a s 1.3 Matrix Multiplication. Know how to multiply two matrices and be able to write down the formula
|
|
- Phillip Campbell
- 5 years ago
- Views:
Transcription
1 Syllabus for Math 308, Paul Smith Book: Kolman-Hill Chapter 1. Linear Equations and Matrices 1.1 Systems of Linear Equations Definition of a linear equation and a solution to a linear equations. Meaning of the coefficient of x i. Definition of an m n system of linear equations (m linear equations in n unknowns) and a solution to a system of linear equations. Definitions of consistent and inconsistent systems, homogeneous systems, trivial and non-trivial solutions. Manipulating a system of linear equations. Method of elimination. Important summary of those methods on page 6. Definition: two systems of m linear equations in n unknowns are equivalent if they have the same solutions. The trichotomy: no solution; a unique solution; infinitely many solutions. The idea behind this trichotomy: if p and q are different solutions to the same system all the points on the line through p and q, which we denote pq, are solutions to that system: λp + (1 λ)q, λ R. Symbols N Z Q R C. Set notation as in the notes posted on my 308 web page. 1.2 Matrices Definition, m n matrix has m rows and n columns. Standard labeling for the entries: a ij is in row i and column j. Definition of equality of matrices. Square matrices, their diagonals. Column and row vectors. Definition of R n as all n 1 vectors/matrices. Or, sometimes simpler to define R n as all 1 n row vectors. Similarly for C n, row and column vectors whose entries are complex numbers. Addition of matrices (same size), associative, commutative. Multiplication of a matrix by a number, or scalar, c R, c(a ij ) = (ca ij ). Define A = ( 1)A. Meaning of A B. Definition of linear combination of columns. Coefficients. Summation notation n n n m n n m a i = a j = and a ij = a ij. i=1 j=1 s=1 a s i=1 j=1 j=1 i=1 1.3 Matrix Multiplication. Know how to multiply two matrices and be able to write down the formula n A ij = A ik B kj. k=1 Use this to prove the associative law (AB)C = A(BC). And to prove (AB) T = B T A T. Let A be an m n matrix and B a p q-matrix. Can form the product AB if and only if n = p. Know AB need not equal BA; have a few such examples at your fingertips. Can have AB = 0 and BA 0; have a few 1
2 2 such examples at your fingertips. In particular, the product of non-zero matrices can be zero. Know the very important formula Ax = x 1 A x n A n (1) where A i is the i th column of A. Know how to go back and forth between a system of linear equations and a single matrix equation Ax = b and how to form of the augmented matrix (A b) and its meaning. Know why (1) implies Ax = b if and only if b is a linear combination of the columns of A. 1.4 Algebraic Properties of Matrix Operations. You must know everything in this section. Relation between the dot product and matrix multiplication. 1.5 Special Types of Matrices and Partitioned Matrices. You must know the meaning of A r for all integers r. Meaning of diagonal matrix, the different identity matrices, and zero matrices, the meaning of symmetric and skew-symmetric matrices. The only partitioned matrix we care about is the augmented matrix. Definition of the inverse of a matrix (if it exists) and how to prove it is unique if it exists. Relation among inverses of various matrices, e.g., A 1 B 1 = (BA) 1 ; (A T ) 1 = (A 1 ) T. You should figure how to prove these proofs in book if needed. If A is invertible the unique solution to Ax = b is A 1 b. Why? On page 46 the book gives a different definition of a (non- )singular matrix than the one we are using in class. Use the definition I gave: a square matrix A is non-singular if and only if the only solution to Ax = 0 is x = 0. Because we use a different definition we must prove that a matrix is non-singular if and only if it has an inverse. The book doesn t need to do that because that is how they define non-singularity! 1.6 Matrix Transformations. VERY IMPORTANT to know that an m n matrix gives a linear transformation f : R n R m by the formula f(x) = Ax. Meaning of the words image and range. Special matrix transformations rotations about the origin in the plane; reflection with respect to the x- and y-axes; projection R 3 R 2 ; contraction and dilation 1.7 Computer Graphics. We won t cover this in class and it won t be on the exam but read it as part of your general education about matrices. 1.8 Correlation coefficients. We won t cover this topic but we will use some ideas introduced in this section. You need to know the formula for the length of a vector, and the cosine of the angle between two non-zero vectors; the criterion for two vectors to be perpendicular (or orthogonal): if u and v are non-zero vectors u is orthogonal to v if u v = 0;
3 3 1.? Odds and ends. You must know the meaning of a statement like P if and only if Q: two statements, P and Q, are involved and the proof will have two separate parts, one showing if P is true, then Q is true, the other showing if Q is true, then P is true. You must know that the following statements are equivalent: P implies Q; if P, then Q; if Q is false so is P ; if P is true so is Q. Chapter 2. Solving Linear Systems. 2.1 Echelon Form of a Matrix Definition of row echelon form and row reduced echelon form (rref). You must be able to state these cleanly and with 100% accuracy. Even the slightest inaccuracy will be penalized see answers to quiz 2. We will not make use of column echelon form and column reduced echelon form but you should know of their existence and that they are completely analogous to row echelon form and row reduced echelon form. You must know the 3 elementary row operations (EROs) and be able to state them cleanly and with 100% accuracy. If you can get from A to B by a sequence of EROs you can get from B to A by a sequence of EROs. What it means for two m n matrices to be row equivalent the book delays that definition until section 2.4. (There is a notion of column equivalent but we will not make use of it.) Theorem: Two matrices are row equivalent if and only if each can be obtained from the other by an ERO. Theorem: Every matrix is row equivalent to one in echelon form, and to a unique one in row reduced echelon form. We will not use the words pivot and pivot column 2.2 Solving Linear Systems Understand how to write down the solutions to Ax = b when (A b) is in row echelon form Suppose (A b) is equivalent to (E c) where E is in RREF. How to recognize from (E c) whether Ax = b is inconsistent. Definition of dependent and independent variables: x j is dependent if column j of E contains a leading 1. All other variables are independent. Independent variables can take on any value; once the independent variables are given particular values the values of the dependent variables are completely determined. Know how to write down all solutions to Ax = b by using (E c). 