ISI MStat PSB 2006 Problem 1 | Inverse of a matrix

This is a very beautiful sample problem from ISI MStat PSB 2006 Problem 1 based on Inverse of a matrix. Let's give it a try !!

Problem- ISI MStat PSB 2006 Problem 1


Let A and B be two invertible \( n \times n\) real matrices. Assume that \( A+B\) is invertible. Show that \( A^{-1}+B^{-1}\) is also invertible.

Prerequisites


Matrix Multiplication

Inverse of a matrix

Solution :

We are given that A,B,A+B are all invertible real matrices . And in this type of problems every information given is a hint to solve the problem let's give a try to use them to show that \( A^{-1}+B^{-1}\) is also invertible.

Observe that ,\( A(A^{-1}+B^{-1})B= (B+A) \Rightarrow |A^{-1}+B^{-1}|=\frac{|A+B|}{|A| |B| } \) taking determinant is both sides as A+B , A and B are invertible so |A+B| , |A| and |B| are non-zero . Hence \(A^{-1}+B^{-1} \) is also non-singular .

Again we have , \( A(A^{-1}+B^{-1})B= (B+A) \Rightarrow B^{-1} {(A^{-1}+B^{-1})}^{-1} A^{-1} = {(A+B)}^{-1} \) , taking inverse on both sides .

Now as A+B , A and B are invertible so , we have \( {(A^{-1}+B^{-1})}^{-1}=B {(A+B)}^{-1} A \) . Hence we are done .


Food For Thought

If \( A \& B\) are non-singular matrices of the same order such that \( (A+B) \) and \( \left(A+A B^{-1} A\right) \) are also non-singular, then find the value of \( (A+B)^{-1}+\left(A+A B^{-1} A\right)^{-1} \).


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ISI MStat PSB 2007 Problem 2 | Rank of a matrix

This is a very beautiful sample problem from ISI MStat PSB 2007 Problem 2 based on Rank of a matrix. Let's give it a try !!

Problem- ISI MStat PSB 2007 Problem 2


Let \(A\) and \(B\) be \( n \times n\) real matrices such that \( A^{2}=A\) and \( B^{2}=B\)
Suppose that \( I-(A+B)\) is invertible. Show that rank(A)=rank(B).

Prerequisites


Matrix Multiplication

Inverse of a matrix

Rank of a matrix

Solution :

Here it is given that \( I-(A+B)\) is invertible which implies it's a non-singular matrix .

Now observe that ,\( A(I-(A+B))=A-A^2-AB= -AB \) as \( A^2=A\)

Again , \( B(I-(A+B))=B-BA-B^2=-BA \) as \(B^2=B\) .

Now we know that for non-singular matrix M and another matrix N , \( rank(MN)=rank(N) \) . We will use it to get that

\( rank(A)=rank(A(I-(A+B)))=rank(-AB)=rank(AB) \) and \(rank(B)=rank(B(I-(A+B)))=rank(-BA)=rank(BA)\) .

And it's also known that \( rank(AB)=rank(BA)\) . Hence \( rank(A)=rank(B)\) (Proved) .


Food For Thought

Try to prove the same using inequalities involving rank of a matrix.


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ISI MStat PSB 2007 Problem 1 | Determinant and Eigenvalues of a matrix

This is a very beautiful sample problem from ISI MStat PSB 2007 Problem 1 based on Determinant and Eigen values and Eigen vectors . Let's give it a try !!

Problem- ISI MStat PSB 2007 Problem 1


Let \( A\) be a \( 2 \times 2\) matrix with real entries such that \( A^{2}=0 .\) Find the determinant of \( I+A\) where I denotes the identity matrix.

Prerequisites


Determinant

Eigen Values

Eigen Vectors

Solution :

Let \( {\lambda}_{1} , {\lambda}_{2} \) be two eigen values of A then , \( {{\lambda}_{1}}^2 , {{\lambda}_{2}}^2 \) .

