Linear Independence. Linear Algebra MATH Linear Algebra LI or LD Chapter 1, Section 7 1 / 1

Similar documents
Linear Combination. v = a 1 v 1 + a 2 v a k v k

Linear Equations in Linear Algebra

MATH 152 Exam 1-Solutions 135 pts. Write your answers on separate paper. You do not need to copy the questions. Show your work!!!

The scope of the midterm exam is up to and includes Section 2.1 in the textbook (homework sets 1-4). Below we highlight some of the important items.

if b is a linear combination of u, v, w, i.e., if we can find scalars r, s, t so that ru + sv + tw = 0.

Row Space, Column Space, and Nullspace

Math 205, Summer I, Week 3a (continued): Chapter 4, Sections 5 and 6. Week 3b. Chapter 4, [Sections 7], 8 and 9

Linear Algebra Exam 1 Spring 2007

6.4 BASIS AND DIMENSION (Review) DEF 1 Vectors v 1, v 2,, v k in a vector space V are said to form a basis for V if. (a) v 1,, v k span V and

MTH 306 Spring Term 2007

Math 2331 Linear Algebra

MATH 1120 (LINEAR ALGEBRA 1), FINAL EXAM FALL 2011 SOLUTIONS TO PRACTICE VERSION

DEPARTMENT OF MATHEMATICS

Math 301 Test I. M. Randall Holmes. September 8, 2008

Linear Equation: a 1 x 1 + a 2 x a n x n = b. x 1, x 2,..., x n : variables or unknowns

Math 550 Notes. Chapter 2. Jesse Crawford. Department of Mathematics Tarleton State University. Fall 2010

Dr. Abdulla Eid. Section 4.2 Subspaces. Dr. Abdulla Eid. MATHS 211: Linear Algebra. College of Science

MATH240: Linear Algebra Exam #1 solutions 6/12/2015 Page 1

Math 21b: Linear Algebra Spring 2018

Carleton College, winter 2013 Math 232, Solutions to review problems and practice midterm 2 Prof. Jones 15. T 17. F 38. T 21. F 26. T 22. T 27.

Solution: By inspection, the standard matrix of T is: A = Where, Ae 1 = 3. , and Ae 3 = 4. , Ae 2 =

Elements of linear algebra

5) homogeneous and nonhomogeneous systems 5a) The homogeneous matrix equation, A x = 0 is always consistent.

( v 1 + v 2 ) + (3 v 1 ) = 4 v 1 + v 2. and ( 2 v 2 ) + ( v 1 + v 3 ) = v 1 2 v 2 + v 3, for instance.

MATH 2331 Linear Algebra. Section 1.1 Systems of Linear Equations. Finding the solution to a set of two equations in two variables: Example 1: Solve:

Exercise 2) Consider the two vectors v 1. = 1, 0, 2 T, v 2

Lecture 6: Spanning Set & Linear Independency

Solutions to Homework 5 - Math 3410

Math 314H EXAM I. 1. (28 points) The row reduced echelon form of the augmented matrix for the system. is the matrix

Chapter 1. Vectors, Matrices, and Linear Spaces

1111: Linear Algebra I

Vector Spaces and SubSpaces

Linear Algebra Quiz 4. Problem 1 (Linear Transformations): 4 POINTS Show all Work! Consider the tranformation T : R 3 R 3 given by:

Lecture 18: Section 4.3

Math 544, Exam 2 Information.

Math 2030 Assignment 5 Solutions

is Use at most six elementary row operations. (Partial

Linear Equations in Linear Algebra

Lecture 9: Vector Algebra

Math 54 HW 4 solutions

1. TRUE or FALSE. 2. Find the complete solution set to the system:

Linear Algebra Math 221

Final Exam Practice Problems Answers Math 24 Winter 2012

Rank and Nullity. MATH 322, Linear Algebra I. J. Robert Buchanan. Spring Department of Mathematics

MATH 2331 Linear Algebra. Section 2.1 Matrix Operations. Definition: A : m n, B : n p. Example: Compute AB, if possible.

