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Idempotent matrix

Idempotent matrix

Edited By Komal Miglani | Updated on Jul 02, 2025 06:34 PM IST

A matrix (plural: matrices) is a rectangular arrangement of symbols along rows and columns that might be real or complex numbers. Thus, a system of m x n symbols arranged in a rectangular formation along m rows and n columns is called an m by n matrix (which is written as m x n matrix). There are special types of matrices like Orthogonal matrices, Unitary matrices, and Idempotent matrices. In real life, we use unitary matrices in quantum mechanics.

This Story also Contains
  1. Square matrix
  2. Idempotent matrix
  3. Properties of Idempotent Matrix
  4. Solved Examples Based on Idempotent Matrices
Idempotent matrix
Idempotent matrix

In this article, we will cover the concept of unitary matrices. This category falls under the broader category of Matrices, which is a crucial Chapter in class 12 Mathematics. It is not only essential for board exams but also for competitive exams like the Joint Entrance Examination(JEE Main) and other entrance exams such as SRMJEE, BITSAT, WBJEE, BCECE, and more. A total of twelve questions have been asked on this topic in JEE MAINS(2013 - 2023) including one in 2021 and one in 2023.

Square matrix

The square matrix is the matrix in which the number of rows = number of columns. So matrix $A=\left[a_{i j}\right]_{m \times n}$ is said to be a square matrix when $m=n$.

$
\left[\begin{array}{lll}
a_{11} & a_{12} & a_{13} \\
a_{21} & a_{22} & a_{23} \\
a_{31} & a_{32} & a_{33}
\end{array}\right]_{3 \times 3} \text { or, }\left[\begin{array}{cc}
2 & -4 \\
7 & 3
\end{array}\right]_{2 \times 2}$

Idempotent matrix

An idempotent matrix is defined as a square matrix that remains unchanged when multiplied by itself.

A square matrix is said to be an idempotent matrix if it satisfies the condition A2 = A.

Properties of Idempotent Matrix

1) Every identity matrix is also an idempotent matrix, as the identity matrix gives the same matrix when multiplied by itself.

2) All idempotent matrices are singular matrices, except for the identity matrix.

3) The determinant of an idempotent matrix is either one or zero. If A is an idempotent matrix then |A| = 1 or 0.

4) The non-diagonal entries of an idempotent matrix can be non-zero entries.

5) The trace of an idempotent matrix is always an integer and equal to the rank of the matrix

6) The relationship between idempotent and involuntary matrices is if A is a square matrix “A” is said to be an idempotent matrix if and only if P = 2A − I is an involuntary matrix.

Recommended Video Based on Ídempotent Matrices


Solved Examples Based on Idempotent Matrices

Eaxmple 1: What is an index of the idempotent matrix

$
A=\left[\begin{array}{ccc}
2 & 5 & 14 \\
1 & 3 & 8 \\
-1 & -2 & -6
\end{array}\right]
$

1) 2
2) 3
3) 4
4) 5

Solution: We know that the idempotent matrix - $A^2=A$

$
A^2=\left[\begin{array}{ccc}
2 & 5 & 14 \\
1 & 3 & 8 \\
-1 & -2 & -6
\end{array}\right]\left[\begin{array}{ccc}
2 & 5 & 14 \\
1 & 3 & 8 \\
-1 & -2 & -6
\end{array}\right]=\left[\begin{array}{ccc}
-5 & -3 & 16 \\
-3 & -2 & -10 \\
2 & 1 & 6
\end{array}\right]
$

now if we multiply $A^2 \times A^2$
we get $A^4=I$.
Thus A is an idempotent matrix of order $=4$
Hence, the answer is the option (3).

Example 2: Which of the following matrices is idempotent?

1) \( \begin{pmatrix} 1 & 0 \\ 0 & 1 \end{pmatrix} \)
2) \( \begin{pmatrix} 1 & 1 \\ 0 & 1 \end{pmatrix} \)
3) \( \begin{pmatrix} 0 & 1 \\ 1 & 0 \end{pmatrix} \)
4) \( \begin{pmatrix} 0 & 0 \\ 0 & 1 \end{pmatrix} \)

Solution:
A matrix \( A \) is idempotent if \( A^2 = A \).

1. Matrix A: \( \begin{pmatrix} 1 & 0 \\ 0 & 1 \end{pmatrix} \)

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

\( A^2 = A \), so Matrix A is idempotent.