2.3 Elementary Matrices: Finding A 1 My treatment of inverses differs from that in the book. I do not make use of elementary matrices. The idea behind elementary matrices is quite simple. There are three types of elementary matrices. The book calls them Type I, Type II, Type III. Multiplying a matrix A on the left by one
4 4 of these elementary matrices, E say, produces a matrix EA that can also be obtained from A by an elementary row operation, and vice versa; if B is obtained from A by performing a single ERO there is an elementary matrix E such that EA = B. For example, consider the ERO swap row i with row j ; perform that operation on the m m identity matrix to produce the matrix E; then E is an elementary matrix; the matrix EA can be obtained by swapping rows i and j of A. Similarly for the other two EROs. Thus the result of performing a sequence of EROs on A produces a matrix B that is equal to E n E n 1 E 1 A where each E i is an elementary matrix (see Thm. 2.6). Using elementary matrices the book proves some of the same results we proved about invertible matrices. Lemma 2.1in the book is important: a matrix is non-singular if and only if it is row equivalent to the identity matrix.that is the content of Lemma 2.1 in simpler terms. They state that result as Corollary 2.2, but the proof of my version of Lemma 2.1 is simpler than their s. The blue box on page 120 is important. We have proved that in class, with the exception of (5). It is important to know how to find A 1 when it exists. Notice that the book uses exactly the same method as the one I showed you in class see their Example 4 on page 121. We also proved Theorem 2.11 as part of our proof that a nonsingular matrix is invertible. 2.4 Equivalent Matrices. I have already discussed some of this material in section 2.1 above. We have not and will not prove Theorem 2.12 but you will learn something useful by reading the proof carefully. Theorem 2.14 is important. It can be proved without using elementary matrices. (To do that combine Prop. 5.4, Thm. 5.5, and Thm. 9.7 in my notes.) 2.5 LU-Factorization (Optional) We will skip this section. Chapter 3. Determinants 3.1 Definition In class we define the determinant in a different way from the book. Consequently most of section 3.1 can be skipped (or postponed). But do read from Example 6 to the end of the section. And some of the exercises are worth doing. 3.2 Properties of Determinants Know the statements of all Theorems in this section, though you can skip the proofs. Read the examples. 3.3 Cofactor Expansion Read section 3.3 carefully. We use the cofactor expansion to define the determinant inductively, i.e., first we define it for a 2 2 matrix; then we assume we have a formula for the determinant of an (n 1) (n 1) matrix and define the determinant of an n n matrix in terms of the formula for an (n 1) (n 1) matrix. The precise formula
5 we use is given in the first part of Theorem 3.10 with i = 1 and a slight change to the meaning of A ij. For information about the sign see Defn Compare the formula at the bottom of page 158 with the formula we gave in class for a 3 3 matrix. Now look at Example 2. The material about computing areas will not be something I expect you to know, but it would be good to read through it as part of your education. 3.4 Inverse of a Matrix We will cover this section in detail. We have already seen one way to compute the inverse: form the augmented matrix (A I) then perform EROs to get this in the form (I B) (if A does not have an inverse this will not be possible). The matrix B is A 1. This section gives another way to compute the inverse of A by computing the determinants of its minors (see Defn. 3.3, p. 157, for the defn. of minor. 3.5 Other Applications of Determinants The idea in this section is simple: if A has an inverse, the equation Ax = b always has a unique solution, namely x = A 1 b. This is about as obvious as saying that the equation 3x = 7 has a unique solution, namely x = That is all there is to Cramer s Rule. The point is that even if we know A has an inverse we still have to compute it if we want to write down an explicit solution. Cramer s Rule just does that using the formula for A 1 given in sect Determinants from a Computational Point of View Chapter 4. Real Vector Spaces. 4.1 Vectors in the Plane and in 3-space 4.2 Vector Spaces 4.3 Subspaces 4.4 Span 4.5 Linear Independence 4.6 Basis and Dimension 4.7 Homogeneous Systems 4.8 Coordinates and Isomorphisms 4.9 Rank of a Matrix Chapter 5. Inner Product Spaces. 5.1 Standard Inner Product on R2 and R3 5.2 Cross Product in R3 (Optional) 5.3 Inner Product Spaces. 5.4 Gram-Schmidt Process. Orthogonal Complements. 5.5 Least Squares (Optional). Chapter 6. Linear Transformations and Matrices. 6.1 Definition and Examples. 6.2 Kernel and Range of a Linear Transformation. 6.3 Matrix of a Linear Transformation. 5
6 6 6.4 Vector Space of Matrices and Vector Space of Linear Transformations (Optional). 6.5 Similarity. 6.6 Inroduction to Homogeneous Coordinates (Optional). Chapter 7. Eigenvalues and Eigenvectors. 7.1 Eigenvalues and Eigenvectors. 7.2 Diagonalization and Similar Matrices. 7.3 Diagonalization of symmetric matrices Appendices. You should also know the material in the appendices.