Lecture 03. Math 22 Summer 2017 Section 2 June 26, 2017

Sept. 3, 2013 Math 3312 sec 003 Fall 2013

The definition of a vector space (V, +, )

MATH 2050 Assignment 6 Fall 2018 Due: Thursday, November 1. x + y + 2z = 2 x + y + z = c 4x + 2z = 2

Study Guide for Linear Algebra Exam 2

Practice Exam. 2x 1 + 4x 2 + 2x 3 = 4 x 1 + 2x 2 + 3x 3 = 1 2x 1 + 3x 2 + 4x 3 = 5

Linear Algebra MATH20F Midterm 1

MATH 323 Linear Algebra Lecture 12: Basis of a vector space (continued). Rank and nullity of a matrix.

Math 2114 Common Final Exam May 13, 2015 Form A

Math 3191 Applied Linear Algebra

Linear Independence x

1 Invariant subspaces

Math 2331 Linear Algebra

Math 4377/6308 Advanced Linear Algebra

Rank, Homogenous Systems, Linear Independence

Math 415 Exam I. Name: Student ID: Calculators, books and notes are not allowed!

Vector space and subspace

Dependence and independence

Name: Final Exam MATH 3320

Kevin James. MTHSC 3110 Section 4.3 Linear Independence in Vector Sp

6. The scalar multiple of u by c, denoted by c u is (also) in V. (closure under scalar multiplication)

Linear Algebra- Final Exam Review

Midterm 1 Review. Written by Victoria Kala SH 6432u Office Hours: R 12:30 1:30 pm Last updated 10/10/2015

MA 242 LINEAR ALGEBRA C1, Solutions to First Midterm Exam

Math 221 Midterm Fall 2017 Section 104 Dijana Kreso

Fall 2016 MATH*1160 Final Exam

Span & Linear Independence (Pop Quiz)

Chapter 1 Vector Spaces

Tues Feb Vector spaces and subspaces. Announcements: Warm-up Exercise:

Math 3191 Applied Linear Algebra

VECTORS [PARTS OF 1.3] 5-1

which are not all zero. The proof in the case where some vector other than combination of the other vectors in S is similar.

Summer Session Practice Final Exam

1. Determine by inspection which of the following sets of vectors is linearly independent. 3 3.

Solutions to Math 51 First Exam April 21, 2011

5.) For each of the given sets of vectors, determine whether or not the set spans R 3. Give reasons for your answers.

Math 3191 Applied Linear Algebra

MATH 3321 Sample Questions for Exam 3. 3y y, C = Perform the indicated operations, if possible: (a) AC (b) AB (c) B + AC (d) CBA

Spring 2015 Midterm 1 03/04/15 Lecturer: Jesse Gell-Redman

Spring 2014 Midterm 1 02/26/14 Lecturer: Jesus Martinez Garcia

Linear Equations in Linear Algebra

MATH 300, Second Exam REVIEW SOLUTIONS. NOTE: You may use a calculator for this exam- You only need something that will perform basic arithmetic.

SYMBOL EXPLANATION EXAMPLE

DEPARTMENT OF MATHEMATICS

MATH 1553, SPRING 2018 SAMPLE MIDTERM 1: THROUGH SECTION 1.5

MATH10212 Linear Algebra B Homework Week 4

Family Feud Review. Linear Algebra. October 22, 2013

MATH 1553 PRACTICE FINAL EXAMINATION

Linear Algebra Practice Problems

Math 2174: Practice Midterm 1

Review Solutions for Exam 1

Announcements August 31

Math 3A Winter 2016 Midterm

Vector Spaces. (1) Every vector space V has a zero vector 0 V

MATH 304 Linear Algebra Lecture 10: Linear independence. Wronskian.