2. Matrix B: \( \begin{pmatrix} 1 & 1 \\ 0 & 1 \end{pmatrix} \)

$B^2 = \begin{pmatrix} 1 & 1 \\ 0 & 1 \end{pmatrix} \begin{pmatrix} 1 & 1 \\ 0 & 1 \end{pmatrix} = \begin{pmatrix} 1 & 2 \\ 0 & 1 \end{pmatrix}$

\( B^2 \neq B \), so Matrix B is not idempotent.

3. Matrix C: \( \begin{pmatrix} 0 & 1 \\ 1 & 0 \end{pmatrix} \)

$ C^2 = \begin{pmatrix} 0 & 1 \\ 1 & 0 \end{pmatrix} \begin{pmatrix} 0 & 1 \\ 1 & 0 \end{pmatrix} = \begin{pmatrix} 1 & 0 \\ 0 & 1 \end{pmatrix}$

\( C^2 \neq C \), so Matrix C is not idempotent.

4. Matrix D: \( \begin{pmatrix} 0 & 0 \\ 0 & 1 \end{pmatrix} \)

$D^2 = \begin{pmatrix} 0 & 0 \\ 0 & 1 \end{pmatrix} \begin{pmatrix} 0 & 0 \\ 0 & 1 \end{pmatrix} = \begin{pmatrix} 0 & 0 \\ 0 & 1 \end{pmatrix}$

\( D^2 = D \), so Matrix D is idempotent.

Hence, matrices that are idempotent are 1 and 4.

Example 3: Which of the following is a property of an idempotent matrix \( A \)?

1) \( A \) is invertible.
2) \( A \) has a trace equal to its rank.
3) The eigenvalues of \( A \) are purely imaginary.
4) \( A \) is always diagonalizable.

Solution:
1. False. An idempotent matrix is not necessarily invertible. For instance, a matrix with eigenvalue 0 is not invertible.
2. True. For an idempotent matrix, the trace (sum of eigenvalues) equals the rank because the eigenvalues are either 0 or 1.
3. False. The eigenvalues of an idempotent matrix are 0 and 1, which are not purely imaginary.
4. False. An idempotent matrix is not necessarily diagonalizable, though it can be.

Hence, the answer is option 2.

Example 4: Find the value of a for which the matrix $\left[\begin{array}{ccc}2 & -2 & -4 \\ -1 & 3 & 4 \\ a & -2 & -3\end{array}\right]$ is idempotent.

1) 2

2) -1

3) 1

4) 0

Solution: We know that, For Idempotent A2=A

$\begin{aligned} & A^2=\left[\begin{array}{ccc}2 & -2 & -4 \\ -1 & 3 & 4 \\ a & -2 & -3\end{array}\right] \times\left[\begin{array}{ccc}2 & -2 & -4 \\ -1 & 3 & 4 \\ a & -2 & -3\end{array}\right]=\left[\begin{array}{ccc}4+2-4 a & -4-6+8 & -8-8+12 \\ -2-3+4 a & 2+9-8 & 4+12-12 \\ 2 a+2-3 a & -2 a-6+6 & -4 a-8+9\end{array}\right] \\ & A^2=\left[\begin{array}{ccc}6-4 a & -2 & -4 \\ 4 a-5 & 3 & 4 \\ -a+2 & -2 & -3\end{array}\right]=A=\left[\begin{array}{ccc}2 & -2 & -4 \\ -1 & 3 & 4 \\ a & -2 & -3\end{array}\right]\end{aligned}$

Solving any element we get a=1

Hence, the answer is the option 3.

Example 5: Consider the matrix \( B \):
$B = \begin{pmatrix}
0 & 1 \\
0 & 0
\end{pmatrix}$
Is \( B \) an idempotent matrix?
1) Yes
2) No

Solution:
Compute \( B^2 \):
$B^2 = \begin{pmatrix}
0 & 1 \\
0 & 0
\end{pmatrix} \times \begin{pmatrix}
0 & 1 \\
0 & 0
\end{pmatrix} = \begin{pmatrix}
0 & 0 \\
0 & 0
\end{pmatrix}$
Since \( B^2 \neq B \), \( B \) is not idempotent.


Frequently Asked Questions (FAQs)

1. What is idempotent matrix?

 An idempotent matrix is defined as a square matrix that remains unchanged when multiplied by itself. A square matrix is said to be an idempotent matrix if it satisfies the condition A2 = A.

2. What is determinant of idempotent matrix?

The determinant of an idempotent matrix is either one or zero. If A is an idempotent matrix then |A| = 1 or 0.