Linear Algebra: Lecture notes from Kolman and Hill 9th edition.
Linear Algebra: Lecture notes from Kolman and Hill 9th edition Taylan Şengül March 20, 2019 Please let me know of any mistakes in these notes Contents Week 1 1 11 Systems of Linear Equations 1 12 Matrices
More informationMAT Linear Algebra Collection of sample exams
MAT 342 - Linear Algebra Collection of sample exams A-x. (0 pts Give the precise definition of the row echelon form. 2. ( 0 pts After performing row reductions on the augmented matrix for a certain system
More informationReduction to the associated homogeneous system via a particular solution
June PURDUE UNIVERSITY Study Guide for the Credit Exam in (MA 5) Linear Algebra This study guide describes briefly the course materials to be covered in MA 5. In order to be qualified for the credit, one
More information3.4 Elementary Matrices and Matrix Inverse
Math 220: Summer 2015 3.4 Elementary Matrices and Matrix Inverse A n n elementary matrix is a matrix which is obtained from the n n identity matrix I n n by a single elementary row operation. Elementary
More informationANSWERS. E k E 2 E 1 A = B
MATH 7- Final Exam Spring ANSWERS Essay Questions points Define an Elementary Matrix Display the fundamental matrix multiply equation which summarizes a sequence of swap, combination and multiply operations,
More informationTopic 15 Notes Jeremy Orloff
Topic 5 Notes Jeremy Orloff 5 Transpose, Inverse, Determinant 5. Goals. Know the definition and be able to compute the inverse of any square matrix using row operations. 2. Know the properties of inverses.
More informationIMPORTANT DEFINITIONS AND THEOREMS REFERENCE SHEET
IMPORTANT DEFINITIONS AND THEOREMS REFERENCE SHEET This is a (not quite comprehensive) list of definitions and theorems given in Math 1553. Pay particular attention to the ones in red. Study Tip For each
More informationConceptual Questions for Review
Conceptual Questions for Review Chapter 1 1.1 Which vectors are linear combinations of v = (3, 1) and w = (4, 3)? 1.2 Compare the dot product of v = (3, 1) and w = (4, 3) to the product of their lengths.
More informationMATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 1 x 2. x n 8 (4) 3 4 2
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS SYSTEMS OF EQUATIONS AND MATRICES Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a
More informationThe 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.
Math 410 Homework Problems In the following pages you will find all of the homework problems for the semester. Homework should be written out neatly and stapled and turned in at the beginning of class
More informationMATH 240 Spring, Chapter 1: Linear Equations and Matrices
MATH 240 Spring, 2006 Chapter Summaries for Kolman / Hill, Elementary Linear Algebra, 8th Ed. Sections 1.1 1.6, 2.1 2.2, 3.2 3.8, 4.3 4.5, 5.1 5.3, 5.5, 6.1 6.5, 7.1 7.2, 7.4 DEFINITIONS Chapter 1: Linear
More information1. Let m 1 and n 1 be two natural numbers such that m > n. Which of the following is/are true?
. Let m and n be two natural numbers such that m > n. Which of the following is/are true? (i) A linear system of m equations in n variables is always consistent. (ii) A linear system of n equations in
More informationGlossary of Linear Algebra Terms. Prepared by Vince Zaccone For Campus Learning Assistance Services at UCSB
Glossary of Linear Algebra Terms Basis (for a subspace) A linearly independent set of vectors that spans the space Basic Variable A variable in a linear system that corresponds to a pivot column in the
More informationIMPORTANT DEFINITIONS AND THEOREMS REFERENCE SHEET
IMPORTANT DEFINITIONS AND THEOREMS REFERENCE SHEET This is a (not quite comprehensive) list of definitions and theorems given in Math 1553. Pay particular attention to the ones in red. Study Tip For each
More informationLecture Notes in Linear Algebra
Lecture Notes in Linear Algebra Dr. Abdullah Al-Azemi Mathematics Department Kuwait University February 4, 2017 Contents 1 Linear Equations and Matrices 1 1.2 Matrices............................................