Now it's given that \( A^2=0 \) , so we have \( {{\lambda}_{1}}^2=0 , {{\lambda}_{2}}^2 =0 \) . You may verify it ! (Hint : use the theorem that \( \lambda \) is a eigen value of matrix B and \( \vec{x}\) is it's corresponding eigen value then we can write \(Bx=\lambda \vec{x} \) or , use \(det(B- \lambda I )=0 \) ).

Hence we have \( {\lambda}_{1} =0 , {\lambda}_{2}=0 \) .

Now , eigen values of Identity matrix I are 1 . So, we can write for eigen value \( \vec{x}\) of (A+I) , \( (A+I) \vec{x}= Ax+I\vec{x}=0+\vec{x}=\vec{x} \).

Thus we get that both the eigen values of (A+1) are 1 . Again we know that determinant of a matrix is product of it's eigen values .

So, we have \(|A+I|=1\).

Do you think this solution is correct ?

If yes , then you are absolutely wrong . The mistake is in assuming A and I has same eigen vectors \( Ax+I\vec{x} \ne \vec{x} \)

Correct Solution

We have shown in first part of wrong solution that A has eigen values 0 . Hence the characteristic polynomial of A can be written as , \( |A- \lambda I|= {\lambda}^2 \) .

Now taking \( \lambda =-1 \) we get \( |A+ I|={(-1)}^2 \implies |A+I|= 1 \) .


Food For Thought

If we are given that \( A^{n} = 0 \) for positive integer n , instead of \( A^2=0 \) then find the same .


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ISI MStat PSB 2014 Problem 1 | Vector Space & Linear Transformation

This is a very beautiful sample problem from ISI MStat PSB 2014 Problem 1 based on Vector space and Eigen values and Eigen vectors . Let's give it a try !!

Problem- ISI MStat PSB 2014 Problem 1


Let \(E={1,2, \ldots, n},\) where n is an odd positive integer. Let \( V\) be
the vector space of all functions from E to \(\mathbb{R}^{3}\), where the vector space
operations are given by \( (f+g)(k) =f(k)+g(k)\), for \( f, g \in V, k \in E \
(\lambda f)(k) =\lambda f(k),\) for \( f \in V, \lambda \in \mathbb{R}, k \in E \)
(a) Find the dimension of \(V\)
(b) Let \(T: V \rightarrow V\) be the map given by \( T f(k)=\frac{1}{2}(f(k)+f(n+1-k)), \quad k \in E \)
Show that T is linear.
(c) Find the dimension of the null space of T.

Prerequisites


Linear Transformation

Null Space

Dimension

Solution :

While doing this problem we will use a standard notation for vectors of canonical basis i..e \( e_j\) . In \(R^{3} \) they are \( e_1=(1,0,0) , e_2=(0,1,0) \) and \( e_3=(0,0,1) \) .

(a) For \( i \in {1 , 2 , \cdots , n}\) and \( j \in {1 , 2 , 3}\) , let \( f_{ij}\) be the function in \(V\) which maps \( i \mapsto e_j\) and \(k \mapsto (0,0,0)\) where \(k \in {1 , 2 , \cdots , n}\) and \( k \neq i\). Then \( {f_{ij} : i \in {1 , 2 , \cdots , n} , j \in {1 , 2 , 3}}\) is a basis of \(V\) .

It looks somewhat like this , \(f_{11}(1)={(1,0,0)} ,f_{11}(2)={(0,0,0)} , \cdots , f_{11}(n)={(0,0,0)} \)

\( f_{12}(1)={(0,1,0)} ,f_{12}(2)={(0,0,0)} , \cdots , f_{12}(n)={(0,0,0)} \) , \( \cdots , f_{n3}(1)={(0,0,0)} ,f_{n3}(2)={(0,0,0)} , \cdots , f_{n3}(n)={(0,0,1)} \)

Hence , dimension of \(V\) is 3n.

