1. Section 2.1 Determinants by Cofactor Expansion

2. THE DETERMINANT Recall from algebra, that the function f (x) = x 2 is a function from the real numbers into the real numbers. In a similar way, the determinant is a function from square matrices into the real numbers; that is, the input is a square matrix and the output is a real number. Notation: The determinant of the matrix A is notated by det(A) or |A|.

3. Recall that the 2×2 matrix is invertible if ad − bc ≠ 0. The determinant of the matrix A is det(A) = |A| = ad − bc. Thus, DETERMINANT OF A 2×2 MATRIX

4. MINORS AND COFACTORS If A is a square matrix, then the minor of entry a ij is denoted by M ij and is defined to be the determinant of the submatrix that remains after the i th row and j th column are deleted from A. The number (−1) i + j M ij is denoted by C ij and is called the cofactor of entry a ij.

5. COFACTOR EXPANSION Theorem 2.1.1: The determinant of and n×n matrix A can be computed by multiplying the entries in any row (or any column) by their cofactors and adding the resulting products; that is, for each 1 ≤ i ≤ n and 1 ≤ j ≤ n,

6. A COMMENT ON COFACTOR EXPANSION When computing the determinant by cofactor expansion, it is helpful to expand along the row or column that contains the most zeros.

7. MULTIPLYING BY COFACTORS OF DIFFERENT ROWS If one multiplies the entries in any row by the corresponding cofactors from a different row, the sum of these products is always zero.

8. ADJOINT OF A MATRIX If A is any n×n matrix and C ij is the cofactor of a ij, then the matrix is called the matrix of cofactors from A. The transpose of this matrix is called the adjoint of A and is denoted by adj(A).

9. INVERSE OF A MATRIX USING ITS ADJOINT Theorem 1.2.1: If A is an invertible matrix, then

10. DETERMINANTS AND TRIANGULAR MATRICES Theorem 2.1.3: If A is an n×n triangular matrix (upper triangular, lower triangular, or diagonal), then det(A) is the product of the entries on the main diagonal of the matrix; that is

11. CRAMER’S RULE If Ax = b is a system of linear equations in n unknown such that det(A) ≠ 0, then the system has a unique solution. This solution is where A j is the matrix obtained by replacing the entries in the j th column of A by the entries in the matrix b.