More informationLinear Algebra Highlights
Linear Algebra Highlights Chapter 1 A linear equation in n variables is of the form a 1 x 1 + a 2 x 2 + + a n x n. We can have m equations in n variables, a system of linear equations, which we want to
More informationhomogeneous 71 hyperplane 10 hyperplane 34 hyperplane 69 identity map 171 identity map 186 identity map 206 identity matrix 110 identity matrix 45
address 12 adjoint matrix 118 alternating 112 alternating 203 angle 159 angle 33 angle 60 area 120 associative 180 augmented matrix 11 axes 5 Axiom of Choice 153 basis 178 basis 210 basis 74 basis test
More informationMTH Linear Algebra. Study Guide. Dr. Tony Yee Department of Mathematics and Information Technology The Hong Kong Institute of Education
MTH 3 Linear Algebra Study Guide Dr. Tony Yee Department of Mathematics and Information Technology The Hong Kong Institute of Education June 3, ii Contents Table of Contents iii Matrix Algebra. Real Life
More informationMATH 1120 (LINEAR ALGEBRA 1), FINAL EXAM FALL 2011 SOLUTIONS TO PRACTICE VERSION
MATH (LINEAR ALGEBRA ) FINAL EXAM FALL SOLUTIONS TO PRACTICE VERSION Problem (a) For each matrix below (i) find a basis for its column space (ii) find a basis for its row space (iii) determine whether
More informationLecture 1 Systems of Linear Equations and Matrices
Lecture 1 Systems of Linear Equations and Matrices Math 19620 Outline of Course Linear Equations and Matrices Linear Transformations, Inverses Bases, Linear Independence, Subspaces Abstract Vector Spaces
More informationLecture Summaries for Linear Algebra M51A
These lecture summaries may also be viewed online by clicking the L icon at the top right of any lecture screen. Lecture Summaries for Linear Algebra M51A refers to the section in the textbook. Lecture
More informationMath 520 Exam 2 Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008
Math 520 Exam 2 Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008 Exam 2 will be held on Tuesday, April 8, 7-8pm in 117 MacMillan What will be covered The exam will cover material from the lectures
More informationReview for Exam Find all a for which the following linear system has no solutions, one solution, and infinitely many solutions.
Review for Exam. Find all a for which the following linear system has no solutions, one solution, and infinitely many solutions. x + y z = 2 x + 2y + z = 3 x + y + (a 2 5)z = a 2 The augmented matrix for
More informationMAT188H1S LINEAR ALGEBRA: Course Information as of February 2, Calendar Description:
MAT188H1S LINEAR ALGEBRA: Course Information as of February 2, 2019 2018-2019 Calendar Description: This course covers systems of linear equations and Gaussian elimination, applications; vectors in R n,
More informationGAUSSIAN ELIMINATION AND LU DECOMPOSITION (SUPPLEMENT FOR MA511)
GAUSSIAN ELIMINATION AND LU DECOMPOSITION (SUPPLEMENT FOR MA511) D. ARAPURA Gaussian elimination is the go to method for all basic linear classes including this one. We go summarize the main ideas. 1.
More informationANSWERS (5 points) Let A be a 2 2 matrix such that A =. Compute A. 2
MATH 7- Final Exam Sample Problems Spring 7 ANSWERS ) ) ). 5 points) Let A be a matrix such that A =. Compute A. ) A = A ) = ) = ). 5 points) State ) the definition of norm, ) the Cauchy-Schwartz inequality
More information1 9/5 Matrices, vectors, and their applications
1 9/5 Matrices, vectors, and their applications Algebra: study of objects and operations on them. Linear algebra: object: matrices and vectors. operations: addition, multiplication etc. Algorithms/Geometric
More informationMath 102, Winter Final Exam Review. Chapter 1. Matrices and Gaussian Elimination
Math 0, Winter 07 Final Exam Review Chapter. Matrices and Gaussian Elimination { x + x =,. Different forms of a system of linear equations. Example: The x + 4x = 4. [ ] [ ] [ ] vector form (or the column
More information2. Every linear system with the same number of equations as unknowns has a unique solution.
1. For matrices A, B, C, A + B = A + C if and only if A = B. 2. Every linear system with the same number of equations as unknowns has a unique solution. 3. Every linear system with the same number of equations
More informationMath 344 Lecture # Linear Systems
Math 344 Lecture #12 2.7 Linear Systems Through a choice of bases S and T for finite dimensional vector spaces V (with dimension n) and W (with dimension m), a linear equation L(v) = w becomes the linear
More informationMAT 2037 LINEAR ALGEBRA I web:
MAT 237 LINEAR ALGEBRA I 2625 Dokuz Eylül University, Faculty of Science, Department of Mathematics web: Instructor: Engin Mermut http://kisideuedutr/enginmermut/ HOMEWORK 2 MATRIX ALGEBRA Textbook: Linear
More informationLinear Systems and Matrices
Department of Mathematics The Chinese University of Hong Kong 1 System of m linear equations in n unknowns (linear system) 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.......