(b) To show T is linear we have to show that \( T(af(k)+bg(k)) =aT(f(k))+bT(g(k)) \) for some scalar a,b .

\(T(af(k)+bg(k))=\frac{ af(k)+bg(k)+af(n+1-k)+bg(n+1-k)}{2} = a \frac{f(k)+f(n+1-k)}{2} + b \frac{g(k)+g(n+1-k)}{2} = aT(f(k))+bT(g(k)) \).

Hence proved .

(c) \( f\in ker T\) gives \(f(k)=-f(n+1-k)\) so, the values of \(f\) for the last \(\frac{n-1}{2}\) points are opposite to first \(\frac{n-1}{2}(i.e. f(n)=-f(1) ~\text{etc.})\) so we can freely assign the values of f for first \(\frac{n-1}{2}\) to any of \(e_j\) .Hence, the null space has dimension \(\frac{3(n-1)}{2}.\)


Food For Thought

let \( T: \mathbb{R}^{3} \rightarrow \mathbb{R}^{3}\) be a non singular linear transformation.Prove that there exists a line passing through the origin that is being mapped to itself.

Prerequisites : eigen values & vectors and Polynomials


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ISI MStat PSB 2010 Problem 1 | Tricky Linear Algebra Question

This is a very beautiful sample problem from ISI MStat PSB 2010 Problem 1 based on Matrix multiplication and Eigen values and Eigen vectors . Let's give it a try !!

Problem- ISI MStat PSB 2010 Problem 1


Let \(A\) be a \(4 \times 4\) matrix with non-negative entries such that the sum of the entries in each row of \( A\) equals 1 . Find the sum of all entries in matrix \(A^{5}\) .

Prerequisites


Matrix Multiplication

Eigen Values

Eigen Vectors

Solution :

Doing this problem you have to use the hint given in the question . Here the hint is that the sum of the entries in each row of \( A\) equals 1 . How can you use that ? Think about it!

Here comes the trick .

Let V be a vector such that \( V={[1,1,1,1]}^{T} \) . Now if we multiply A by V then we will get V i.e \( AV=V \) .

This is because it is given that the sum of the entries in each row of \( A\) equals 1 .

So, from \( AV=V \) we can say that 1 is an eigen value of A .

Hence \( A^5V=A^4(AV)=A^4V=A^3(AV)= \cdots = V \) . From here we can say the sum of all the entries of each rows of \(A^5 \) is 1.

Therefore the sum of all the entries of \( A^5\) is also 4 .


Food For Thought

Let \(A\) and B be \( n \times n \) matrices with real entries satisfying \(tr(A A^{T}+B B^{T})=tr(A B+A^{T} B^{T})\) .
Prove that \( A=B^{T}\) .

Hint : Use properties of trace that's the trick here .


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ISI MStat PSB 2015 Problem 2 | Vector Space & its Dimension

This is a beautiful problem from ISI MStat PSB 2015 Problem 2. We provide detailed solution with prerequisite mentioned explicitly.

Problem- ISI MStat PSB 2015 Problem 2

For any \(n \times n\) matrix \( A=\left(\left(a_{i j}\right)\right),\) consider the following three proper-
ties:

  1. \(a_{i j}\) is real valued for all \(i, j\) and \(A\) is upper triangular.
  2. \(\sum_{j=1}^{n} a_{i j}=0,\) for all \(1 \leq i \leq n\)
  3. \( \sum_{i=1}^{n} a_{i j}=0,\) for all \(1 \leq j \leq n\)
    Define the following set of matrices:
    \( c_n \) = {A: A is \( n \times n \) and satisfies (1),(2) and (3) above }

(a) Show that \( c_n \) is a vector space for any \(n \geq 1\) .

(b) Find the dimension of , \( c_n \) when n = 2 and n = 3.

