Matrix Operations Quiz

Addition:
- Matrices of the same dimension can be added.
- Element-wise addition: ( C[i][j] = A[i][j] + B[i][j] ).
Subtraction:
- Similar to addition, matrices of the same dimension can be subtracted.
- Element-wise subtraction: ( C[i][j] = A[i][j] - B[i][j] ).
Scalar Multiplication:
- Each element of the matrix is multiplied by a scalar ( k ).
- ( B[i][j] = k \cdot A[i][j] ).
Matrix Multiplication:
- Requires that the number of columns in the first matrix matches the number of rows in the second.
- Resulting matrix ( C ) with dimensions ( m \times p ) where ( A ) is ( m \times n ) and ( B ) is ( n \times p ).
- Calculated as: ( C[i][j] = \sum_{k=1}^{n} A[i][k] \cdot B[k][j] ).
Transpose of a Matrix:
- Flips a matrix over its diagonal.
- ( (A^T)[i][j] = A[j][i] ).
Inverse of a Matrix:
- A matrix ( A ) has an inverse ( A^{-1} ) if ( A \cdot A^{-1} = I ) (identity matrix).
- Only square matrices can have inverses.
- Inverse can be calculated using methods such as Gauss-Jordan elimination or using determinants.

Definition:
- A scalar value derived from a square matrix that provides information about the matrix (e.g., whether it is invertible).
Properties:
- If the determinant is zero, the matrix is singular (non-invertible).
- The determinant changes sign if two rows (or columns) are swapped.
- Scaling a row by a factor ( k ) scales the determinant by ( k ).
- The determinant of an identity matrix is 1.
Calculation:
- 2x2 Matrix: For ( A = \begin{pmatrix} a & b \ c & d \end{pmatrix} ), ( \text{det}(A) = ad - bc ).
- 3x3 Matrix: For ( A = \begin{pmatrix} a & b & c \ d & e & f \ g & h & i \end{pmatrix} ),
  - ( \text{det}(A) = a(ei - fh) - b(di - fg) + c(dh - eg) ).
- Larger Matrices: Use methods such as:
  - Expansion by minors and cofactors.
  - Row reduction to convert to upper triangular form and then multiply the diagonal entries.
Applications:
- Used in solving systems of linear equations (Cramer's rule).
- Helps in finding eigenvalues and eigenvectors.

Matrices can be added or subtracted only if they have the same dimensions through element-wise operations.
Scalar multiplication involves multiplying each element of a matrix by a scalar ( k ), resulting in a new matrix.
Matrix multiplication requires the number of columns in the first matrix to equal the number of rows in the second, yielding a product matrix ( C ) with dimensions ( m \times p ).
The entry in the resulting matrix ( C[i][j] ) is calculated by summing products of corresponding elements from the rows of ( A ) and columns of ( B ).
The transpose, denoted ( A^T ), of a matrix flips it over its diagonal, switching rows and columns: ( (A^T)[i][j] = A[j][i] ).
A matrix ( A ) possesses an inverse ( A^{-1} ) if their product yields the identity matrix ( I ); only square matrices can have inverses.
Methods to calculate the inverse include Gauss-Jordan elimination and determinant evaluation.

The determinant is a scalar value associated with a square matrix, indicating properties like invertibility.
A determinant of zero signals that the matrix is singular and non-invertible.
The sign of the determinant reverses when two rows or columns are interchanged.
Scaling a row by a constant ( k ) results in the determinant being scaled by the same factor.
The determinant of an identity matrix is always 1.
For a 2x2 matrix ( A = \begin{pmatrix} a & b \ c & d \end{pmatrix} ), the determinant is calculated as ( \text{det}(A) = ad - bc ).
For a 3x3 matrix ( A = \begin{pmatrix} a & b & c \ d & e & f \ g & h & i \end{pmatrix} ), the determinant is ( \text{det}(A) = a(ei - fh) - b(di - fg) + c(dh - eg) ).
Determinants of larger matrices can be computed using techniques like expansion by minors or row reduction to upper triangular form, followed by multiplying the diagonal entries.
Determinants are crucial for solving systems of linear equations, particularly via Cramer’s rule, and are essential in finding eigenvalues and eigenvectors.

Inverse Matrix: For a matrix ( A ), its inverse ( A^{-1} ) satisfies ( A A^{-1} = I ), where ( I ) is the identity matrix.
Existence Criteria: A matrix possesses an inverse only if it is square and has a non-zero determinant (( \det(A) \neq 0 )).
Adjugate Method: The formula for the inverse is ( A^{-1} = \frac{1}{\det(A)} \text{adj}(A) ).
Gauss-Jordan Elimination: To find the inverse, augment the matrix ( A ) with the identity matrix ( I ) and transform it to ( [I | A^{-1}] ).
Cofactor Expansion: Calculate the determinant first, then use it alongside the adjugate to find the inverse.
Inverse Properties:
- The inverse of the inverse returns the original matrix: ( (A^{-1})^{-1} = A ).
- The inverse of a product of matrices reverses the order: ( (AB)^{-1} = B^{-1}A^{-1} ).
- The inverse of the transpose equals the transpose of the inverse: ( (A^T)^{-1} = (A^{-1})^T ).

Addition: Only valid for matrices with identical dimensions; resultant matrix ( C ) has elements ( c_{ij} = a_{ij} + b_{ij} ).
Subtraction: Similar rules as addition; resultant matrix ( C ) has elements ( c_{ij} = a_{ij} - b_{ij} ).
Scalar Multiplication: Multiply every element of matrix ( A ) by a scalar ( k ): ( B = kA ), where each element is ( b_{ij} = k \cdot a_{ij} ).
Matrix Multiplication: Non-commutative nature of matrices; ( C = AB ) is computed as ( c_{ij} = \sum_{k} a_{ik} b_{kj} ), requiring ( A )'s columns to match ( B )'s rows.
Transpose: For matrix ( A ), the transpose ( A^T ) is defined as ( (A^T){ij} = a{ji} ).
Transpose Properties:
- Addition and scalar multiplication of transposed matrices follow: ( (A + B)^T = A^T + B^T ) and ( (kA)^T = kA^T ).
- The transpose of a product reverses the order: ( (AB)^T = B^T A^T ).

Eigenvalue: Denoted by ( \lambda ), it satisfies the equation ( Av = \lambda v ) for a non-zero vector ( v ).
Eigenvector: A non-zero vector that, when multiplied by matrix ( A ), changes only by the scalar eigenvalue.
Characteristic Equation: To find eigenvalues, solve ( \det(A - \lambda I) = 0 ).
Calculation of Eigenvectors: For each eigenvalue ( \lambda ), substitute into ( (A - \lambda I)v = 0 ) to determine associated eigenvectors.
Eigenvalue Properties:
- The sum of a matrix's eigenvalues equals its trace.
- The product of eigenvalues is equivalent to the matrix's determinant.
- Eigenvectors corresponding to different eigenvalues are linearly independent.