Lecture 1: Systems of linear equations and their solutions

Similar documents
Matrices. A matrix is a method of writing a set of numbers using rows and columns. Cells in a matrix can be referenced in the form.

Contents. 1 Vectors, Lines and Planes 1. 2 Gaussian Elimination Matrices Vector Spaces and Subspaces 124

Chapter 1: Systems of Linear Equations

MATH 304 Linear Algebra Lecture 20: The Gram-Schmidt process (continued). Eigenvalues and eigenvectors.

Lecture 1 Systems of Linear Equations and Matrices

Conceptual Questions for Review

j=1 u 1jv 1j. 1/ 2 Lemma 1. An orthogonal set of vectors must be linearly independent.

Linear Algebra Review. Vectors

Linear Algebra Practice Problems

Lecture 22: Section 4.7

Linear Algebra. Min Yan

From Lay, 5.4. If we always treat a matrix as defining a linear transformation, what role does diagonalisation play?

Lecture 7. Econ August 18

Final Exam Practice Problems Answers Math 24 Winter 2012

Chapter 5. Linear Algebra. A linear (algebraic) equation in. unknowns, x 1, x 2,..., x n, is. an equation of the form

ANALYTICAL MATHEMATICS FOR APPLICATIONS 2018 LECTURE NOTES 3

The value of a problem is not so much coming up with the answer as in the ideas and attempted ideas it forces on the would be solver I.N.

Lecture Summaries for Linear Algebra M51A

1.5 F15 O Brien. 1.5: Linear Equations and Inequalities

Math 1553, Introduction to Linear Algebra

Math 51, Homework-2. Section numbers are from the course textbook.

Systems of linear equations

Math 51, Homework-2 Solutions

Chapter 3. Vector spaces

Linear Algebra. Preliminary Lecture Notes

Department of Aerospace Engineering AE602 Mathematics for Aerospace Engineers Assignment No. 4

Math 520 Exam 2 Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008

Linear Algebra Primer

which arises when we compute the orthogonal projection of a vector y in a subspace with an orthogonal basis. Hence assume that P y = A ij = x j, x i

Abstract Vector Spaces

Lecture 4: Gaussian Elimination and Homogeneous Equations

MODULE 8 Topics: Null space, range, column space, row space and rank of a matrix

2. Every linear system with the same number of equations as unknowns has a unique solution.

Linear Algebra. Preliminary Lecture Notes

Homework 2. Solutions T =

Elementary maths for GMT

Math 302 Outcome Statements Winter 2013

x y = 1, 2x y + z = 2, and 3w + x + y + 2z = 0

Math 307 Learning Goals

1 Inner Product and Orthogonality

Vectors Coordinate frames 2D implicit curves 2D parametric curves. Graphics 2008/2009, period 1. Lecture 2: vectors, curves, and surfaces

2018 Fall 2210Q Section 013 Midterm Exam I Solution

MATRICES. a m,1 a m,n A =

Some Notes on Linear Algebra

1. Linear systems of equations. Chapters 7-8: Linear Algebra. Solution(s) of a linear system of equations (continued)

DIAGONALIZATION. In order to see the implications of this definition, let us consider the following example Example 1. Consider the matrix

MATH 315 Linear Algebra Homework #1 Assigned: August 20, 2018

Mathematical foundations - linear algebra

. The following is a 3 3 orthogonal matrix: 2/3 1/3 2/3 2/3 2/3 1/3 1/3 2/3 2/3

YORK UNIVERSITY. Faculty of Science Department of Mathematics and Statistics MATH M Test #2 Solutions

Review of Linear Algebra

Inner product spaces. Layers of structure:

Chapter 5. Eigenvalues and Eigenvectors

Fundamentals of Linear Algebra. Marcel B. Finan Arkansas Tech University c All Rights Reserved

Lecture 15, 16: Diagonalization

LECTURES 4/5: SYSTEMS OF LINEAR EQUATIONS

2. Review of Linear Algebra

Graphing Systems of Linear Equations

Solutions to Review Problems for Chapter 6 ( ), 7.1

Chapter 5 Eigenvalues and Eigenvectors

Math 1060 Linear Algebra Homework Exercises 1 1. Find the complete solutions (if any!) to each of the following systems of simultaneous equations:

Section 7.3: SYMMETRIC MATRICES AND ORTHOGONAL DIAGONALIZATION

MAC Module 12 Eigenvalues and Eigenvectors. Learning Objectives. Upon completing this module, you should be able to:

