On the Use of Vectors, Reference Frames, and Coordinate Systems in Aerospace Analysis
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MA 242 LINEAR ALGEBRA C1, Solutions to Second Midterm Exam
MA 242 LINEAR ALGEBRA C1, Solutions to Second Midterm Exam Prof. Nikola Popovic, November 9, 2006, 09:30am - 10:50am Problem 1 (15 points). Let the matrix A be given by 1 −2 −1 2 −1 5 6 3 : 5 −4 5 4 5 (a) Find the inverse A−1 of A, if it exists. (b) Based on your answer in (a), determine whether the columns of A span R3. (Justify your answer!) Solution. (a) To check whether A is invertible, we row reduce the augmented matrix [A I3]: 1 −2 −1 1 0 0 1 −2 −1 1 0 0 2 −1 5 6 0 1 0 3 ∼ : : : ∼ 2 0 3 5 1 1 0 3 : 5 −4 5 0 0 1 0 0 0 −7 −2 1 4 5 4 5 Since the last row in the echelon form of A contains only zeros, A is not row equivalent to I3. Hence, A is not invertible, and A−1 does not exist. (b) Since A is not invertible by (a), the Invertible Matrix Theorem says that the columns of A cannot span R3. Problem 2 (15 points). Let the vectors b1; : : : ; b4 be defined by 3 2 −1 0 0 5 1 0 −1 1 0 1 1 0 0 1 b1 = ; b2 = ; b3 = ; and b4 = : −2 −5 3 0 B C B C B C B C B 4 C B 7 C B 0 C B −3 C @ A @ A @ A @ A (a) Determine if the set B = fb1; b2; b3; b4g is linearly independent by computing the determi- nant of the matrix B = [b1 b2 b3 b4]. -
Final Exam Outline 1.1, 1.2 Scalars and Vectors in R N. the Magnitude
Final Exam Outline 1.1, 1.2 Scalars and vectors in Rn. The magnitude of a vector. Row and column vectors. Vector addition. Scalar multiplication. The zero vector. Vector subtraction. Distance in Rn. Unit vectors. Linear combinations. The dot product. The angle between two vectors. Orthogonality. The CBS and triangle inequalities. Theorem 1.5. 2.1, 2.2 Systems of linear equations. Consistent and inconsistent systems. The coefficient and augmented matrices. Elementary row operations. The echelon form of a matrix. Gaussian elimination. Equivalent matrices. The reduced row echelon form. Gauss-Jordan elimination. Leading and free variables. The rank of a matrix. The rank theorem. Homogeneous systems. Theorem 2.2. 2.3 The span of a set of vectors. Linear dependence. Linear dependence and independence. Determining n whether a set {~v1, . ,~vk} of vectors in R is linearly dependent or independent. 3.1, 3.2 Matrix operations. Scalar multiplication, matrix addition, matrix multiplication. The zero and identity matrices. Partitioned matrices. The outer product form. The standard unit vectors. The transpose of a matrix. A system of linear equations in matrix form. Algebraic properties of the transpose. 3.3 The inverse of a matrix. Invertible (nonsingular) matrices. The uniqueness of the solution to Ax = b when A is invertible. The inverse of a nonsingular 2 × 2 matrix. Elementary matrices and EROs. Equivalent conditions for invertibility. Using Gauss-Jordan elimination to invert a nonsingular matrix. Theorems 3.7, 3.9. n n n 3.5 Subspaces of R . {0} and R as subspaces of R . The subspace span(v1,..., vk). The the row, column and null spaces of a matrix. -
Introduction to MATLAB®
Introduction to MATLAB® Lecture 2: Vectors and Matrices Introduction to MATLAB® Lecture 2: Vectors and Matrices 1 / 25 Vectors and Matrices Vectors and Matrices Vectors and matrices are used to store values of the same type A vector can be either column vector or a row vector. Matrices can be visualized as a table of values with dimensions r × c (r is the number of rows and c is the number of columns). Introduction to MATLAB® Lecture 2: Vectors and Matrices 2 / 25 Vectors and Matrices Creating row vectors Place the values that you want in the vector in square brackets separated by either spaces or commas. e.g 1 >> row vec=[1 2 3 4 5] 2 row vec = 3 1 2 3 4 5 4 5 >> row vec=[1,2,3,4,5] 6 row vec= 7 1 2 3 4 5 Introduction to MATLAB® Lecture 2: Vectors and Matrices 3 / 25 Vectors and Matrices Creating row vectors - colon operator If the values of the vectors are regularly spaced, the colon operator can be used to iterate through these values. (first:last) produces a vector with all integer entries from first to last e.g. 1 2 >> row vec = 1:5 3 row vec = 4 1 2 3 4 5 Introduction to MATLAB® Lecture 2: Vectors and Matrices 4 / 25 Vectors and Matrices Creating row vectors - colon operator A step value can also be specified with another colon in the form (first:step:last) 1 2 >>odd vec = 1:2:9 3 odd vec = 4 1 3 5 7 9 Introduction to MATLAB® Lecture 2: Vectors and Matrices 5 / 25 Vectors and Matrices Exercise In using (first:step:last), what happens if adding the step value would go beyond the range specified by last? e.g: 1 >>v = 1:2:6 Introduction to MATLAB® Lecture 2: Vectors and Matrices 6 / 25 Vectors and Matrices Exercise Use (first:step:last) to generate the vector v1 = [9 7 5 3 1 ]? Introduction to MATLAB® Lecture 2: Vectors and Matrices 7 / 25 Vectors and Matrices Creating row vectors - linspace function linspace (Linearly spaced vector) 1 >>linspace(x,y,n) linspace creates a row vector with n values in the inclusive range from x to y. -