Prerequisites

Solution

(a) To show that \( c_n \) is a vector space for any \(n \geq 1\)

So, here if we can show that \( c_n \) is a subspace of the vector space of \( n\times n \) real matrices with usual matrix addition and scalar multiplication then we are done!

Let's try to show this ,

Putting \(a_{i j} =0\) for all i,j then \( A= \left(\left(a_{i j}\right)\right),\) satisfies all the properties (1),(2) & (3) .

So, \( \begin{pmatrix} 0 & 0 &... & 0 \\ 0 & 0 &... & 0 \\ \vdots & \vdots & \vdots \\ 0 & 0 &... & 0 \end{pmatrix} \) \( \epsilon \) \( c_n \)

Shall show that (i) for all \( A , B \) \( \epsilon \) \( c_n \) , \( A + B \epsilon c_n \) and

(ii) for all \( A \) \( \epsilon \) \( c_n \) for all \( p_1 \epsilon\) {\( \mathbb{R}\) }-{0} , \( p_1 A \epsilon c_n \)

For (i) Take any \( A=((a_{i j})) , B=(( b_{i j})) \) \( \epsilon \) \( c_n \)

Let , D=\(A + B \) and if \( D=(( d_{i j}))\) then \( d_{ij}= a_{i j} + b_{i j} \)

Now we will see whether D satisfies all the three properties (1),(2) and (3)

\( d_{ij} =0\) when \(a_{i j}=0\) and \(b_{i j} =0 \)

Hence as A and B are upper triangular matrix , D is also an upper triangular matrix .

So it satisfies property (1)

Again , \(\sum_{j=1}^{n} a_{i j}=0,\) for all \(1 \leq i \leq n\) and \(\sum_{j=1}^{n} b_{i j}=0,\) for all \(1 \leq i \leq n\) ,

then \(\sum_{j=1}^{n} d_{i j}=0,\) for all \(1 \leq i \leq n\) as \( d_{ij}=a_{i j} + b_{i j} \)

Hence it satisfies property (2) .

Now we have \( \sum_{i=1}^{n} a_{i j}=0,\) for all \(1 \leq j \leq n\) and \( \sum_{i=1}^{n} b_{i j}=0,\) for all \(1 \leq j \leq n\) ,then \( \sum_{i=1}^{n} d_{i j}=0,\) for all \(1 \leq j \leq n\) as \( d_{ij}=a_{i j} + b_{i j} \)

Hence it satisfies the properties (3)

For (ii) Take any \( A=((a_{i j})) \) \( \epsilon \) \( c_n \)

take any \( p_1 \epsilon\) {\( \mathbb{R}\) }-{0}

Let, \( K=p_1 A\) and if \(K=(( k_{i j}))\) then \( d_{ij}= p_1 a_{i j} \)

Then , \( k_{ij} =0\) when \(a_{i j}=0\)

Hence as A is an upper triangular matrix , K is also an upper triangular matrix .

So it satisfies property (1)

Again , \(\sum_{j=1}^{n} a_{i j}=0,\) for all \(1 \leq i \leq n\) then \(\sum_{j=1}^{n} k_{i j}=0,\) for all \(1 \leq i \leq n\) as \( k_{ij}=p_1 a_{i j} \)

Hence it satisfies property (2) .

Now we have \( \sum_{i=1}^{n} a_{i j}=0,\) for all \(1 \leq j \leq n\) ,then \( \sum_{i=1}^{n} k_{i j}=0,\) for all \(1 \leq j \leq n\) as \( k_{ij}=p_1 a_{i j} \)

Hence it satisfies the properties (3)

So, \( c_n \) is closed under vector addition and scalar multiplication.

Therefore , \( c_n \) is a subspace of the vector space of \( n \times n \) real matrices with usual matrix addition and scalar multiplication . Hence we are done !