MAC Module 12 Eigenvalues and Eigenvectors

A A x i x j i j (i, j) (j, i) Let. Compute the value of for and

MAT 1302B Mathematical Methods II

Chapter 7. Tridiagonal linear systems. Solving tridiagonal systems of equations. and subdiagonal. E.g. a 21 a 22 a A =

3. Vector spaces 3.1 Linear dependence and independence 3.2 Basis and dimension. 5. Extreme points and basic feasible solutions

Extreme Values and Positive/ Negative Definite Matrix Conditions

Chapter 1 Vector Spaces

Math113: Linear Algebra. Beifang Chen

Lecture Notes in Mathematics. Arkansas Tech University Department of Mathematics. The Basics of Linear Algebra

SOLUTIONS FOR PROBLEMS 1-30

Linear Algebra Practice Problems

Linear Algebra. Ben Woodruff. Compiled July 17, 2010

Review problems for MA 54, Fall 2004.

SCORE BOOSTER JAMB PREPARATION SERIES II

Linear Algebra Massoud Malek

Lecture 3: Review of Linear Algebra

MATH SOLUTIONS TO PRACTICE MIDTERM LECTURE 1, SUMMER Given vector spaces V and W, V W is the vector space given by

Math Precalculus I University of Hawai i at Mānoa Spring

Lecture 3: Review of Linear Algebra

Chapter Two Elements of Linear Algebra

1 - Systems of Linear Equations

PH1105 Lecture Notes on Linear Algebra.

Math Camp Lecture 4: Linear Algebra. Xiao Yu Wang. Aug 2010 MIT. Xiao Yu Wang (MIT) Math Camp /10 1 / 88

Lecture 10 - Eigenvalues problem

Overview. Motivation for the inner product. Question. Definition

MTH 2032 Semester II

MATH 20F: LINEAR ALGEBRA LECTURE B00 (T. KEMP)

MATH 220 FINAL EXAMINATION December 13, Name ID # Section #

Pair of Linear Equations in Two Variables

Spiral Review Probability, Enter Your Grade Online Quiz - Probability Pascal's Triangle, Enter Your Grade

Algebra II. Paulius Drungilas and Jonas Jankauskas

Lecture 18: The Rank of a Matrix and Consistency of Linear Systems

Linear Equations in Two Variables

5 Systems of Equations

Chapter 5. Linear Algebra. Sections A linear (algebraic) equation in. unknowns, x 1, x 2,..., x n, is. an equation of the form

Let x be an approximate solution for Ax = b, e.g., obtained by Gaussian elimination. Let x denote the exact solution. Call. r := b A x.

B553 Lecture 5: Matrix Algebra Review

Transcription:

Lecture 1: Systems of linear equations and their solutions

Course overview Topics to be covered this semester: Systems of linear equations and Gaussian elimination: Solving linear equations and applications Matrices: Arithmetic of matrices, trace and determinant of matrices Eigenvalues, eigenvectors, diagonalization and applications Orthogonality in Euclidean space Subspaces of Euclidean space, spanning sets, linearly independent sets, bases Vector spaces Subspaces of vector spaces, spanning sets and linearly independent sets in abstract vector spaces

Something familiar: Two equations in two unknowns Given the system of equations 2x + 6y = 8 6x 2y = 4 We have a couple of ways to solve this system. 1. We may solve for one variable in terms of the other variable. For example, from the first equation, we have 2x = 8 6y, which yields x = 4 3y. We then substitute the expression for x into the second equation to get: 6(4 3y) 2y = 4, thus, 24 18y 2y = 4, so 20y = 20, and thus y = 1. So: x = 4 3 1 = 1 and therefore, x = 1, y = 1 is the unique solution to the given system.

2. We may also solve the system by eliminating a variable. For example, if we multiply the first equation by 3, we obtain 6x + 18y = 24 6x 2y = 4 If we then subtract the second equation from the first (thereby eliminating x), we get 20y = 20, so y = 1. Substituting this into the (original) first equation yields 2x + 6 1 = 8, so 2x = 2, and thus x = 1, as expected.

Class Example Find the unique solution to: 3x 9y = 9 6x + y = 37 Multiply the first equation by 2, 6x 18y = 18 6x + y = 37 Subtracting the second equation from the first, we get: 19y = 19, and thus y = 1. Substituting y = 1 into the first equation yields 3x 9 1 = 9, so 3x = 18, and x = 6. Therefore, x = 6, y = 1 is the unique solution. We also say the ordered pair (6, 1) is a solution.

