n-1} +1)$$
I think it is obvious how irregular the result depends on the given eigenvalues of A. If some $lambda_k=0$ then $det(A)=0$ but that zero-factor changes to $(lambda_k+1)$ and det(B) need not be zero. Other way round - if some factor $lambda_k=-1$ then the addition by I makes that factor $lambda_k+1=0$ and the determinant $det(B)$ becomes zero. If some $0 gt lambda_k gt -1$ then the determinant may change its sign...
So there is no hope to make one single statement about the behave of B related to A - except that where @pritam linked to, or except you would accept a statement like $$det(A)=e_n(Lambda_n) to det(B)= sum_{j=0}^n e_j(Lambda) $$ where $ Lambda = {lambda_k}_{k=0..n-1} $ and $e_k(Lambda)$ denotes k'th elementary symmetric polynomial over $Lambda$... (And this is only for diagonalizable matrices)
$endgroup$
$begingroup$
You don't need diagonalizable in your arguments. You can always work with the Jordan form, or with the Schur decomposition, and reason exactly like you did.
$endgroup$
– Martin Argerami
Dec 12 '12 at 13:07
add a comment |
$begingroup$
The characteristic polynomial $P_A(lambda)$ of a matrix $A$ is defined as
$$
P_A(lambda)=det(A-Ilambda)
$$
Therefore, $det(A)=P_A(0)$, while $det(A+I)=P_A(-1)$ and $det(A-I)=P_A(1)$.
$endgroup$
$begingroup$
Merry Christmas.
$endgroup$
– user1551
Dec 12 '12 at 9:43
$begingroup$
@user1551: and Happy New Year!
$endgroup$
– robjohn♦
Dec 12 '12 at 10:02
add a comment |
$begingroup$
For I and A $Ntimes N$ matrices we have:
$$det(I-A)=1-sum_{j}^{N}A_{jj}+sum_{ngeq 2}^{N}(-1)^{n}sum_{1leq j_{1}<...<j_{n}leq N}det((A_{j_{a},j_{b}})^{n}).$$
$endgroup$
add a comment |
$begingroup$
I would like to contribute the following special case which I encountered when I was looking for an answer and stumbled upon this thread.
If A=uv^intercal
, then det(I+A) = 1+u^Tv
$endgroup$
$begingroup$
P.S Someone please help with formatting this to correct transposes. Didn't manage.
$endgroup$
– mexmex
Dec 4 '18 at 9:54
add a comment |
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6 Answers
6
active
oldest
votes
6 Answers
6
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
In case you are interested, there is a result which expresses $det(A+B)$ in terms of $det(A)$ and $det (B)$, it is given by following $$det (A+B)=det(A)+det(B)+sum_{i=1}^{n-1}Gamma_n^idet(A/B^i)$$
Where $Gamma_n^idet(A/B^i)$ is defined as a sum of the combination
of determinants, in which the $i$ rows of $A$ are substituted
by the corresponding rows of $B$. You can find the proof in this IEEE article: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=262036&userType=inst
$endgroup$
1
$begingroup$
Note: This formula is just a consequence of applying the multilinearity of the determinant once to each row (consecutively, so you end up with a sum of $2^n $ addends).
$endgroup$
– darij grinberg
Mar 12 '18 at 8:20
add a comment |
$begingroup$
In case you are interested, there is a result which expresses $det(A+B)$ in terms of $det(A)$ and $det (B)$, it is given by following $$det (A+B)=det(A)+det(B)+sum_{i=1}^{n-1}Gamma_n^idet(A/B^i)$$
Where $Gamma_n^idet(A/B^i)$ is defined as a sum of the combination
of determinants, in which the $i$ rows of $A$ are substituted
by the corresponding rows of $B$. You can find the proof in this IEEE article: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=262036&userType=inst
$endgroup$
1
$begingroup$
Note: This formula is just a consequence of applying the multilinearity of the determinant once to each row (consecutively, so you end up with a sum of $2^n $ addends).