More informationLinear Algebra Practice Problems
Math 7, Professor Ramras Linear Algebra Practice Problems () Consider the following system of linear equations in the variables x, y, and z, in which the constants a and b are real numbers. x y + z = a
More informationChapter 4 - MATRIX ALGEBRA. ... a 2j... a 2n. a i1 a i2... a ij... a in
Chapter 4 - MATRIX ALGEBRA 4.1. Matrix Operations A a 11 a 12... a 1j... a 1n a 21. a 22.... a 2j... a 2n. a i1 a i2... a ij... a in... a m1 a m2... a mj... a mn The entry in the ith row and the jth column
More informationMath 4A Notes. Written by Victoria Kala Last updated June 11, 2017
Math 4A Notes Written by Victoria Kala vtkala@math.ucsb.edu Last updated June 11, 2017 Systems of Linear Equations A linear equation is an equation that can be written in the form a 1 x 1 + a 2 x 2 +...
More informationMATRICES. knowledge on matrices Knowledge on matrix operations. Matrix as a tool of solving linear equations with two or three unknowns.
MATRICES After studying this chapter you will acquire the skills in knowledge on matrices Knowledge on matrix operations. Matrix as a tool of solving linear equations with two or three unknowns. List of
More informationMath Linear Algebra Final Exam Review Sheet
Math 15-1 Linear Algebra Final Exam Review Sheet Vector Operations Vector addition is a component-wise operation. Two vectors v and w may be added together as long as they contain the same number n of
More informationMath 18, Linear Algebra, Lecture C00, Spring 2017 Review and Practice Problems for Final Exam
Math 8, Linear Algebra, Lecture C, Spring 7 Review and Practice Problems for Final Exam. The augmentedmatrix of a linear system has been transformed by row operations into 5 4 8. Determine if the system
More informationMATH 2360 REVIEW PROBLEMS
MATH 2360 REVIEW PROBLEMS Problem 1: In (a) (d) below, either compute the matrix product or indicate why it does not exist: ( )( ) 1 2 2 1 (a) 0 1 1 2 ( ) 0 1 2 (b) 0 3 1 4 3 4 5 2 5 (c) 0 3 ) 1 4 ( 1
More informationEquality: Two matrices A and B are equal, i.e., A = B if A and B have the same order and the entries of A and B are the same.
Introduction Matrix Operations Matrix: An m n matrix A is an m-by-n array of scalars from a field (for example real numbers) of the form a a a n a a a n A a m a m a mn The order (or size) of A is m n (read
More informationMath 1553, Introduction to Linear Algebra
Learning goals articulate what students are expected to be able to do in a course that can be measured. This course has course-level learning goals that pertain to the entire course, and section-level
More information1 Determinants. 1.1 Determinant
1 Determinants [SB], Chapter 9, p.188-196. [SB], Chapter 26, p.719-739. Bellow w ll study the central question: which additional conditions must satisfy a quadratic matrix A to be invertible, that is to
More informationSUMMARY OF MATH 1600
SUMMARY OF MATH 1600 Note: The following list is intended as a study guide for the final exam. It is a continuation of the study guide for the midterm. It does not claim to be a comprehensive list. You
More informationThis MUST hold matrix multiplication satisfies the distributive property.
The columns of AB are combinations of the columns of A. The reason is that each column of AB equals A times the corresponding column of B. But that is a linear combination of the columns of A with coefficients
More informationMethods for Solving Linear Systems Part 2
Methods for Solving Linear Systems Part 2 We have studied the properties of matrices and found out that there are more ways that we can solve Linear Systems. In Section 7.3, we learned that we can use
More informationMATH 310, REVIEW SHEET 2
MATH 310, REVIEW SHEET 2 These notes are a very short summary of the key topics in the book (and follow the book pretty closely). You should be familiar with everything on here, but it s not comprehensive,
More informationPOLI270 - Linear Algebra
POLI7 - Linear Algebra Septemer 8th Basics a x + a x +... + a n x n b () is the linear form where a, b are parameters and x n are variables. For a given equation such as x +x you only need a variable and
More informationLinear Algebra March 16, 2019
Linear Algebra March 16, 2019 2 Contents 0.1 Notation................................ 4 1 Systems of linear equations, and matrices 5 1.1 Systems of linear equations..................... 5 1.2 Augmented
More informationMath 302 Outcome Statements Winter 2013
Math 302 Outcome Statements Winter 2013 1 Rectangular Space Coordinates; Vectors in the Three-Dimensional Space (a) Cartesian coordinates of a point (b) sphere (c) symmetry about a point, a line, and a
More informationLinear Algebra Done Wrong. Sergei Treil. Department of Mathematics, Brown University
Linear Algebra Done Wrong Sergei Treil Department of Mathematics, Brown University Copyright c Sergei Treil, 2004, 2009 Preface The title of the book sounds a bit mysterious. Why should anyone read this
More informationVectors and matrices: matrices (Version 2) This is a very brief summary of my lecture notes.