(b) n=2 ,

\( A=((a_{i j})) \) \( \epsilon \) \( c_2\) then , \( A= \begin{pmatrix} a_{11} & a_{12} \\ 0 & a_{22} \end{pmatrix} \)by property (1) , \( a_{11}+a_{12}=0 , a_{22}=0 \)---(I) by property (2) and \( a_{11}=0 , a_{12}+a_{22}=0 \)---(II) by property (3) .

Now solving (I) and (II) we get \( A= \begin{pmatrix} 0 & 0 \\ 0 & 0 \end{pmatrix} \)

Giving , \( c_2\) = { \(\begin{pmatrix} 0 & 0 \\ 0 & 0 \end{pmatrix} \) } hence \(dim(c_2)=0 \)

n=3

\( A=((a_{i j})) \) \( \epsilon \) \( c_3\) then , \( A= \begin{pmatrix} a_{11} & a_{12} & a_{13} \\ 0 & a_{22}& a_{23} \\ 0 & 0& a_{33} \end{pmatrix} \) by property (1) , \( a_{11}+a_{12}+ a_{13}=0 , a_{22}+a_{23}=0 , a_{33}=0 \)---(I) by property (2) and \( a_{11}=0 , a_{12}+a_{22}=0 a_{13}+a_{23}+a_{33}=0 \)---(II) by property (3) .

Now solving (I) and (II) we get \(a_{11}=0 , a_{33}=0 \) \( a_{13}=-a_{12}=a_{22}=-a_{23}=-a_{13}=t\) (say) then ,

\( A= t \begin{pmatrix} 0 & -1 & -1 \\ 0 & 1 & -1 \\ 0 & 0& 0\end{pmatrix} \) , \(t \epsilon R\)

Giving , \( c_3 \)= {t \(\begin{pmatrix} 0 & -1 & -1 \\ 0 & 1 & -1 \\ 0 & 0& 0\end{pmatrix} \)} ,\(t \epsilon R\) .

Hence , \(dim ( c_3 )=1\)

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Eigen Value of a matrix | IIT JAM 2017 | Problem 58

Try this problem from IIT JAM 2017 exam (Problem 58) and know how to evaluate Eigen value of a Matrix.

Eigen value of a Matrix | IIT JAM 2017 | Problem 58


Let $\alpha, \beta, \gamma, \delta$ be the eigenvalues of the matrix
$$
\begin{bmatrix}
0 & 0 & 0 & 0 \\
1 & 0 & 0 & -2 \\
0 & 1 & 0 & 1 \\
0 & 0 & 1 & 2
\end{bmatrix}
$$
Then find the value of $\alpha^{2}+\beta^{2}+\gamma^{2}+\delta^{2}$

Key Concepts


Linear Algebra

Matrix

Eigen Values

Check the Answer


Answer: $6$

IIT JAM 2017 , Problem 58

Try with Hints


Characteristic Equation of a matrix $A$ of order $n$ is defined by $|A-xI|=0$, where $x$ is scalar and $I$ is the identity matrix of order $n$.

The roots of the characteristic equation are called the Eigen Values of that matrix.

Now it is easy. Give it a try.

Let

$$
A=\begin{bmatrix}
0 & 0 & 0 & 0 \\
1 & 0 & 0 & -2 \\
0 & 1 & 0 & 1 \\
0 & 0 & 1 & 2
\end{bmatrix}
$$

Then its characteristic equation is $|A-xI|=0$ for some scalar $x$ and $I$ the identity matrix of order $4$

i.e., $
\begin{vmatrix}
-x & 0 & 0 & 0 \\
1 & -x & 0 & -2 \\
0 & 1 & -x & 1 \\
0 & 0 & 1 & 2-x
\end{vmatrix}
=0$

$
\Rightarrow -x\begin{vmatrix}
-x & 0 & -2 \\
1 & -x & 1 \\
0 & 1 & 2-x
\end{vmatrix}
=0 $

