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8.1-8.58.1-8.5 Matrix Algebra. Quick Review Quick Review Solutions.

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Presentation on theme: "8.1-8.58.1-8.5 Matrix Algebra. Quick Review Quick Review Solutions."— Presentation transcript:

1 8.1-8.58.1-8.5 Matrix Algebra

2 Quick Review

3 Quick Review Solutions

4 What you’ll learn about Matrices Matrix Addition and Subtraction Matrix Multiplication Identity and Inverse Matrices Determinant of a Square Matrix Applications … and why Matrix algebra provides a powerful technique to manipulate large data sets and solve the related problems that are modeled by the matrices.

5 Matrix

6 Matrix Vocabulary Each element, or entry, a ij, of the matrix uses double subscript notation. The row subscript is the first subscript i, and the column subscript is j. The element a ij is the ith row and the jth column. In general, the order of an m × n matrix is m×n.

7 Example Determining the Order of a Matrix


9 Matrix Addition and Matrix Subtraction

10 Example Matrix Addition


12 Example Using Scalar Multiplication


14 The Zero Matrix

15 Additive Inverse

16 Matrix Multiplication

17 Example Matrix Multiplication


19 Identity Matrix

20 Inverse of a Square Matrix

21 Inverse of a 2 × 2 Matrix

22 Determinant of a Square Matrix

23 Inverses of n × n Matrices An n × n matrix A has an inverse if and only if det A ≠ 0.

24 Example Finding Inverse Matrices


26 Properties of Matrices Let A, B, and C be matrices whose orders are such that the following sums, differences, and products are defined. 1. Community property Addition: A + B = B + A Multiplication: Does not hold in general 2. Associative property Addition: (A + B) + C = A + (B + C) Multiplication: (AB)C = A(BC) 3. Identity property Addition: A + 0 = A Multiplication: A·I n = I n ·A = A 4. Inverse property Addition: A + (-A) = 0 Multiplication: AA -1 = A -1 A = I n |A|≠0 5. Distributive property Multiplication over addition: A(B + C) = AB + AC (A + B)C = AC + BC Multiplication over subtraction: A(B - C) = AB - AC (A - B)C = AC - BC

27 8.1-8.5 (cont.) Multivariate Linear Systems and Row Operations

28 Quick Review

29 Quick Review Solutions

30 What you’ll learn about Triangular Forms for Linear Systems Gaussian Elimination Elementary Row Operations and Row Echelon Form Reduced Row Echelon Form Solving Systems with Inverse Matrices Applications … and why Many applications in business and science are modeled by systems of linear equations in three or more variables.

31 Equivalent Systems of Linear Equations The following operations produce an equivalent system of linear equations. 1. Interchange any two equations of the system. 2. Multiply (or divide) one of the equations by any nonzero real number. 3. Add a multiple of one equation to any other equation in the system.

32 Row Echelon Form of a Matrix A matrix is in row echelon form if the following conditions are satisfied. 1. Rows consisting entirely of 0’s (if there are any) occur at the bottom of the matrix. 2. The first entry in any row with nonzero entries is 1. 3. The column subscript of the leading 1 entries increases as the row subscript increases.

33 Elementary Row Operations on a Matrix A combination of the following operations will transform a matrix to row echelon form. 1. Interchange any two rows. 2. Multiply all elements of a row by a nonzero real number. 3. Add a multiple of one row to any other row.

34 Example Finding a Row Echelon Form


36 Reduced Row Echelon Form If we continue to apply elementary row operations to a row echelon form of a matrix, we can obtain a matrix in which every column that has a leading 1 has 0’s elsewhere. This is the reduced echelon form.

37 Example Solving a System Using Inverse Matrices


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