Vectors and matrices: matrices (Version 2) This is a very brief summary of my lecture notes Matrices and linear equations A matrix is an m-by-n array of numbers A = a 11 a 12 a 13 a 1n a 21 a 22 a 23 a
More informationChapter 5. Linear Algebra. A linear (algebraic) equation in. unknowns, x 1, x 2,..., x n, is. an equation of the form
Chapter 5. Linear Algebra A linear (algebraic) equation in n unknowns, x 1, x 2,..., x n, is an equation of the form a 1 x 1 + a 2 x 2 + + a n x n = b where a 1, a 2,..., a n and b are real numbers. 1
More information[Disclaimer: This is not a complete list of everything you need to know, just some of the topics that gave people difficulty.]
Math 43 Review Notes [Disclaimer: This is not a complete list of everything you need to know, just some of the topics that gave people difficulty Dot Product If v (v, v, v 3 and w (w, w, w 3, then the
More informationSpring 2014 Math 272 Final Exam Review Sheet
Spring 2014 Math 272 Final Exam Review Sheet You will not be allowed use of a calculator or any other device other than your pencil or pen and some scratch paper. Notes are also not allowed. In kindness
More informationLinear Algebra. Matrices Operations. Consider, for example, a system of equations such as x + 2y z + 4w = 0, 3x 4y + 2z 6w = 0, x 3y 2z + w = 0.
Matrices Operations Linear Algebra Consider, for example, a system of equations such as x + 2y z + 4w = 0, 3x 4y + 2z 6w = 0, x 3y 2z + w = 0 The rectangular array 1 2 1 4 3 4 2 6 1 3 2 1 in which the
More informationElementary Matrices. MATH 322, Linear Algebra I. J. Robert Buchanan. Spring Department of Mathematics
Elementary Matrices MATH 322, Linear Algebra I J. Robert Buchanan Department of Mathematics Spring 2015 Outline Today s discussion will focus on: elementary matrices and their properties, using elementary
More information1. What is the determinant of the following matrix? a 1 a 2 4a 3 2a 2 b 1 b 2 4b 3 2b c 1. = 4, then det
What is the determinant of the following matrix? 3 4 3 4 3 4 4 3 A 0 B 8 C 55 D 0 E 60 If det a a a 3 b b b 3 c c c 3 = 4, then det a a 4a 3 a b b 4b 3 b c c c 3 c = A 8 B 6 C 4 D E 3 Let A be an n n matrix
More informationLinear Algebra. Linear Equations and Matrices. Copyright 2005, W.R. Winfrey
Copyright 2005, W.R. Winfrey Topics Preliminaries Systems of Linear Equations Matrices Algebraic Properties of Matrix Operations Special Types of Matrices and Partitioned Matrices Matrix Transformations
More informationMath 321: Linear Algebra
Math 32: Linear Algebra T. Kapitula Department of Mathematics and Statistics University of New Mexico September 8, 24 Textbook: Linear Algebra,by J. Hefferon E-mail: kapitula@math.unm.edu Prof. Kapitula,
More informationLINEAR ALGEBRA REVIEW
LINEAR ALGEBRA REVIEW SPENCER BECKER-KAHN Basic Definitions Domain and Codomain. Let f : X Y be any function. This notation means that X is the domain of f and Y is the codomain of f. This means that for
More informationMTH 464: Computational Linear Algebra
MTH 464: Computational Linear Algebra Lecture Outlines Exam 2 Material Prof. M. Beauregard Department of Mathematics & Statistics Stephen F. Austin State University February 6, 2018 Linear Algebra (MTH
More informationMath113: Linear Algebra. Beifang Chen
Math3: Linear Algebra Beifang Chen Spring 26 Contents Systems of Linear Equations 3 Systems of Linear Equations 3 Linear Systems 3 2 Geometric Interpretation 3 3 Matrices of Linear Systems 4 4 Elementary
More informationMATH. 20F SAMPLE FINAL (WINTER 2010)
MATH. 20F SAMPLE FINAL (WINTER 2010) You have 3 hours for this exam. Please write legibly and show all working. No calculators are allowed. Write your name, ID number and your TA s name below. The total
More informationLinear Algebra Homework and Study Guide
Linear Algebra Homework and Study Guide Phil R. Smith, Ph.D. February 28, 20 Homework Problem Sets Organized by Learning Outcomes Test I: Systems of Linear Equations; Matrices Lesson. Give examples of
More informationDaily Update. Math 290: Elementary Linear Algebra Fall 2018
Daily Update Math 90: Elementary Linear Algebra Fall 08 Lecture 7: Tuesday, December 4 After reviewing the definitions of a linear transformation, and the kernel and range of a linear transformation, we
More informationis Use at most six elementary row operations. (Partial
MATH 235 SPRING 2 EXAM SOLUTIONS () (6 points) a) Show that the reduced row echelon form of the augmented matrix of the system x + + 2x 4 + x 5 = 3 x x 3 + x 4 + x 5 = 2 2x + 2x 3 2x 4 x 5 = 3 is. Use
More informationMATH 20F: LINEAR ALGEBRA LECTURE B00 (T. KEMP)
MATH 20F: LINEAR ALGEBRA LECTURE B00 (T KEMP) Definition 01 If T (x) = Ax is a linear transformation from R n to R m then Nul (T ) = {x R n : T (x) = 0} = Nul (A) Ran (T ) = {Ax R m : x R n } = {b R m
More informationChapter 1: Systems of linear equations and matrices. Section 1.1: Introduction to systems of linear equations
Chapter 1: Systems of linear equations and matrices Section 1.1: Introduction to systems of linear equations Definition: A linear equation in n variables can be expressed in the form a 1 x 1 + a 2 x 2
More informationMATH 308 LINEAR ALGEBRA NOTES
MATH 308 LINEAR ALGEBRA NOTES S. PAUL SMITH Contents 1. Introduction 2 2. Matrix arithmetic 2 2.1. Matrices 101 2 2.2. Row and column vectors 3 2.3. Addition and subtraction 4 2.4. The zero matrix and
More informationLinear Algebra. Min Yan
Linear Algebra Min Yan January 2, 2018 2 Contents 1 Vector Space 7 1.1 Definition................................. 7 1.1.1 Axioms of Vector Space..................... 7 1.1.2 Consequence of Axiom......................