Transcription:

Linear Independence Linear Algebra MATH 76 Linear Algebra LI or LD Chapter, Section 7 /

Linear Combinations and Span Suppose s, s,..., s p are scalars and v, v,..., v p are vectors (all in the same space R n ). We call s v + s v + + s p v p a linear combination of the vectors v, v,..., v p. We always have the trivial linear combination v + v + + v p =. Here we want to know when there is a non-trivial LC of v, v,..., v p that equals. This means that s v + s v + + s p v p = and some scalar s j. Linear Algebra LI or LD Chapter, Section 7 /

The span of v, v,..., v p Span{ v, v,..., v p } = { w w = s v + s v + + s p v p, s j scalars } The span of a single vector is a line. Except when this is not true. The span of a two vectors is: a line if the two vectors are parallel a plane if the two vectors are not parallel except when this is not true What is the span of 7 vectors? It depends.... Linear Algebra LI or LD Chapter, Section 7 /

Linear Dependence The vectors v, v,..., v p are linearly dependent if there is a non-trivial LC of them that equals : that is, if there are scalars s, s,..., s p so that s v + s v + + s p v p =, and (at least) one of the scalars is non-zero. Linearly dependent vectors carry redundant information. Vectors that are not LD are said to be linearly independent. Linearly independent vectors carry NO redundant information. Linear Algebra LI or LD Chapter, Section 7 4 /

Example three vectors in R Are the vectors 4 LD or LI? Notice that + = 4. Thus + 4 =. So the three vectors are LD. This means that Span{ 4 } = Span{ } which is a plane in R. Linear Algebra LI or LD Chapter, Section 7 5 /

Example three vectors in R Are the vectors 7 LD or LI? Well, + 5 = 7, so + 5 7 =. Therefore, the three vectors are LD. This means that Span{ 7 } = Span{ } which is a plane in R. Linear Algebra LI or LD Chapter, Section 7 6 /

Example three vectors in R Are the vectors LD or LI? Since + =, we get + =. So the three vectors are LD. This means that Span{ } = Span{ } which is a plane in R. Linear Algebra LI or LD Chapter, Section 7 7 /

Example three vectors in R 4 Are the vectors v = v = v = 6 LD or LI? Easy to see that v = v v, so v v v = and therefore v, v, v are LD. Also, Span { } { } v, v, v = Span v, v which is a -plane in R 4. But what if we had five (or fifteen) vectors? Let A = [ ] v v v be the matrix with columns given by v, v, v. Recall that A x is the LC of the columns of A given by x A x = A x = x v + x v + x v. x See that there is a non-trivial LC x v + x v + x v = if and only if there is a non-trivial (i.e., non-zero) solution to A x =. Linear Algebra LI or LD Chapter, Section 7 8 /

Example three vectors in R 4 (continued) We row reduce A to get 6 R R R 4 R R + R R 4 R Since the last column has no row leader, we know that x is a free variable. Thus x = t (with t any scalar), x = t and x = t. There are infinitely many solutions to A x =, and all but one of these is non-trivial. Thus, there are infinitely many non-trival LCs s v + s v + s v =. Linear Algebra LI or LD Chapter, Section 7 9 /

Linearly Dependent Vectors Suppose the vectors v, w are LD. This means there are scalars s, t such that s v + t w = and s, t are NOT both zero. Assume s. Then we can write v = (t/s) w. Thus v is a scalar multiple of w; i.e., v is a LC of w. Suppose v is a LC of v, v,..., v p. This means there are scalars s, s,..., s p so that v = s v + s v + + s p v p. Then v ( s v + s v + + s p v p ) =. so we see that v, v, v,..., v p are LD. If some v j is an LC of the other vectors, then v, v, v,..., v p are LD. Converse true too! Linear Algebra LI or LD Chapter, Section 7 /

A useful Fact too many vectors are always LD If p > n, then any set of p vectors in R n is LD. Any 5 vectors in R 4 are LD. Any vectors in R 7 are LD. Why? Just look at REF of appropriate coefficient matrix! Linear Algebra LI or LD Chapter, Section 7 /

Parallel Vectors Two vectors are parallel if and only if one is a scalar multiple of the other. Any two non-zero vectors are LI if and only if they are not parallel. Why? (a good quiz question!) Suppose we have three vectors v, v, v (say, in R 5 ) and v v v v. Are v, v, v LD or LI? Hint: it is not difficult to produce a simple example! (a good exam question ) Linear Algebra LI or LD Chapter, Section 7 /