3. Is the identity matrix an idempotent matrix or not?

Yes, Every identity matrix is also an idempotent matrix, as the identity matrix gives the same matrix when multiplied by itself.

4. What is the trace of the identity matrix?

 The sum of all diagonal elements of a square matrix is called the trace of a matrix. The trace of an idempotent matrix is always an integer and equal to the rank of the matrix.

5. What is the square matrix?

 The square matrix is the matrix in which the number of rows = number of columns. So a matrix $\mathrm{A}=\left[\mathrm{a}_{\mathrm{ij}}\right]_{\mathrm{m} \times \mathrm{n}}$ is said to be a square matrix when m = n.

6. What is an idempotent matrix?
An idempotent matrix is a square matrix that, when multiplied by itself, results in the same matrix. In other words, if A is an idempotent matrix, then A² = A. This property makes idempotent matrices unique and important in linear algebra and various applications.
7. How can you determine if a matrix is idempotent?
To determine if a matrix is idempotent, multiply the matrix by itself. If the result is equal to the original matrix, then it is idempotent. Alternatively, you can check if A² - A = 0, where 0 is the zero matrix.
8. What are some examples of idempotent matrices?
Some common examples of idempotent matrices include:
9. Can a non-square matrix be idempotent?
No, idempotent matrices must be square. This is because matrix multiplication is only defined for square matrices when multiplying a matrix by itself. Non-square matrices cannot be multiplied by themselves, so they cannot satisfy the idempotent property.
10. What is the relationship between idempotent matrices and projection matrices?
All projection matrices are idempotent, but not all idempotent matrices are projection matrices. Projection matrices are a subset of idempotent matrices that project vectors onto a subspace. They have the additional property of being symmetric (A = A^T), which is not required for all idempotent matrices.
11. What is the significance of idempotent matrices in linear regression?
In linear regression, the hat matrix (H = X(X^T X)⁻¹X^T) is an idempotent matrix. It projects the observed values onto the space of predicted values, making it a crucial tool in analyzing the properties of linear regression models.
12. Can a matrix with negative elements be idempotent?
Yes, a matrix with negative elements can be idempotent. The idempotent property is not dependent on the sign of the matrix elements, but on the overall structure of the matrix that satisfies A² = A.
13. Can a rotation matrix be idempotent?
No, a rotation matrix (except for the identity matrix) cannot be idempotent. Rotation matrices preserve vector lengths and angles, while idempotent matrices (except for the identity) always change vectors when applied. The only rotation matrix that is idempotent is the identity matrix, which represents a rotation by 0°.
14. Can an idempotent matrix be invertible?
An idempotent matrix is invertible if and only if it is the identity matrix. This is because if A is idempotent and invertible, then A = A² = AA⁻¹ = I, where I is the identity matrix.
15. How do idempotent matrices relate to fixed points in linear transformations?
The column space of an idempotent matrix A consists of fixed points of the linear transformation represented by A. This means that for any vector v in the column space of A, Av = v. This property is fundamental to understanding the action of idempotent matrices on vector spaces.
16. What is the relationship between idempotent matrices and nilpotent matrices?
Idempotent and nilpotent matrices are mutually exclusive, except for the zero matrix. A nilpotent matrix N satisfies N^k = 0 for some positive integer k, while an idempotent matrix A satisfies A² = A. The only matrix that satisfies both conditions is the zero matrix.
17. How do idempotent matrices relate to spectral decomposition?
The spectral decomposition of an idempotent matrix A can be written as A = λ₁P₁ + λ₂P₂ + ... + λₖPₖ, where λᵢ are the distinct eigenvalues (0 or 1) and Pᵢ are the corresponding orthogonal projection matrices. This decomposition highlights the structure of idempotent matrices in terms of their eigenspaces.
18. What is the role of idempotent matrices in error-correcting codes?
In error-correcting codes, idempotent matrices are used to construct parity-check matrices. The idempotent property ensures that repeated application of the error-correction process doesn't introduce new errors, making the coding scheme more robust.
19. What is the significance of idempotent matrices in Markov chains?
In Markov chains, idempotent transition matrices represent absorbing states. If a Markov chain reaches a state represented by an idempotent matrix, it will remain in that state in all subsequent steps, which is crucial in modeling certain stochastic processes.
20. What is the connection between idempotent matrices and oblique projections?
While orthogonal projections are always idempotent and symmetric, oblique projections are idempotent but not necessarily symmetric. Oblique projections allow for projections along directions that are not perpendicular to the target subspace, providing more flexibility in certain applications.
