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Chapter 4: Determinants

Every square matrix A is associated with a number, called its determinant and it is denoted by det (A) or |A| .

Only square matrices have determinants. The matrices which are not square do not have determinants

If A = [a], then det (A) = |A| = a

|A| = a_{11}a_{22} – a_{21}a_{12}

then determinant can be formed by enlarging the matrix by adjoining the first two columns on the right and draw lines as show below parallel and perpendicular to the diagonal.

The value of the determinant, thus will be the sum of the product of element. in line parallel to the diagonal minus the sum of the product of elements in line perpendicular to the line segment. Thus,

Δ = a_{11}a_{22}a_{33} + a_{12}a_{23}a_{31} + a_{13}a_{21}a_{32} – a_{13}a_{22}a_{31} – a_{11}a_{23}a_{32} – a_{12}a_{21}a_{33}.

Note This method doesn’t work for determinants of order greater than 3.

(i) The value of the determinant remains unchanged, if rows are changed into columns and
columns are changed into rows e.g., |A’| = |A|

(ii) If A = [a_{ij}]_{n x n} , n > 1 and B be the matrix obtained from A by interchanging two of its rows
or columns, then

det (B) = – det (A)

(iii) If two rows (or columns) of a square matrix A are proportional, then |A| = O.

(iv) |B| = k |A| ,where B is the matrix obtained from A, by multiplying one row (or column) of
A by k.

(v) |kA| = kn|A|, where A is a matrix of order n x n.

(vi) If each element of a row (or column) of a determinant is the sum of two or more terms,
then the determinant can be expressed as the sum of two or more determinants, e.g.,

(vii) If the same multiple of the elements of any row (or column) of a determinant are added to the corresponding elements of any other row (or column), then the value of the new determinant remains unchanged, e.g.,

(viii) If each element of a row (or column) of a determinant is zero, then its value is zero.

(ix) If any two rows (columns) of a determinant are identical, then its value is zero.

(x) If each element of row (column) of a determinant is expressed as a sum of two or more
terms, then the determinant can be expressed as the sum of two or more determinants.

(i) |AB| = |A||B| , where A and B are square matrices of the same order.

(ii) |A^{n}| = |A|^{n}

(iii) If A, B and C are square matrices of the same order such that ith column (or row) of A is
the sum of i th columns (or rows) of B and C and all other columns (or rows) of A, Band C are
identical, then |A| =|B| + |C|

(iv) |I_{n}| = 1,where I_{n} is identity matrix of order n

(v) |O_{n}| = 0, where O_{n} is a zero matrix of order n

(vi) If Δ(x) be a 3rd order determinant having polynomials as its elements.

(a) If Δ(a) has 2 rows (or columns) proportional, then (x – a) is a factor of Δ(x).

(b) If Δ(a) has 3 rows (or columns) proportional, then (x – a)^{2} is a factor of Δ(x). ,

(vii) A square matrix A is non-singular, if |A| ≠ 0 and singular, if |A| =0.

(viii) Determinant of a skew-symmetric matrix of odd order is zero and of even order is a nonzero
perfect square.

(ix) In general, |B + C| ≠ |B| + |C|

(x) Determinant of a diagonal matrix = Product of its diagonal elements

(xi) Determinant of a triangular matrix = Product of its diagonal elements

(xii) A square matrix of order n, is non-singular, if its rank r = n i.e., if |A| ≠ 0, then rank (A) =
n

(xiv) If A is a non-singular matrix, then |A^{-1}| = 1 / |A| = |A|^{-1}

(xv) Determinant of a orthogonal matrix = 1 or – 1.

(xvi) Determinant of a hermitian matrix is purely real .

(xvii) If A and B are non-zero matrices and AB = 0, then it implies |A| = 0 and |B| = 0.

then the minor M_{ij} of the element a_{ij} is the determinant obtained by deleting the i row and jth column.

The cofactor of the element a_{ij} is C_{ij} = (- 1)i + j M_{ij}

Adjoint of a matrix is the transpose of the matrix of cofactors of the give matrix, i.e.,

(i) The sum of the products of elements of .any row (or column) of a determinant with the cofactors of the corresponding elements of any other row (or column) is zero, i.e., if

then a_{11}C_{31} + a_{12}C_{32} + a_{13}C_{33} = 0 ans so on.

