MATHEMATICS Linear Equations

Slides:



Advertisements
Similar presentations
Elementary Linear Algebra
Advertisements

Gauss Elimination.
Gauss – Jordan Elimination Method: Example 2 Solve the following system of linear equations using the Gauss - Jordan elimination method Slide 1.
1 Copyright © 2015, 2011, 2007 Pearson Education, Inc. Chapter 4-1 Systems of Equations and Inequalities Chapter 4.
Chapter 1 Systems of Linear Equations
Linear Systems and Matrices
10.1 Gaussian Elimination Method
LIAL HORNSBY SCHNEIDER
Section 8.1 – Systems of Linear Equations
Elementary Linear Algebra Howard Anton Copyright © 2010 by John Wiley & Sons, Inc. All rights reserved. Chapter 1.
Matrices Write and Augmented Matrix of a system of Linear Equations Write the system from the augmented matrix Solve Systems of Linear Equations using.
Introduction Information in science, business, and mathematics is often organized into rows and columns to form rectangular arrays called “matrices” (plural.
Linear Algebra – Linear Equations
SYSTEMS OF LINEAR EQUATIONS
Elementary Linear Algebra Howard Anton Copyright © 2010 by John Wiley & Sons, Inc. All rights reserved. Chapter 1.
Elementary Linear Algebra
Chapter 1 Systems of Linear Equations and Matrices
Systems and Matrices (Chapter5)
ECON 1150 Matrix Operations Special Matrices
 Row and Reduced Row Echelon  Elementary Matrices.
Matrices King Saud University. If m and n are positive integers, then an m  n matrix is a rectangular array in which each entry a ij of the matrix is.
Copyright © Cengage Learning. All rights reserved. 7.4 Matrices and Systems of Equations.
Slide Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley.
Section 1.1 Introduction to Systems of Linear Equations.
Math 201 for Management Students
Euclidean m-Space & Linear Equations Row Reduction of Linear Systems.
Three variables Systems of Equations and Inequalities.
How To Find The Reduced Row Echelon Form. Reduced Row Echelon Form A matrix is said to be in reduced row echelon form provided it satisfies the following.
MATH 250 Linear Equations and Matrices
CALCULUS – II Gauss Elimination
8.1 Matrices and Systems of Equations. Let’s do another one: we’ll keep this one Now we’ll use the 2 equations we have with y and z to eliminate the y’s.
Chapter 7 Notes Honors Pre-Calculus. 7.1/7.2 Solving Systems Methods to solve: EXAMPLES: Possible intersections: 1 point, 2 points, none Elimination,
8.1 Matrices & Systems of Equations
Copyright © 2011 Pearson Education, Inc. Solving Linear Systems Using Matrices Section 6.1 Matrices and Determinants.
Matrices and Systems of Equations
Section 7-3 Solving 3 x 3 systems of equations. Solving 3 x 3 Systems  substitution (triangular form)  Gaussian elimination  using an augmented matrix.
Chapter 1 Linear Algebra S 2 Systems of Linear Equations.
Matrices and Systems of Equations
Chapter 1 Systems of Linear Equations Linear Algebra.
Arab Open University Faculty of Computer Studies M132: Linear Algebra
 Recall that when you wanted to solve a system of equations, you used to use two different methods.  Substitution Method  Addition Method.
Copyright © 2010 Pearson Education, Inc. Publishing as Pearson Addison- Wesley Systems of Equations in Three Variables Identifying Solutions Solving Systems.
College Algebra Chapter 6 Matrices and Determinants and Applications
Linear Equations in Linear Algebra
Chapter 7: Systems of Equations and Inequalities; Matrices
Chapter 4 Systems of Linear Equations; Matrices
Copyright © 2014, 2010, 2007 Pearson Education, Inc.
Systems of linear equations
Gaussian Elimination and Gauss-Jordan Elimination
Gaussian Elimination and Gauss-Jordan Elimination
Solving Systems of Equations Using Matrices
Linear Equations Gauss & Cramer’s
Linear Equations by Dr. Shorouk Ossama.
MATHEMATICS Matrix Algebra
MATHEMATICS Matrix Multiplication
L9Matrix and linear equation
Chapter 8: Lesson 8.1 Matrices & Systems of Equations
Matrices and Systems of Equations 8.1
Chapter 1 Systems of Linear Equations and Matrices
Matrices and Systems of Equations
Gaussian Elimination and Gauss-Jordan Elimination
Chapter 4 Systems of Linear Equations; Matrices
Unit 3: Matrices
Chapter 1: Linear Equations in Linear Algebra
College Algebra Chapter 6 Matrices and Determinants and Applications
Linear Equations in Linear Algebra
Chapter 4 Systems of Linear Equations; Matrices
Matrices.
Section 8.1 – Systems of Linear Equations
Matrices are identified by their size.
Presentation transcript:

MATHEMATICS Linear Equations by Dr. Eman Saad & Dr. Shorouk Ossama

Linear Equations Recall that in two dimensions a line in a rectangular xy-coordinate system can be represented by an equation of the form ax + by = c (a, b not both 0) And in three dimensions a plane in a rectangular xyz- coordinate system can be represented by an equation of the form ax + by + cz = d (a, b, c not all 0)