21. How do idempotent matrices behave under the Kronecker product?
The Kronecker product of two idempotent matrices is also idempotent. If A and B are idempotent matrices, then their Kronecker product A ⊗ B is also idempotent. This property is useful in tensor algebra and multilinear algebra applications.
22. What is the relationship between idempotent matrices and matrix exponentials?
For an idempotent matrix A, its matrix exponential e^A is equal to I + (e - 1)A, where e is the base of natural logarithms and I is the identity matrix. This simplified form of the matrix exponential for idempotent matrices is useful in various applications, including differential equations and control theory.
23. What is the significance of idempotent matrices in quantum mechanics?
In quantum mechanics, idempotent matrices are used to represent projection operators, which are crucial in describing measurements and the collapse of wave functions. The idempotent property ensures that repeated measurements yield consistent results.
24. What is the connection between idempotent matrices and projective geometry?
In projective geometry, idempotent matrices can represent projections onto subspaces. The idempotent property ensures that these projections are consistent and well-defined, making them useful tools in studying projective transformations and invariants.
25. How do idempotent matrices relate to the concept of matrix factorization?
Idempotent matrices play a role in certain matrix factorizations. For example, any idempotent matrix A can be factored as A = BC, where B and C are matrices such that CB = I. This factorization is related to the rank-nullity theorem and is useful in various linear algebra applications.
26. What is the significance of idempotent matrices in statistical analysis?
In statistics, idempotent matrices are used in the analysis of variance (ANOVA) and regression analysis. They appear in the construction of sum of squares matrices and play a crucial role in deriving the distributions of various test statistics.
27. What is the relationship between idempotent matrices and matrix decompositions?
Idempotent matrices are closely related to certain matrix decompositions. For example, in the QR decomposition of an idempotent matrix A = QR, the matrix R has a special structure with only 0s and 1s on its diagonal, reflecting the idempotent property.
28. What is the significance of idempotent matrices in control theory?
In control theory, idempotent matrices are used in the design of certain types of controllers, particularly in state feedback systems. The idempotent property ensures that the control action stabilizes at a desired state after a single application.
29. What is the significance of idempotent matrices in network analysis?
In network analysis, idempotent matrices are used to represent certain network operations, such as routing or filtering. The idempotent property ensures that these operations, when applied multiple times, have the same effect as a single application, which
30. What are the eigenvalues of an idempotent matrix?
The eigenvalues of an idempotent matrix can only be 0 or 1. This is because if λ is an eigenvalue of an idempotent matrix A, then λ² = λ (since A² = A), which is only satisfied when λ = 0 or λ = 1.
31. How does the trace of an idempotent matrix relate to its rank?
For an idempotent matrix, the trace (sum of diagonal elements) is equal to its rank (number of linearly independent rows or columns). This is because the trace is also equal to the sum of the eigenvalues, which can only be 0 or 1 for idempotent matrices.
32. How do idempotent matrices behave under addition?
The sum of two idempotent matrices is not necessarily idempotent. However, if two idempotent matrices A and B commute (AB = BA), and AB = 0, then their sum A + B is also idempotent.
33. What is the relationship between idempotent matrices and orthogonal projections?
Orthogonal projection matrices are a special case of idempotent matrices. They are both idempotent and symmetric, projecting vectors onto a subspace perpendicular to its nullspace.
34. How does matrix diagonalization relate to idempotent matrices?
An idempotent matrix A can be diagonalized as A = PDP⁻¹, where D is a diagonal matrix with only 0s and 1s on the diagonal. This diagonalization reflects the fact that idempotent matrices have only 0 and 1 as eigenvalues.
35. What is the nullspace of an idempotent matrix?
The nullspace of an idempotent matrix A consists of all vectors x such that Ax = 0. It corresponds to the eigenspace associated with the eigenvalue 0.
36. How do idempotent matrices relate to complementary subspaces?
If A is an idempotent matrix, then I - A is also idempotent, and their column spaces are complementary subspaces. This means that any vector can be uniquely decomposed into components in the column spaces of A and I - A.
37. Can the product of two idempotent matrices be idempotent?
The product of two idempotent matrices is not necessarily idempotent. However, if two idempotent matrices A and B commute (AB = BA), then their product AB is also idempotent.
38. What is the determinant of an idempotent matrix?
The determinant of an idempotent matrix can only be 0 or 1. This is because the determinant is the product of eigenvalues, and idempotent matrices have only 0 and 1 as eigenvalues.