$endgroup$
– darij grinberg
Mar 12 '18 at 8:20
add a comment |
$begingroup$
In case you are interested, there is a result which expresses $det(A+B)$ in terms of $det(A)$ and $det (B)$, it is given by following $$det (A+B)=det(A)+det(B)+sum_{i=1}^{n-1}Gamma_n^idet(A/B^i)$$
Where $Gamma_n^idet(A/B^i)$ is defined as a sum of the combination
of determinants, in which the $i$ rows of $A$ are substituted
by the corresponding rows of $B$. You can find the proof in this IEEE article: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=262036&userType=inst
$endgroup$
In case you are interested, there is a result which expresses $det(A+B)$ in terms of $det(A)$ and $det (B)$, it is given by following $$det (A+B)=det(A)+det(B)+sum_{i=1}^{n-1}Gamma_n^idet(A/B^i)$$
Where $Gamma_n^idet(A/B^i)$ is defined as a sum of the combination
of determinants, in which the $i$ rows of $A$ are substituted
by the corresponding rows of $B$. You can find the proof in this IEEE article: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=262036&userType=inst
edited Dec 12 '12 at 11:11
answered Dec 12 '12 at 9:26
pritampritam
8,0671441
8,0671441
1
$begingroup$
Note: This formula is just a consequence of applying the multilinearity of the determinant once to each row (consecutively, so you end up with a sum of $2^n $ addends).
$endgroup$
– darij grinberg
Mar 12 '18 at 8:20
add a comment |
1
$begingroup$
Note: This formula is just a consequence of applying the multilinearity of the determinant once to each row (consecutively, so you end up with a sum of $2^n $ addends).
$endgroup$
– darij grinberg
Mar 12 '18 at 8:20
1
1
$begingroup$
Note: This formula is just a consequence of applying the multilinearity of the determinant once to each row (consecutively, so you end up with a sum of $2^n $ addends).
$endgroup$
– darij grinberg
Mar 12 '18 at 8:20
$begingroup$
Note: This formula is just a consequence of applying the multilinearity of the determinant once to each row (consecutively, so you end up with a sum of $2^n $ addends).
$endgroup$
– darij grinberg
Mar 12 '18 at 8:20
add a comment |
$begingroup$
There is no simple answer. $P(lambda) = det(A-lambda I)$ is the characteristic polynomial of $A$. This is a polynomial of degree $n$: the coefficient of $lambda^n$ is $(-1)^n$ and the coefficient of $lambda^0$ is $det(A)$. The coefficients of other powers of $lambda$ are various functions of the entries of $A$. $det(A+I)$ and $det(A-I)$ are $P(-1)$ and $P(1)$; that's about all there is to say about them.
$endgroup$
add a comment |
$begingroup$
There is no simple answer. $P(lambda) = det(A-lambda I)$ is the characteristic polynomial of $A$. This is a polynomial of degree $n$: the coefficient of $lambda^n$ is $(-1)^n$ and the coefficient of $lambda^0$ is $det(A)$. The coefficients of other powers of $lambda$ are various functions of the entries of $A$. $det(A+I)$ and $det(A-I)$ are $P(-1)$ and $P(1)$; that's about all there is to say about them.
$endgroup$
add a comment |
$begingroup$
There is no simple answer. $P(lambda) = det(A-lambda I)$ is the characteristic polynomial of $A$. This is a polynomial of degree $n$: the coefficient of $lambda^n$ is $(-1)^n$ and the coefficient of $lambda^0$ is $det(A)$. The coefficients of other powers of $lambda$ are various functions of the entries of $A$. $det(A+I)$ and $det(A-I)$ are $P(-1)$ and $P(1)$; that's about all there is to say about them.
$endgroup$
There is no simple answer. $P(lambda) = det(A-lambda I)$ is the characteristic polynomial of $A$. This is a polynomial of degree $n$: the coefficient of $lambda^n$ is $(-1)^n$ and the coefficient of $lambda^0$ is $det(A)$. The coefficients of other powers of $lambda$ are various functions of the entries of $A$. $det(A+I)$ and $det(A-I)$ are $P(-1)$ and $P(1)$; that's about all there is to say about them.
answered Dec 12 '12 at 9:14
Robert IsraelRobert Israel
320k23210462
320k23210462
add a comment |
add a comment |
$begingroup$
As others already have pointed out, there is no simple relation. Here is one answer more for the intuition.