Geometric Interpretation Recall that the graph of the equation ax + by = c is a straight line in the plane. Note: If b 0, we get the slope-intercept form y = a b x + c b. Thus, (u, v) is a solution to ax + by = c if and only if (u, v) lies on the corresponding line. Thus, given two equations, ax + by = c and dx + ey = f, (u, v) is a solution to both equations if and only if lie lies on both lines. Geometrically, there are three possibilities: 1 The lines intersect in one point 2 The lines are parallel 3 The two lines are the same

Consequences of Geometric Interpretation It follows that a given system of equations ax + by = c dx + ey = f has either 1 A unique solution (when the two lines intersect in a point) or 2 No solution (when the lines are parallel) or 3 Infinitely many solutions (when the two lines are the same) Thus, there can never be just finitely many solutions! For example, no system of two linear equations in two unknowns has 17 solutions.

In case 3 above, the system of two equations reduces to just one equation, say ax + by = c. Suppose a 0. Then we solve the equation for x to obtain x = ( b/a)y + c/a. To write the general solution, we introduce a new parameter, t, and say the solutions are for all real numbers t. y = t and x = ( b/a)t + c/a, Alternately, we say the solutions are all ordered pairs ( a b t + c b, t), with t any real number.

Class Example Write the solutions to 2x + 6y = 10 in parametric form using the parameter t. Solution: Solving for x in terms of y, we get: x = 5 3y. Thus the solutions are: x = 5 3t and y = t, for all real numbers t or, {(5 3t, t) t R}. Or, solving for you in terms of x, yields y = 5 3 1 3x, so the solutions are: {(s, 5 3 1 s) s R}. 3 Note: we can use any parameter we like.

Linear Equations A linear equation is an equation involving variables and coefficients, but no products or powers of variables. Some examples: (a) 2x + 3y = 6 (b) 7u 8v + 2y + πz = 17 (c) 75x 1 + 2 19 x 2 + 23x 3 = 3 π General linear equation: a 1 x 1 + a 2 x 2 + + a n x n = b ( ), where a 1,..., a n, b are real numbers.

Solutions: A solution to a linear equation is an ordered tuple that yields a valid equation upon substituting for the variables. So for example, if (s 1,..., s n ) is an n-tuple of real numbers then (s 1,..., s n ) is a solution to the equation (*) if and only if a 1 s 1 + + a n s n = b. We also say that x 1 = s 1,..., x n = s n is a solution to (*).

Example Consider the equation x 1 2x 2 3x 3 = 17.. The following are solutions to this equation: (22, 1, 1), is a solution since: 22 2 1 3 1 = 17. (25, -2, 4) is a solution since: 25 2( 2) 3 4 = 17. The general solution is (17+2s+3t, s, t), where s, t are arbitrary real numbers since: (17 + 2s + 3t) 2 s 3 t = 17.

Class Example Find two particular solutions to the equation: 2x 1 4x 2 + 8x 3 = 6. (1,1,1) and (3,0,0) are two among the infinitely many solutions.to find the general solution: Solve for x 1 : 2x 1 = 6 + 4x 2 8x 3, so x 1 = 3 + 2x 2 4x 3. Using parameters s, t, we have that the general solution is (3 + 2s 4t, s, t). Taking s = 1, t = 1, we get the first solution (1,1,1). Taking s = 0, t = 0, we get the second solutions (3,0,0).

Systems of Linear Equations A system of m linear equations in n unknowns is a system of equations of the form a 11 x 1 + a 12 x 2 + + a 1n x n = b 1 a 21 x 1 + a 22 x 2 + + a 2n x n = b 2. =. a m1 x 1 + a m2 x 2 + + a mn x n = b m where all of the coefficients a ij and all of the b k are real numbers. An ordered n-tuple (s 1,..., s n ), or equivalently, the set of real numbers x 1 = s 1,..., x n = s n, is a solution to the system of equations if it is a solution to each equation in the system.

Example Consider the system of equations 2x + 0y + 4z + 6w = 12 0x + 3y 6z + 9w = 15. (6, 5, 0, 0) is a solution since, setting x = 6, y = 5, z = 0, w = 0 yields 2 6 + 0 5 + 4 0 + 6 0 = 12 0 6 + 3 5 6 0 + 9 0 = 15.

Class Example Verify that, for all s, t R, (6 2s 3t, 5 + 2s 3t, s, t) is a solution to the system 2x + 0y + 4z + 6w = 12 0x + 3y 6z + 9w = 15. Solution: Substituing x = 6 2s 3t, y = 5 + 2s 3t, z = s, w = t into the first equation yields 2(6 2s 3t)+0(5+2s 3t)+4s +5t = 12 4s 6t +0+4s +6t = 12, while the same substitution into the second equation yields, 0(6 2s 3t) + 3(5 + 2s 3t) 6s + 9t = 15 + 6s 9t 6s + 9t = 15, which is what we want.