39. How do idempotent matrices behave under matrix powers?
For an idempotent matrix A, all positive integer powers of A are equal to A itself. In other words, A^n = A for all positive integers n. This is a direct consequence of the defining property A² = A.
40. What is the connection between idempotent matrices and Boolean algebra?
Idempotent matrices have properties similar to elements in Boolean algebra. In Boolean algebra, an element a is idempotent if a * a = a, which is analogous to the matrix property A² = A. This connection is useful in certain areas of computer science and logic.
41. What is the relationship between idempotent matrices and matrix roots?
An idempotent matrix A is its own square root, cube root, and nth root for any positive integer n. This is because A^n = A for all positive integers n. However, not all matrices that are their own square root are idempotent.
42. How do idempotent matrices behave under similarity transformations?
Idempotency is preserved under similarity transformations. If A is idempotent and P is invertible, then P⁻¹AP is also idempotent. This property is useful in studying the invariance of idempotency under change of basis.
43. Can complex idempotent matrices exist?
Yes, complex idempotent matrices can exist. The definition A² = A applies to matrices with complex entries as well. However, the properties related to symmetry and orthogonality may need to be adjusted for the complex case.
44. How do idempotent matrices relate to the concept of pseudoinverses?
The Moore-Penrose pseudoinverse of an idempotent matrix A is the matrix A itself. This is because idempotent matrices already satisfy some of the key properties that define pseudoinverses, such as AA⁺A = A, where A⁺ is the pseudoinverse of A.
45. How do idempotent matrices relate to the concept of generalized inverses?
Every idempotent matrix A is its own generalized inverse, satisfying AGA = A, where G = A. This property simplifies many calculations involving generalized inverses when dealing with idempotent matrices.
46. How do idempotent matrices behave under the Jordan canonical form?
The Jordan canonical form of an idempotent matrix consists only of 1x1 Jordan blocks with eigenvalues 0 or 1. This simplified structure reflects the fact that idempotent matrices are diagonalizable and have only 0 and 1 as eigenvalues.
47. What is the relationship between idempotent matrices and matrix polynomials?
For an idempotent matrix A, any polynomial f(A) can be simplified to f(0)(I - A) + f(1)A. This is because A^n = A for all positive integers n, allowing higher powers to be reduced to the first power.
48. How do idempotent matrices relate to the concept of matrix norms?
For any idempotent matrix A, its spectral norm (the largest singular value) is always 1, unless A is the zero matrix. This property is a consequence of the fact that the eigenvalues of idempotent matrices are either 0 or 1.
49. What is the significance of idempotent matrices in graph theory?
In graph theory, idempotent matrices are used to represent certain graph properties. For example, the adjacency matrix of a graph raised to a power can be idempotent, indicating specific structural properties of the graph, such as bipartiteness.
50. How do idempotent matrices behave under matrix logarithms?
The matrix logarithm of an idempotent matrix A (excluding the zero matrix) is given by ln(A) = (A - I), where ln denotes the principal matrix logarithm. This simplified form is due to the special eigenvalue structure of idempotent matrices.
51. How do idempotent matrices behave under the matrix sign function?
The matrix sign function of an idempotent matrix A is equal to 2A - I. This simplified form is a consequence of the fact that idempotent matrices have only 0 and 1 as eigenvalues, allowing for a straightforward computation of the sign function.
52. How do idempotent matrices relate to the concept of matrix conditioning?
Idempotent matrices (excluding the zero matrix) always have a condition number of 1 with respect to the spectral norm. This is because their non-zero singular values are all equal to 1, making them well-conditioned matrices for numerical computations.
53. How do idempotent matrices behave under matrix functions?
For any analytic function f(x), the matrix function f(A) of an idempotent matrix A can be simplified to f(0)(I - A) + f(1)A. This property allows for easy computation of matrix functions for idempotent matrices, which is useful in various applications.
54. What is the connection between idempotent matrices and matrix series?
For an idempotent matrix A, many matrix series simplify significantly. For example, the geometric series I + A + A² + ... converges to (I - A)⁻¹ for ||A|| < 1, but for an idempotent A, it simplifies to I + A, regardless of the norm of A.
55. How do idempotent matrices relate to the concept of matrix stability?
Idempotent matrices (except for the zero matrix) are always marginally stable in the sense of Lyapunov stability. This is because their eigenvalues are either 0 or 1, lying on the boundary of the stable region in the complex plane.
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