Math 102 -- Linear Algebra I -- Study Guide
Math 102 Linear Algebra I Stefan Martynkiw These notes are adapted from lecture notes taught by Dr.Alan Thompson and from “Elementary Linear Algebra: 10th Edition” :Howard Anton. Picture above sourced from (http://i.imgur.com/RgmnA.gif) 1/52 Table of Contents Chapter 3 – Euclidean Vector Spaces.........................................................................................................7 3.1 – Vectors in 2-space, 3-space, and n-space......................................................................................7 Theorem 3.1.1 – Algebraic Vector Operations without components...........................................7 Theorem 3.1.2 .............................................................................................................................7 3.2 – Norm, Dot Product, and Distance................................................................................................7 Definition 1 – Norm of a Vector..................................................................................................7 Definition 2 – Distance in Rn......................................................................................................7 Dot Product.......................................................................................................................................8 Definition 3 – Dot Product...........................................................................................................8 Definition 4 – Dot Product, Component by component..............................................................8 -
Notes on Change of Bases Northwestern University, Summer 2014
Notes on Change of Bases Northwestern University, Summer 2014 Let V be a finite-dimensional vector space over a field F, and let T be a linear operator on V . Given a basis (v1; : : : ; vn) of V , we've seen how we can define a matrix which encodes all the information about T as follows. For each i, we can write T vi = a1iv1 + ··· + anivn 2 for a unique choice of scalars a1i; : : : ; ani 2 F. In total, we then have n scalars aij which we put into an n × n matrix called the matrix of T relative to (v1; : : : ; vn): 0 1 a11 ··· a1n B . .. C M(T )v := @ . A 2 Mn;n(F): an1 ··· ann In the notation M(T )v, the v showing up in the subscript emphasizes that we're taking this matrix relative to the specific bases consisting of v's. Given any vector u 2 V , we can also write u = b1v1 + ··· + bnvn for a unique choice of scalars b1; : : : ; bn 2 F, and we define the coordinate vector of u relative to (v1; : : : ; vn) as 0 1 b1 B . C n M(u)v := @ . A 2 F : bn In particular, the columns of M(T )v are the coordinates vectors of the T vi. Then the point of the matrix M(T )v is that the coordinate vector of T u is given by M(T u)v = M(T )vM(u)v; so that from the matrix of T and the coordinate vectors of elements of V , we can in fact reconstruct T itself. -
Lesson 1 Introduction to MATLAB
Math 323 Linear Algebra and Matrix Theory I Fall 1999 Dr. Constant J. Goutziers Department of Mathematical Sciences [email protected] Lesson 1 Introduction to MATLAB MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. This allows easy solution of many technical computing problems, especially those with matrix and vector formulations. For many colleges and universities MATLAB is the Linear Algebra software of choice. The name MATLAB stands for matrix laboratory. 1.1 Row and Column vectors, Formatting In linear algebra we make a clear distinction between row and column vectors. • Example 1.1.1 Entering a row vector. The components of a row vector are entered inside square brackets, separated by blanks. v=[1 2 3] v = 1 2 3 • Example 1.1.2 Entering a column vector. The components of a column vector are also entered inside square brackets, but they are separated by semicolons. w=[4; 5/2; -6] w = 4.0000 2.5000 -6.0000 • Example 1.1.3 Switching between a row and a column vector, the transpose. In MATLAB it is possible to switch between row and column vectors using an apostrophe. The process is called "taking the transpose". To illustrate the process we print v and w and generate the transpose of each of these vectors. Observe that MATLAB commands can be entered on the same line, as long as they are separated by commas. v, vt=v', w, wt=w' v = 1 2 3 vt = 1 2 3 w = 4.0000 2.5000 -6.0000 wt = 4.0000 2.5000 -6.0000 • Example 1.1.4 Formatting. -
