IAS PYQs 3
1988
1) Show that a linear transformation of a vector space of dimension into a vector space of dimension over the same field can be represented by a matrix. is linear transformation of into such that , find the matrix of .
2) Determine a basis of the subspace spanned by the vectors , , , .
3) Show that it is impossible for any real symmetric matrix of the form to have identical eigen values.
4) Prove that the eigen values of a Hermitian matrix are all real and that an eigen value of a skew Hermitian matrix is either zero or a pure imaginary number.
5) By converting to an echelon matrix, determine the rank of
6) Given does it follow that ? Can you provide a counter example?
7) Find a non- singular transformation of three variables which simultaneously diagonalizes.
Give the diagonal form of .
1987
1.(i) Find all matrices which commute with the matrix
1.(ii) Prove that the product of two symmetric matrices is a symmetric matrix, if and only if the matrices commute.
2) Show that the rank of the product of two square matrices each of order , satisfies the inequality , where stands for the rank of a square matrix .
3) If find the rank of the matrix
TBC
4) If the eigen values of a matrix A are and if is a polynomial in show that the eigen values of the matrix, are .
5) If is a skew symmetric matrix and the corresponding unit matrix, show that is orthogonal. Hence construct an orthogonal matrix, if
6) (i) If are two arbitrary square matrices of which is non-singular, show that and have the same characteristic polynomial.
(ii) Show that a real matrix is orthogonal, if and only if for all real vectors .
7) Show that a necessary and sufficient condition for a system of Linear equations to be consistent is that the rank of the coefficient matrix is equal to the rank of the augmented matrix. Hence show that the system , , is consistent and has a unique solution. Determine this solution.
8) In an -dimensional vector space the system of vectors are linearly independent and can be linearly expressed to terms of the vectors . show that .
Find a maximal linearly independent subsystem of the system of linear forms , , , .
9) Let : be a linear transformation. If is finite dimensional then, show that rank (T) + nullity =.
10) Prove that two finite dimensional vector spaces and over the same field are isomorphic if they are of the same dimension .
11.(a) Prove that every square matrix is a root of its characteristic polynomial.
11.(b) Determine the eigen values and the corresponding eigen vectors of
12) Let and be - square matrices over . show that and have the same eigen values.
1986
1) If , , are three matrices, show that =. Show by an example that the matrix multiplication is non-commutative.
2) Examine the correctness or otherwise of the statements:
(i) The division law is not valid in matrix algebra.
(ii) If , are square matrices each of order and is the corresonding unit matrix, the equation = can never hold.
3) Find a matrix such that
may hold.
4) Find a maximal linearly independent subsystem in the system of vectors:
, , and .
5) Show that the system of equations
although consistent, is not uniquely solvable. Determine the general solution by choosing as a parameter.
6) Show that
(i) a square matrix is singular, iff, atleast one of its characteristic roots (i.e., eigen values) is zero.
(ii) the rank of an matrix A remains unchanged, if is pre-multiplied or post-multiplied by a non-singular matrix and that rank A. Prove that is a vector space over .
7) Find all the eigenvalues and a basis for each eigen space for the matrix .
8) Prove that every square matrix satisfies its own characteristic equation.
9) Prove that the rank of the product of two matrices cannot exceed the rank of either of them.
10) If , are two subspaces of a vector space , show that their dimensions satisfy.
11) Let and be vector spaces over the same field and , let be a basis of . Show that a map : can be uniquely extended to a linear transformation : , whose restriction to the given basis is , that is ; .
12.(i) If and are two linear transformations on the space, and if and exist, then show that exists and .
12.(ii) Prove that similar matrices have the same characteristic polynomial and hence the same eigen values.
12.(iii) Prove that the eigen values of a Hermitian matrix are real.
13) Reduce to canonical form
(i) Find reciprocal of the matrix
, show that the transform of the matrix by , i.e. TAT is the diagonal matrix. Determine the eigen values of the matrix .
1985
1) Let , prove that the set form the basis of the vector space of matrices.
2) Find the inverse of the matrix .
3) Consider the basis of , where , , . Express in terms of the basis , , . Let be defined as , , . Find .
4) Reduce the matrix to echelon form .
5) Show that if is an eigen value of matrix then is an eigen value of if is a positive integer.
6) Determine the vectors , , in are linearly independent.
7) Solve
8) Let be the vector space of all functions from in to ; let be the subset of even functions , , and be the subset of odd functions . Prove that
(i) and are subspaces of
(ii)
(iii)
9) Find the dimension and a basis of the solution space of the system
10) Let and be subspaces of a finite dimensional vector space . Prove that .
11) Prove that every matrix is a zero of its characteristic polynomial.
12) If is an orthogonal matrix and if where is non-singular, then prove that is orthogonal.
1984
1) If and are finite-dimensional subspaces of a vector space then show that is finite dimensional and
2) Show that row equivalent matrices have the same rank.
3) A linear transformation of a vector space with finite basis is non-singular iff the vectors are linearly independent in . When this is the case, show that has a linear inverse, with .
4) Solve the following system of equations:
5) Let be a square matrix and be non-singular. Let . Show that
(i) and have the same eigen values
(ii) Trace trace
(iii) If be an eigen vector of A corresponding to an eigen value then is a eigen vector of corresponding to the same eigen value.
6) A matrix has the eigen values , , and and the corresponding eigen vectors are , , . Find the matrix.
7) Let be the set of all functions from a non-empty set into a field for any functions and any scalar let and be the functions in defined as follows:
for every .
8) Find all the eigenvalues and a basis for each eigen space for the matrix .
9) Prove that every square matrix satisfies its own characteristic equation.
10) Prove that the rank of the product of two matrices cannot exceed the rank of either of them.
1983
1) Let be a finitely generated vector space. Show that has a finite basis and any two bases of have the same number of vectors.
2) Let be the vector space of polynomials of degree . Determine whether the following vectors of are linearly dependent or independent: , , .
3) For any linear transformation , prove that = .
4) Show that every non-singular matrix can be expressed as a product of elementary matrices.
5) Reduce the matrix to its normal form and hence or otherwise determine its rank.
6) Show that the equations
are consistent and solve them.
7) Prove that every square matrix satisfies its characteristic equation. Use this result to find the inverse of .
8) Find the eigen values and eigen vectors of the matrix .
9) Show that the characteristic roots of an upper or lower triangular matrix are just the diagonal elements of the matrix.