Examples of “linear equations”: the first being a linear equation in the variables x and y and the second a linear equation in the variables x, y, and z. More generally, we define a linear equation in the n variables x1, x2, …., xn to be one that can be expressed in the form: a1x1 + a2x2 + ….. + anxn = b In the special case where b=0, that called a homogeneous linear equation a1x1 + a2x2 + ….. + anxn = 0

The following are linear equations: x + 3y = 7 x1 – 2x2 – 3x3 + x4 = 0 (½) x – y + 3z = -1 x1 + x2 + ….. + xn = 1 The following are not linear equations: X + 3y2 = 4 3x + 2y – xy = 5 Sinx + y = 0 + 2 x2 + x3 = 1 x1

Linear Systems With Two And Three Unknowns: Linear systems in two unknowns arise in connection with intersections of lines. For example, consider the linear system. a1x + b1y = c1 a2x + b2y = c2

Inverse Method: If we have any system: x1 + x2 = 4 9x1 – x2 = 14 So: Multiply from left by A-1 A-1 A X = A-1 B X = A-1 B Get A-1 Multiply A-1 by B These are x and y A X = B

X = A-1 d X1 X2

Cramer’s Rule: a11 x1 + a12x2 = d1 a21 x1 + a22x2 = d2 The eliminated of x2 and x1 by this process leads to the solution This formula is known as Cramer’s Rule. If the denominator is zero then the two equations can have no solutions or an infinity of solutions.

Example: Solve The Equations x1 + 2x2 = 4 x1 - x2 = 1 Δ det A = = - 1 – 2 = -3 Δ1 = = -4 -2 = -6 Δ2 = = 1 - 4 = -3

+ - + Δ det A = = 1 x (2+3) +2 x (4+1) + 1 x (6-1) = 5 + 10 + 5 = 20 Δ1 = = -4 x (2+3) +2 x (-2+7) + 1 x (-3-7) = -20 +10 -10 = -20

Δ2 = = 1 x (-2+7) + 4 x (4+1) + 1 x ( 14+1) = 5 + 20 + 15 = 40 Δ3 = = Δ2 = = 1 x (-2+7) + 4 x (4+1) + 1 x ( 14+1) = 5 + 20 + 15 = 40 Δ3 = = 1 x (7+3) + 2 x (14+1) - 4 x ( 6-1) = 10 + 30 - 20 = 20 20 3 1 3

Augmented Matrices and Elementary Row Operations: As the number of equations and unknowns in a linear system increases, so does the complexity of the algebra involved in finding solutions. The required computations can be made more manageable by simplifying notation and standardizing procedures. we can abbreviate the system by writing only the rectangular array of numbers.

This is called the augmented matrix for the system This is called the augmented matrix for the system. For example, the augmented matrix for the system of equations: Since the rows (horizontal lines) of an augmented matrix correspond to the equations in the associated system,

these three operations correspond to the following operations on the rows of the augmented matrix: 1. Multiply a row through by a nonzero constant. 2. Interchange two rows. Add a constant times one row to another. These are called elementary row operations on a matrix.

Example: x + y + 2z = 9 2x + 4y – 3z = 1 3x + 6y -5z = 0 -2 R1 + R2 = R`2 -3 R2 + R3 = R`3 -3 R1 + R3 = R`3 (-2) R3 = R`3 (1/2) R2 = R`2 - R1 + R2 = R`1 - (11/2) R3 + R1 = R`1 (7/2) R3 + R2 = R`2 x = 1 y = 2 z = 3 1 4 2 5 3 6 7

Gauss Elimination: We need write the equations in form of augmented matrix for the system. The fourth column consists of the constant on the right- hand sides.

The elementary operations referred to previously become elementary row operations on the matrix. The final matrix is said to be in echelon form: that is, it has zeros below the diagonal elements starting from the top left. We can solve the equations by back substation.

Example: Find the solution by using gauss elimination - R1 + R2 = R`2 -3 R1 + R3 = R`3 R2 - R3 = R`3 1 2 3

back substation Three Equations: 0 + 0 + 3Z = 19 0 + 3Y + Z = 5 X Y Z = back substation Three Equations: 0 + 0 + 3Z = 19 0 + 3Y + Z = 5 X + Y + Z = 5

Example: Find the solution by using gauss elimination -3 R1 + R2 = R`2 -4 R1 + R3 = R`3 R2 ÷ -5 = R`2 11 R2 + R3 = R`3 1 2 3

back substation Three Equations: 0 + 0 + 6Z = 6 0 + Y + Z = 1 X Y Z = back substation Three Equations: 0 + 0 + 6Z = 6 0 + Y + Z = 1 X +2Y + Z = 2

SUMMARY Pages From 51 To 52: Cramer’s Rule Pages From 53 : Cancelled Pages From 54 To 57: Gauss Elimination Sheet (4)

Thanks