Linear Algebra I: Vector Spaces A
Linear Algebra I: Vector Spaces A 1 Vector spaces and subspaces 1.1 Let F be a field (in this book, it will always be either the field of reals R or the field of complex numbers C). A vector space V D .V; C; o;˛./.˛2 F// over F is a set V with a binary operation C, a constant o and a collection of unary operations (i.e. maps) ˛ W V ! V labelled by the elements of F, satisfying (V1) .x C y/ C z D x C .y C z/, (V2) x C y D y C x, (V3) 0 x D o, (V4) ˛ .ˇ x/ D .˛ˇ/ x, (V5) 1 x D x, (V6) .˛ C ˇ/ x D ˛ x C ˇ x,and (V7) ˛ .x C y/ D ˛ x C ˛ y. Here, we write ˛ x and we will write also ˛x for the result ˛.x/ of the unary operation ˛ in x. Often, one uses the expression “multiplication of x by ˛”; but it is useful to keep in mind that what we really have is a collection of unary operations (see also 5.1 below). The elements of a vector space are often referred to as vectors. In contrast, the elements of the field F are then often referred to as scalars. In view of this, it is useful to reflect for a moment on the true meaning of the axioms (equalities) above. For instance, (V4), often referred to as the “associative law” in fact states that the composition of the functions V ! V labelled by ˇ; ˛ is labelled by the product ˛ˇ in F, the “distributive law” (V6) states that the (pointwise) sum of the mappings labelled by ˛ and ˇ is labelled by the sum ˛ C ˇ in F, and (V7) states that each of the maps ˛ preserves the sum C. -
Coordinate Vectors and Examples
Coordinate Vectors and Examples Francis J. Narcowich Department of Mathematics Texas A&M University June 2013 Coordinate vectors. This is a brief discussion of coordinate vectors and the notation for them that I presented in class. Here is the setup for all of the problems. We begin with a vector space V that has a basis B = {v1,..., vn} – i.e., a spanning set that is linearly independent. We always keep the same order for vectors in the basis. Technically, this is called an ordered basis. The following theorem, Theorem 3.2, p. 139, in the text gives the necessary ingredients for making coordinates: Theorem 1 (Coordinate Theorem) Let V = Span(B), where the set B = {v1,..., vn}. Then, every v ∈ V can represented in exactly one way as linear combination of the vj’s if and only if B = {v1,..., vn} is linearly independent – hence, B is a basis, since it spans. In particular, if B is a basis, there are unique scalars x1,...,xn such that v = x1v1 + x2v2 + ··· + xnvn . (1) This theorem allows us to assign coordinates to vectors, provided we don’t change the order of the vectors in B. That is, B is is an ordered basis. When order matters we write B = [v1,..., vn]. (For example, for P3, the ordered basis [1,x,x2] is different than [x,x2, 1].) If the basis is ordered, then the coefficient xj in equation (1) corresponds to vj, and we say that the xj’s are the coordinates of v relative to B. We collect them into the coordinate vector x1 . -
Bases, Coordinates and Representations (3/27/19)
Bases, Coordinates and Representations (3/27/19) Alex Nita Abstract This section gets to the core of linear algebra: the separation of vectors and linear transformations from their particular numerical realizations, which, until now, we've largely taken for granted. We've taken them for granted because calculus and trigonometry were constructed within the standard Cartesian co- ordinate system for R2 and R3, and the issue was never raised. There is a way to construct a coordinate-free calculus, of course, just like there is a way to construct a coordinate-free linear algebra|the abstract way, purely in terms of properties|but I believe that misses the crucial point. We want coordinates. We need them to apply linear algebra to anything, because coordinates correspond to units of measurement forming a `grid.' Putting coordinates on an abstract vector space is like `turning the lights on' in the dark. Except the abstract vector space is not en- tirely dark. The abstract vector space has structure, it's just that the structure is defined purely in terms of properties, not numbers. In the quintessentially modern Bourbaki style of math, where prop- erties alone are considered, the `abstract vector space over a field’ loses even that residual scalar-multiplicative role of numbers, replac- ing it with the generic scalar|the abstract number|the algebraic field. A field captures, by a clever use of (algebraic) properties, the commonalities of Q, R, C, finite fields Fp, and other types of num- bers. The virtue of this approach is that many proofs become simplified— we no longer have to compute ad nauseam, we can just use proper- ties, whose symbolic tidiness compresses or eliminates the mess of numbers. -