$\Rightarrow -x[-x\{(-x)(2-x)\}-1]-2(1-0)]=0$

$\Rightarrow x^{4}-2 x^{3}-x^{2}+2 x=0$

$\Rightarrow x(x^{3}-2 x^{2}-x+2)=0$

$\Rightarrow x(x-1)(x-2)(x+1)=0$

Then the eigen values are : $0,1,-1,2$

Then, $\alpha^{2}+\beta^{2}+\gamma^{2}+\delta^{2} =0^2+1^2+(-1)^2+2^2=6$ [ANS]

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Minimal Polynomial of a Matrix | TIFR GS-2018 (Part B)

Try this beautiful problem from TIFR GS 2018 (Part B) based on Minimal Polynomial of a Matrix. This problem requires knowledge of linear algebra.

Minimal Polynomial of a matrix - TIFR GS- Part B (Problem 10)


The minimal polynomial of $\begin{pmatrix} 2 &1& 0&0 \\ 0 &2&0&0 \\ 0&0&2&0\\ 0&0&1&5\end{pmatrix}$ is

  • $(x-2)(x-5)$
  • $(x-2)^2(x-5)$
  • $(x-2)^3(x-5)$
  • None of these

Key Concepts


Linear Algebra

Matrix / Vector Space

Characteristic Polynomial

Check the Answer


Answer: $(x-2)^2(x-5)$

TIFR GS -2018 (Part- B) | Problem No 10

Graduate Texts in Mathematics : Springer-Verlag

Try with Hints


Some Definitions and Results Needed :

  1. Monic Polynomial : A polynomial is said to be monic if the coefficient of the highest degree term is 1.
  2. Characteristic Polynomial of a matrix : Let $A$ be a square matrix of order $n$ then the polynomial $|A-\lambda I_n|$ is called its characteristic polynomial and $|A-\lambda I_n|=0$ is called the characteristic equation. [$I_n$ is the identity matrix of order $n$]
  3. Cayley - Hamilton Theorem : If $p(\lambda)$ is the characteristic polynomial of an $n\times n$ matrix $A$ over a field $F$, then the matrix $A$ satisfies the equation $p(x)=0$, i.e., $p(A)=0$. In other words, every square matrix satisfies its own characteristic equation.
  4. Minimal Polynomial : The monic polynomial of lowest degree satisfied by a square matrix $A$ is called its minimal polynomial
  5. Let $p(\lambda)$ and $m(\lambda)$ be the characteristic and minimal polynomials of a square matrix $A$ of order $n$ respectively. Then either both $p(\lambda)$ and $m(\lambda)$ are of degree $n$ or $m(\lambda)$ is a factor of $p(\lambda)$ .
  6. Minimal polynomial is unique.

So all the ingredients you need to cook the problem are given... Can you make it delicious ?

To find the Characteristic equation of the given matrix :

Let $A= \begin{pmatrix} 2 &1& 0&0 \\ 0 &2&0&0 \\ 0&0&2&0\\ 0&0&1&5\end{pmatrix} $

Then, $|A-\lambda I_4|= \begin{vmatrix} 2-\lambda &1& 0&0 \\ 0 &2-\lambda &0&0 \\ 0&0&2-\lambda &0\\ 0&0&1&5-\lambda \end{vmatrix} $

$\quad = (2-\lambda)^3(5-\lambda) = p(\lambda) $ [say]

then, the characteristic equation of $A$ is $p(x)=(x-2)^3(x-5)=0$

Then By Cayley Hamilton Theorem $(A-2I_4)^2(A-5I_4)=O_{4 \times 4}$ [$O_{4 \times 4}$ is the NULL MATRIX of order $4$]

As minimal polynomial is unique then if minimal polynomial is a polynomial of degree $4$ it is same as the characteristic polynomial by Property 5 in the first hint and if minimal polynomial is less than degree $4$ then it is a factor of characteristic polynomial.

all the factors of characteristic polynomial are :