More informationChapter 2: Matrices and Linear Systems
Chapter 2: Matrices and Linear Systems Paul Pearson Outline Matrices Linear systems Row operations Inverses Determinants Matrices Definition An m n matrix A = (a ij ) is a rectangular array of real numbers
More informationMATH 15a: Linear Algebra Practice Exam 2
MATH 5a: Linear Algebra Practice Exam 2 Write all answers in your exam booklet. Remember that you must show all work and justify your answers for credit. No calculators are allowed. Good luck!. Compute
More informationElementary Linear Algebra Review for Exam 2 Exam is Monday, November 16th.
Elementary Linear Algebra Review for Exam Exam is Monday, November 6th. The exam will cover sections:.4,..4, 5. 5., 7., the class notes on Markov Models. You must be able to do each of the following. Section.4
More informationW2 ) = dim(w 1 )+ dim(w 2 ) for any two finite dimensional subspaces W 1, W 2 of V.
MA322 Sathaye Final Preparations Spring 2017 The final MA 322 exams will be given as described in the course web site (following the Registrar s listing. You should check and verify that you do not have
More informationChapter 1. Vectors, Matrices, and Linear Spaces
1.4 Solving Systems of Linear Equations 1 Chapter 1. Vectors, Matrices, and Linear Spaces 1.4. Solving Systems of Linear Equations Note. We give an algorithm for solving a system of linear equations (called
More informationColumbus State Community College Mathematics Department Public Syllabus
Columbus State Community College Mathematics Department Public Syllabus Course and Number: MATH 2568 Elementary Linear Algebra Credits: 4 Class Hours Per Week: 4 Prerequisites: MATH 2153 with a C or higher
More informationLinear Algebra. Ben Woodruff. Compiled July 17, 2010
Linear Algebra Ben Woodruff Compiled July 7, i c This work is licensed under the Creative Commons Attribution-Share Alike 3. United States License. You may copy, distribute, display, and perform this copyrighted
More informationFinal Review Sheet. B = (1, 1 + 3x, 1 + x 2 ) then 2 + 3x + 6x 2
Final Review Sheet The final will cover Sections Chapters 1,2,3 and 4, as well as sections 5.1-5.4, 6.1-6.2 and 7.1-7.3 from chapters 5,6 and 7. This is essentially all material covered this term. Watch
More informationHomework Set #8 Solutions
Exercises.2 (p. 19) Homework Set #8 Solutions Assignment: Do #6, 8, 12, 14, 2, 24, 26, 29, 0, 2, 4, 5, 6, 9, 40, 42 6. Reducing the matrix to echelon form: 1 5 2 1 R2 R2 R1 1 5 0 18 12 2 1 R R 2R1 1 5
More informationFinal Exam Practice Problems Answers Math 24 Winter 2012
Final Exam Practice Problems Answers Math 4 Winter 0 () The Jordan product of two n n matrices is defined as A B = (AB + BA), where the products inside the parentheses are standard matrix product. Is the
More informationMatrix & Linear Algebra
Matrix & Linear Algebra Jamie Monogan University of Georgia For more information: http://monogan.myweb.uga.edu/teaching/mm/ Jamie Monogan (UGA) Matrix & Linear Algebra 1 / 84 Vectors Vectors Vector: A
More informationSolving a system by back-substitution, checking consistency of a system (no rows of the form
MATH 520 LEARNING OBJECTIVES SPRING 2017 BROWN UNIVERSITY SAMUEL S. WATSON Week 1 (23 Jan through 27 Jan) Definition of a system of linear equations, definition of a solution of a linear system, elementary
More informationQuizzes for Math 304
Quizzes for Math 304 QUIZ. A system of linear equations has augmented matrix 2 4 4 A = 2 0 2 4 3 5 2 a) Write down this system of equations; b) Find the reduced row-echelon form of A; c) What are the pivot
More informationProblem 1: Solving a linear equation
Math 38 Practice Final Exam ANSWERS Page Problem : Solving a linear equation Given matrix A = 2 2 3 7 4 and vector y = 5 8 9. (a) Solve Ax = y (if the equation is consistent) and write the general solution
More informationMath Camp II. Basic Linear Algebra. Yiqing Xu. Aug 26, 2014 MIT
Math Camp II Basic Linear Algebra Yiqing Xu MIT Aug 26, 2014 1 Solving Systems of Linear Equations 2 Vectors and Vector Spaces 3 Matrices 4 Least Squares Systems of Linear Equations Definition A linear
More informationMath Computation Test 1 September 26 th, 2016 Debate: Computation vs. Theory Whatever wins, it ll be Huuuge!