Consider the (restricting) codition, that $A_{n times n}$ is diagonalizable, then $$det(A) = lambda_0 cdot lambda_1 cdot lambda_2 cdot cdots lambda _{n-1} $$
Now consider you add the identity matrix. The determinant changes to
$$det(B) = (lambda_0+1) cdot (lambda_1+1) cdot (lambda_2+1) cdot cdots (lambda _{n-1} +1)$$
I think it is obvious how irregular the result depends on the given eigenvalues of A. If some $lambda_k=0$ then $det(A)=0$ but that zero-factor changes to $(lambda_k+1)$ and det(B) need not be zero. Other way round - if some factor $lambda_k=-1$ then the addition by I makes that factor $lambda_k+1=0$ and the determinant $det(B)$ becomes zero. If some $0 gt lambda_k gt -1$ then the determinant may change its sign...
So there is no hope to make one single statement about the behave of B related to A - except that where @pritam linked to, or except you would accept a statement like $$det(A)=e_n(Lambda_n) to det(B)= sum_{j=0}^n e_j(Lambda) $$ where $ Lambda = {lambda_k}_{k=0..n-1} $ and $e_k(Lambda)$ denotes k'th elementary symmetric polynomial over $Lambda$... (And this is only for diagonalizable matrices)
$endgroup$
$begingroup$
You don't need diagonalizable in your arguments. You can always work with the Jordan form, or with the Schur decomposition, and reason exactly like you did.
$endgroup$
– Martin Argerami
Dec 12 '12 at 13:07
add a comment |
$begingroup$
As others already have pointed out, there is no simple relation. Here is one answer more for the intuition.
Consider the (restricting) codition, that $A_{n times n}$ is diagonalizable, then $$det(A) = lambda_0 cdot lambda_1 cdot lambda_2 cdot cdots lambda _{n-1} $$
Now consider you add the identity matrix. The determinant changes to
$$det(B) = (lambda_0+1) cdot (lambda_1+1) cdot (lambda_2+1) cdot cdots (lambda _{n-1} +1)$$
I think it is obvious how irregular the result depends on the given eigenvalues of A. If some $lambda_k=0$ then $det(A)=0$ but that zero-factor changes to $(lambda_k+1)$ and det(B) need not be zero. Other way round - if some factor $lambda_k=-1$ then the addition by I makes that factor $lambda_k+1=0$ and the determinant $det(B)$ becomes zero. If some $0 gt lambda_k gt -1$ then the determinant may change its sign...
So there is no hope to make one single statement about the behave of B related to A - except that where @pritam linked to, or except you would accept a statement like $$det(A)=e_n(Lambda_n) to det(B)= sum_{j=0}^n e_j(Lambda) $$ where $ Lambda = {lambda_k}_{k=0..n-1} $ and $e_k(Lambda)$ denotes k'th elementary symmetric polynomial over $Lambda$... (And this is only for diagonalizable matrices)
$endgroup$
$begingroup$
You don't need diagonalizable in your arguments. You can always work with the Jordan form, or with the Schur decomposition, and reason exactly like you did.
$endgroup$
– Martin Argerami
Dec 12 '12 at 13:07
add a comment |
$begingroup$
As others already have pointed out, there is no simple relation. Here is one answer more for the intuition.
Consider the (restricting) codition, that $A_{n times n}$ is diagonalizable, then $$det(A) = lambda_0 cdot lambda_1 cdot lambda_2 cdot cdots lambda _{n-1} $$
Now consider you add the identity matrix. The determinant changes to
$$det(B) = (lambda_0+1) cdot (lambda_1+1) cdot (lambda_2+1) cdot cdots (lambda _{n-1} +1)$$
I think it is obvious how irregular the result depends on the given eigenvalues of A. If some $lambda_k=0$ then $det(A)=0$ but that zero-factor changes to $(lambda_k+1)$ and det(B) need not be zero. Other way round - if some factor $lambda_k=-1$ then the addition by I makes that factor $lambda_k+1=0$ and the determinant $det(B)$ becomes zero. If some $0 gt lambda_k gt -1$ then the determinant may change its sign...
So there is no hope to make one single statement about the behave of B related to A - except that where @pritam linked to, or except you would accept a statement like $$det(A)=e_n(Lambda_n) to det(B)= sum_{j=0}^n e_j(Lambda) $$ where $ Lambda = {lambda_k}_{k=0..n-1} $ and $e_k(Lambda)$ denotes k'th elementary symmetric polynomial over $Lambda$... (And this is only for diagonalizable matrices)
$endgroup$
As others already have pointed out, there is no simple relation. Here is one answer more for the intuition.