(ii) The sum of the product of elements of any row (or column) of a determinant with the
cofactors of the corresponding elements of the same row (or column) is Δ

If the elements of more than one column or rows are functions of x, then the integration can be

done only after evaluation/expansion of the determinant.

The solution of the system of simultaneous linear equations

a_{1}x + b_{1}y = C_{1} …(i)

a_{2}x + b_{2}y = C_{2} …(ii)

is given by x = D_{1} / D, Y = D_{2} / D

(i) If D ≠ 0, then the given system of equations is consistent and has a unique solution given by
x = D_{1} / D, y = D_{2} / D

(ii) If D = 0 and Dl = D2 = 0, then the system is consistent and has infinitely many solutions.

(iii) If D = 0 and one of Dl and D2 is non-zero, then the system is inconsistent.

Let the system of equations be

a1x + b1y + C1z = d1

a2x + b2y + C2z = d2

a3x + b3y + C3z = d3

Then, the solution of the system of equation is
x = D1 / D, Y = D2 / D, Z = D3 / D, it is called Cramer’s rule.

(i) If D ≠ 0, then the system of equations is consistent with unique solution.

(ii) If D = 0 and atleast one of the determinant D_{1}, D_{2}, D_{3} is non-zero, then the given system is
inconsistent, i.e., having no solution.

(iii) If D = 0 and D_{1} = D_{2} = D_{3} = 0, then the system is consistent, with infinitely many
solutions.

(iv) If D ≠ 0 and D_{1} = D_{2} = D_{3} = 0, then system has only trivial solution, (x = y = z = 0).

Every matrix satisfies its characteristic equation, i.e., if A be a square matrix, then |A – xl| = 0
is the characteristics equation of A. The values of x are called eigenvalues of A.

i.e., if x_{3} – 4x_{2} – 5x – 7 = 0 is characteristic equation for A, then
A_{3} – 4A_{2} + 5A – 7I = 0

(i) The sum of the eigenvalues of A is equal to its trace.

(ii) The product of the eigenvalues of A is equal to its determinant.

(iii) The eigenvalues of an orthogonal matrix are of unit modulus.

(iv) The feigen values of a unitary matrix are of unit modulus.

(v) A and A’ have same eigenvalues.

(vi) The eigenvalues of a skew-hermitian matrix are either purely imaginary or zero.

(vii) If x is an eigenvalue of A, then x is the eigenvalue of A^{*} .

(viii) The eigenvalues of a triangular matrix are its diagonal elements.

(ix) If x is the eigenvalue of A and |A| ≠ 0, then (1 / x) is the eigenvalue of A^{-1}.

(x) If x is the eigenvalue of A and |A| ≠ 0, then |A| / x is the eigenvalue of adj (A).

(xi) If x_{1}, x_{2},x_{3}, … ,x_{n} are eigenvalues of A, then the eigenvalues of A^{2} are x_{2}
^{2}, x_{2}
^{2},…, x_{n}
^{2}.

Let three points in a plane be A(x_{1}, y_{1}), B(x_{2}, y_{2}) and C(x_{3}, y_{3}), then

= 1 / 2 [x_{1} (y_{2} – y3) + x_{2} (y3 – y_{1}) + x3 (y_{1} – y_{2})]

where a_{i}s ∈ [α_{1}, α_{2},…, α_{n}]

Then, |A|_{max} when diagonal elements are

{ min (α_{1}, α_{2},…, α_{n})}

and non-diagonal elements are

{ max (α_{1}, α_{2},…, α_{n})}

Also, |A|_{min} = – |A|_{max}

- Chapter 5: Continuity and Differentiability
- Chapter 1: Relations and Functions
- Chapter 2: Inverse Trigonometric Functions
- Chapter 3: Matrices
- Chapter 4: Determinants
- Chapter 6: Application of Derivatives
- Chapter 7: Integrals
- Chapter 8: Application of Integrals
- Chapter 9. Differential Equations
- Chapter 10: Vector Algebra
- Chapter 12: Linear Programming
- Chapter 11: Three Dimensional Geometry

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