Math 5- Computation Test September 6 th, 6 Debate: Computation vs. Theory Whatever wins, it ll be Huuuge! Name: Answer Key: Making Math Great Again Be sure to show your work!. (8 points) Consider the following
More informationReview problems for MA 54, Fall 2004.
Review problems for MA 54, Fall 2004. Below are the review problems for the final. They are mostly homework problems, or very similar. If you are comfortable doing these problems, you should be fine on
More informationMODEL ANSWERS TO THE FIRST QUIZ. 1. (18pts) (i) Give the definition of a m n matrix. A m n matrix with entries in a field F is a function
MODEL ANSWERS TO THE FIRST QUIZ 1. (18pts) (i) Give the definition of a m n matrix. A m n matrix with entries in a field F is a function A: I J F, where I is the set of integers between 1 and m and J is
More informationElementary maths for GMT
Elementary maths for GMT Linear Algebra Part 2: Matrices, Elimination and Determinant m n matrices The system of m linear equations in n variables x 1, x 2,, x n a 11 x 1 + a 12 x 2 + + a 1n x n = b 1
More informationMATH 213 Linear Algebra and ODEs Spring 2015 Study Sheet for Midterm Exam. Topics
MATH 213 Linear Algebra and ODEs Spring 2015 Study Sheet for Midterm Exam This study sheet will not be allowed during the test Books and notes will not be allowed during the test Calculators and cell phones
More informationLinear Algebra Summary. Based on Linear Algebra and its applications by David C. Lay
Linear Algebra Summary Based on Linear Algebra and its applications by David C. Lay Preface The goal of this summary is to offer a complete overview of all theorems and definitions introduced in the chapters
More informationLINEAR ALGEBRA SUMMARY SHEET.
LINEAR ALGEBRA SUMMARY SHEET RADON ROSBOROUGH https://intuitiveexplanationscom/linear-algebra-summary-sheet/ This document is a concise collection of many of the important theorems of linear algebra, organized
More informationMATH 315 Linear Algebra Homework #1 Assigned: August 20, 2018
Homework #1 Assigned: August 20, 2018 Review the following subjects involving systems of equations and matrices from Calculus II. Linear systems of equations Converting systems to matrix form Pivot entry
More information1300 Linear Algebra and Vector Geometry
1300 Linear Algebra and Vector Geometry R. Craigen Office: MH 523 Email: craigenr@umanitoba.ca May-June 2017 Introduction: linear equations Read 1.1 (in the text that is!) Go to course, class webpages.
More informationIntroduction to Matrices
POLS 704 Introduction to Matrices Introduction to Matrices. The Cast of Characters A matrix is a rectangular array (i.e., a table) of numbers. For example, 2 3 X 4 5 6 (4 3) 7 8 9 0 0 0 Thismatrix,with4rowsand3columns,isoforder
More informationLinear Algebra Final Exam Study Guide Solutions Fall 2012
. Let A = Given that v = 7 7 67 5 75 78 Linear Algebra Final Exam Study Guide Solutions Fall 5 explain why it is not possible to diagonalize A. is an eigenvector for A and λ = is an eigenvalue for A diagonalize
More information1300 Linear Algebra and Vector Geometry
1300 Linear Algebra and Vector Geometry R. Craigen Office: MH 523 Email: craigenr@umanitoba.ca May-June 2017 Matrix Inversion Algorithm One payoff from this theorem: It gives us a way to invert matrices.
More informationMATH 260 LINEAR ALGEBRA EXAM II Fall 2013 Instructions: The use of built-in functions of your calculator, such as det( ) or RREF, is prohibited.
MAH 60 LINEAR ALGEBRA EXAM II Fall 0 Instructions: he use of built-in functions of your calculator, such as det( ) or RREF, is prohibited ) For the matrix find: a) M and C b) M 4 and C 4 ) Evaluate the
More informationLinear Algebra. and
Instructions Please answer the six problems on your own paper. These are essay questions: you should write in complete sentences. 1. Are the two matrices 1 2 2 1 3 5 2 7 and 1 1 1 4 4 2 5 5 2 row equivalent?
More informationOHSx XM511 Linear Algebra: Solutions to Online True/False Exercises
This document gives the solutions to all of the online exercises for OHSx XM511. The section ( ) numbers refer to the textbook. TYPE I are True/False. Answers are in square brackets [. Lecture 02 ( 1.1)
More information