Consider the (restricting) codition, that $A_{n times n}$ is diagonalizable, then $$det(A) = lambda_0 cdot lambda_1 cdot lambda_2 cdot cdots lambda _{n-1} $$
Now consider you add the identity matrix. The determinant changes to
$$det(B) = (lambda_0+1) cdot (lambda_1+1) cdot (lambda_2+1) cdot cdots (lambda _{n-1} +1)$$
I think it is obvious how irregular the result depends on the given eigenvalues of A. If some $lambda_k=0$ then $det(A)=0$ but that zero-factor changes to $(lambda_k+1)$ and det(B) need not be zero. Other way round - if some factor $lambda_k=-1$ then the addition by I makes that factor $lambda_k+1=0$ and the determinant $det(B)$ becomes zero. If some $0 gt lambda_k gt -1$ then the determinant may change its sign...
So there is no hope to make one single statement about the behave of B related to A - except that where @pritam linked to, or except you would accept a statement like $$det(A)=e_n(Lambda_n) to det(B)= sum_{j=0}^n e_j(Lambda) $$ where $ Lambda = {lambda_k}_{k=0..n-1} $ and $e_k(Lambda)$ denotes k'th elementary symmetric polynomial over $Lambda$... (And this is only for diagonalizable matrices)
edited Dec 12 '12 at 12:46
answered Dec 12 '12 at 12:31
Gottfried HelmsGottfried Helms
23.3k24498
23.3k24498
$begingroup$
You don't need diagonalizable in your arguments. You can always work with the Jordan form, or with the Schur decomposition, and reason exactly like you did.
$endgroup$
– Martin Argerami
Dec 12 '12 at 13:07
add a comment |
$begingroup$
You don't need diagonalizable in your arguments. You can always work with the Jordan form, or with the Schur decomposition, and reason exactly like you did.
$endgroup$
– Martin Argerami
Dec 12 '12 at 13:07
$begingroup$
You don't need diagonalizable in your arguments. You can always work with the Jordan form, or with the Schur decomposition, and reason exactly like you did.
$endgroup$
– Martin Argerami
Dec 12 '12 at 13:07
$begingroup$
You don't need diagonalizable in your arguments. You can always work with the Jordan form, or with the Schur decomposition, and reason exactly like you did.
$endgroup$
– Martin Argerami
Dec 12 '12 at 13:07
add a comment |
$begingroup$
The characteristic polynomial $P_A(lambda)$ of a matrix $A$ is defined as
$$
P_A(lambda)=det(A-Ilambda)
$$
Therefore, $det(A)=P_A(0)$, while $det(A+I)=P_A(-1)$ and $det(A-I)=P_A(1)$.
$endgroup$
$begingroup$
Merry Christmas.
$endgroup$
– user1551
Dec 12 '12 at 9:43
$begingroup$
@user1551: and Happy New Year!
$endgroup$
– robjohn♦
Dec 12 '12 at 10:02
add a comment |
$begingroup$
The characteristic polynomial $P_A(lambda)$ of a matrix $A$ is defined as
$$
P_A(lambda)=det(A-Ilambda)
$$
Therefore, $det(A)=P_A(0)$, while $det(A+I)=P_A(-1)$ and $det(A-I)=P_A(1)$.
$endgroup$
$begingroup$
Merry Christmas.
$endgroup$
– user1551
Dec 12 '12 at 9:43
$begingroup$
@user1551: and Happy New Year!
$endgroup$
– robjohn♦
Dec 12 '12 at 10:02
add a comment |
$begingroup$
The characteristic polynomial $P_A(lambda)$ of a matrix $A$ is defined as
$$
P_A(lambda)=det(A-Ilambda)
$$
Therefore, $det(A)=P_A(0)$, while $det(A+I)=P_A(-1)$ and $det(A-I)=P_A(1)$.
$endgroup$
The characteristic polynomial $P_A(lambda)$ of a matrix $A$ is defined as
$$
P_A(lambda)=det(A-Ilambda)
$$
Therefore, $det(A)=P_A(0)$, while $det(A+I)=P_A(-1)$ and $det(A-I)=P_A(1)$.
answered Dec 12 '12 at 9:15
robjohn♦robjohn
266k27305626
266k27305626
$begingroup$
Merry Christmas.