$p_1(x)=(x-2)(x-5),\quad p_2(x)=(x-2)^2(x-5),\\ \text{ and } p(x)=(x-2)^3(x-5)$

Lets Find $p_1(A)$ i.e., $(A-2I_4)(A-5I_4)$ :

$(A-2I_4)(A-5I_4)=\begin{pmatrix} 0 &1& 0&0 \\ 0 &0&0&0 \\ 0&0&0&0\\ 0&0&1&3\end{pmatrix} \times \begin{pmatrix} -3 &1& 0&0 \\ 0 &-3&0&0 \\ 0&0&-3&0\\ 0&0&1&0\end{pmatrix}$

$\quad\quad= \begin{pmatrix} 0 &-3& 0&0 \\ 0 &0&0&0 \\ 0&0&0&0\\ 0&0&0&0\end{pmatrix} \ne O_{4 \times 4}$

Now, $p_2(A)$ i.e., $(A-2I_4)^2(A-5I_4)$ :

$(A-2I_4)^2(A-5I)=\begin{pmatrix} 2 &1& 0&0 \\ 0 &2&0&0 \\ 0&0&2&0\\ 0&0&1&5\end{pmatrix}^2 \times \begin{pmatrix} -3 &1& 0&0 \\ 0 &-3&0&0 \\ 0&0&-3&0\\ 0&0&1&0\end{pmatrix}$

$\quad\quad= \begin{pmatrix} 0 &0& 0&0 \\ 0 &0&0&0 \\ 0&0&0&0\\ 0&0&3&9\end{pmatrix} \times \begin{pmatrix} -3 &1& 0&0 \\ 0 &-3&0&0 \\ 0&0&-3&0\\ 0&0&1&0\end{pmatrix} = O_{4 \times 4} $

Therefore the lowest degree monic polynomial satisfied by $A$ is $(x-2)^2(x-5)$.

Hence the minimal polynomial is $(x-2)^2(x-5)$

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Eigen Values of a Matrix : IIT JAM 2016 Problem Number 13

What are Eigen Values of a Matrix?


The Eigen values of a matrix are the roots of its characteristic equation.

Try the problem from IIT JAM 2016


The largest eigen value of the matrix

$A = \begin{bmatrix}
1 & 4 & 16 \\[0.3em]
4 & 16 & 1 \\[0.3em]
16 & 1 & 4 \\
\end{bmatrix}$ is

$\textbf{(A)}\qquad 16\qquad \textbf{(B)}\qquad 21\qquad \textbf{(C)}\qquad 48\qquad \textbf{(D)}\qquad 64\qquad $

IIT JAM 2016 Problem Number 13

Finding the largest eigen value of a matrix

6 out of 10

Higher Algebra : S K Mapa

Knowledge Graph- Eigen values of a matrix


Eigen value of matrix knowledge graph

Use some hints


In order to find the largest eigen value of the given matrix we have to find all the eigen values of the given matrix, then we can find the largest among them. Now it is very easy to find the eigen values. Give it a try!!!

Now the process of rank starts with finding the characteristics polynomial of the given matrix, i.e., $\textbf{ det }(A-\lambda I)=0$. We have to find the value of this $\lambda $ which is the eigen value. Now it is very easy to find the determinant of $(A-\lambda I)$. Try to cook this up.

So, now let's do some calculation.

$ \textbf{det}(A-\lambda I) = \left| \begin{matrix}
1-\lambda & 4 & 16 \\[0.3em]
4 & 16-\lambda & 1 \\[0.3em]
16 & 1 & 4 -\lambda \\
\end{matrix} \right| =0 $

Now we are half way done just our calculation part remains.