$endgroup$
– user1551
Dec 12 '12 at 9:43
$begingroup$
@user1551: and Happy New Year!
$endgroup$
– robjohn♦
Dec 12 '12 at 10:02
add a comment |
$begingroup$
Merry Christmas.
$endgroup$
– user1551
Dec 12 '12 at 9:43
$begingroup$
@user1551: and Happy New Year!
$endgroup$
– robjohn♦
Dec 12 '12 at 10:02
$begingroup$
Merry Christmas.
$endgroup$
– user1551
Dec 12 '12 at 9:43
$begingroup$
Merry Christmas.
$endgroup$
– user1551
Dec 12 '12 at 9:43
$begingroup$
@user1551: and Happy New Year!
$endgroup$
– robjohn♦
Dec 12 '12 at 10:02
$begingroup$
@user1551: and Happy New Year!
$endgroup$
– robjohn♦
Dec 12 '12 at 10:02
add a comment |
$begingroup$
For I and A $Ntimes N$ matrices we have:
$$det(I-A)=1-sum_{j}^{N}A_{jj}+sum_{ngeq 2}^{N}(-1)^{n}sum_{1leq j_{1}<...<j_{n}leq N}det((A_{j_{a},j_{b}})^{n}).$$
$endgroup$
add a comment |
$begingroup$
For I and A $Ntimes N$ matrices we have:
$$det(I-A)=1-sum_{j}^{N}A_{jj}+sum_{ngeq 2}^{N}(-1)^{n}sum_{1leq j_{1}<...<j_{n}leq N}det((A_{j_{a},j_{b}})^{n}).$$
$endgroup$
add a comment |
$begingroup$
For I and A $Ntimes N$ matrices we have:
$$det(I-A)=1-sum_{j}^{N}A_{jj}+sum_{ngeq 2}^{N}(-1)^{n}sum_{1leq j_{1}<...<j_{n}leq N}det((A_{j_{a},j_{b}})^{n}).$$
$endgroup$
For I and A $Ntimes N$ matrices we have:
$$det(I-A)=1-sum_{j}^{N}A_{jj}+sum_{ngeq 2}^{N}(-1)^{n}sum_{1leq j_{1}<...<j_{n}leq N}det((A_{j_{a},j_{b}})^{n}).$$
answered Mar 12 '18 at 0:47
Thomas KojarThomas Kojar
1155
1155
add a comment |
add a comment |
$begingroup$
I would like to contribute the following special case which I encountered when I was looking for an answer and stumbled upon this thread.
If A=uv^intercal
, then det(I+A) = 1+u^Tv
$endgroup$
$begingroup$
P.S Someone please help with formatting this to correct transposes. Didn't manage.
$endgroup$
– mexmex
Dec 4 '18 at 9:54
add a comment |
$begingroup$
I would like to contribute the following special case which I encountered when I was looking for an answer and stumbled upon this thread.
If A=uv^intercal
, then det(I+A) = 1+u^Tv
$endgroup$
$begingroup$
P.S Someone please help with formatting this to correct transposes. Didn't manage.
$endgroup$
– mexmex
Dec 4 '18 at 9:54
add a comment |
$begingroup$
I would like to contribute the following special case which I encountered when I was looking for an answer and stumbled upon this thread.
If A=uv^intercal
, then det(I+A) = 1+u^Tv
$endgroup$
I would like to contribute the following special case which I encountered when I was looking for an answer and stumbled upon this thread.
If A=uv^intercal
, then det(I+A) = 1+u^Tv
answered Dec 4 '18 at 9:54
mexmexmexmex
1236
1236
$begingroup$
P.S Someone please help with formatting this to correct transposes. Didn't manage.
$endgroup$
– mexmex
Dec 4 '18 at 9:54
add a comment |
$begingroup$
P.S Someone please help with formatting this to correct transposes. Didn't manage.
$endgroup$
– mexmex
Dec 4 '18 at 9:54
$begingroup$
P.S Someone please help with formatting this to correct transposes. Didn't manage.
$endgroup$
– mexmex
Dec 4 '18 at 9:54
$begingroup$
P.S Someone please help with formatting this to correct transposes. Didn't manage.
$endgroup$
– mexmex
Dec 4 '18 at 9:54
add a comment |
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