$|A-\lambda I|=(1-\lambda)[64-20 \lambda + \lambda ^2-1]-4[16-4 \lambda -16]+16[4-256+16 \lambda]=0$

$\Rightarrow \quad (1- \lambda )( \lambda ^2-20 \lambda +63)+16 \lambda +256 \lambda -4032=0$

$\Rightarrow \quad \lambda ^2-20 \lambda +63 - \lambda ^3+20 \lambda ^2-63 \lambda +272 \lambda -4032=0$

$\Rightarrow \quad - \lambda ^3+21 \lambda ^2+189 \lambda -3969=0$

$ \Rightarrow \quad \lambda ^3-21 \lambda ^2-189 \lambda +3969=0 $

$\Rightarrow \quad(\lambda-21)(\lambda^2-189)=0$

Therefore $\lambda=21, \quad -13.747727, \quad 13.747727$

So from here we can clearly see

$\lambda=21$ is the largest among all the eigen values.

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Diagonilazibility in triangular matrix: TIFR GS 2018 Part A Problem 20

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Understand the problem

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TIFR GS 2018 Part A Problem 20[/et_pb_accordion_item][et_pb_accordion_item title="Topic" _builder_version="3.27.3" hover_enabled="0" _i="1" _address="0.1.0.0.1" open="off"]Linear Algebra[/et_pb_accordion_item][et_pb_accordion_item title="Difficulty Level" _builder_version="3.27.3" hover_enabled="0" _i="2" _address="0.1.0.0.2" open="off"]Hard[/et_pb_accordion_item][et_pb_accordion_item title="Suggested Book" _builder_version="3.27.3" hover_enabled="0" _i="3" _address="0.1.0.0.3" open="on"]Linear Algebra; Hoffman and Kunze[/et_pb_accordion_item][/et_pb_accordion][et_pb_text _builder_version="3.23.3" text_font="Raleway|300|||||||" text_text_color="#ffffff" header_font="Raleway|300|||||||" header_text_color="#e2e2e2" background_color="#0c71c3" border_radii="on|5px|5px|5px|5px" box_shadow_style="preset3" custom_margin="48px||48px" custom_padding="20px|20px|20px|20px" _i="1" _address="0.1.0.1"]

Start with hints

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Let us start by assuming A is diagonalizable. Let’s see what it means to say A is diagonalizable.
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Food for Thought:
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Watch the video

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Connected Program at Cheenta

[/et_pb_text][et_pb_blurb title="College Mathematics Program" url="https://cheenta.com/collegeprogram/" image="https://cheenta.com/wp-content/uploads/2018/03/College-1.png" _builder_version="3.23.3" header_font="||||||||" header_text_color="#e02b20" header_font_size="48px" border_color_all="#e02b20" link_option_url="https://cheenta.com/collegeprogram/" _i="6" _address="0.1.0.6"]

The higher mathematics program caters to advanced college and university students. It is useful for I.S.I. M.Math Entrance, GRE Math Subject Test, TIFR Ph.D. Entrance, I.I.T. JAM. The program is problem driven. We work with candidates who have a deep love for mathematics. This program is also useful for adults continuing who wish to rediscover the world of mathematics.[/et_pb_blurb][et_pb_button button_url="https://cheenta.com/collegeprogram/" button_text="Learn More" button_alignment="center" _builder_version="3.23.3" custom_button="on" button_bg_color="#0c71c3" button_border_color="#0c71c3" button_border_radius="0px" button_font="Raleway||||||||" button_icon="%%3%%" button_text_shadow_style="preset1" box_shadow_style="preset1" box_shadow_color="#0c71c3" background_layout="dark" _i="7" _address="0.1.0.7"][/et_pb_button][et_pb_text _builder_version="3.22.4" text_font="Raleway|300|||||||" text_text_color="#ffffff" header_font="Raleway|300|||||||" header_text_color="#e2e2e2" background_color="#0c71c3" border_radii="on|5px|5px|5px|5px" box_shadow_style="preset3" custom_margin="50px||50px" custom_padding="20px|20px|20px|20px" _i="8" _address="0.1.0.8"]

Similar Problems

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