Week 1 – Vectors and Matrices
Week 1 – Vectors and Matrices Richard Earl∗ Mathematical Institute, Oxford, OX1 2LB, October 2003 Abstract Algebra and geometry of vectors. The algebra of matrices. 2x2 matrices. Inverses. Determinants. Simultaneous linear equations. Standard transformations of the plane. Notation 1 The symbol R2 denotes the set of ordered pairs (x, y) – that is the xy-plane. Similarly R3 denotes the set of ordered triples (x, y, z) – that is, three-dimensional space described by three co-ordinates x, y and z –andRn denotes a similar n-dimensional space. 1Vectors A vector can be thought of in two different ways. Let’s for the moment concentrate on vectors in the xy-plane. From one point of view a vector is just an ordered pair of numbers (x, y). We can associate this • vector with the point in R2 which has co-ordinates x and y. We call this vector the position vector of the point. From the second point of view a vector is a ‘movement’ or translation. For example, to get from • the point (3, 4) to the point (4, 5) we need to move ‘one to the right and one up’; this is the same movement as is required to move from ( 2, 3) to ( 1, 2) or from (1, 2) to (2, 1) . Thinking about vectors from this second point of− view,− all three− of− these movements− are the− same vector, because the same translation ‘one right, one up’ achieves each of them, even though the ‘start’ and ‘finish’ are different in each case. We would write this vector as (1, 1) . -
4.3 COORDINATES in a LINEAR SPACE by Introducing Coordinates, We Can Transform Any N-Dimensional Linear Space Into R 4.3.1 Coord
4.3 COORDINATES IN A LINEAR SPACE By introducing coordinates, we can transform any n-dimensional linear space into Rn 4.3.1 Coordinates in a linear space Consider a linear space V with a basis B con- sisting of f1; f2; :::fn. Then any element f of V can be written uniquely as f = c1f1 + c2f2 + ¢ ¢ ¢ + cnfn, for some scalars c1; c2; :::; cn. There scalars are called the B coordinates of f, and the vector 2 3 c 6 1 7 6 7 6 c2 7 6 7 6 : 7 6 7 4 : 5 cn is called the B-coordinate vector of f, denoted by [f]B. 1 The B coordinate transformation T (f) = [f]B from V to Rn is an isomorphism (i.e., an invert- ible linear transformation). Thus, V is isomor- phic to Rn; the linear spaces V and Rn have the same structure. Example. Choose a basis of P2 and thus trans- n form P2 into R , for an appropriate n. Example. Let V be the linear space of upper- triangular 2 £ 2 matrices (that is, matrices of the form " # a b : 0 c Choose a basis of V and thus transform V into Rn, for an appropriate n. Example. Do the polynomials, f1(x) = 1 + 2 2 2x + 3x , f2(x) = 4 + 5x + 6x , f3(x) = 7 + 2 8x + 10x from a basis of P2? Solution 3 Since P2 is isomorphic to R , we can use a coordinate transformation to make this into a problem concerning R3. The three given poly- nomials form a basis of P2 if the coordinate vectors 2 3 2 3 2 3 1 4 7 6 7 6 7 6 7 ~v1 = 4 2 5 ; ~v2 = 4 5 5 ; ~v3 = 4 8 5 3 6 9 form a basis of R3. -
Abstract Vector Spaces, Linear Transformations, and Their Coordinate Representations Contents 1 Vector Spaces
Abstract Vector Spaces, Linear Transformations, and Their Coordinate Representations Contents 1 Vector Spaces 1 1.1 Definitions..........................................1 1.1.1 Basics........................................1 1.1.2 Linear Combinations, Spans, Bases, and Dimensions..............1 1.1.3 Sums and Products of Vector Spaces and Subspaces..............3 1.2 Basic Vector Space Theory................................5 2 Linear Transformations 15 2.1 Definitions.......................................... 15 2.1.1 Linear Transformations, or Vector Space Homomorphisms........... 15 2.2 Basic Properties of Linear Transformations....................... 18 2.3 Monomorphisms and Isomorphisms............................ 20 2.4 Rank-Nullity Theorem................................... 23 3 Matrix Representations of Linear Transformations 25 3.1 Definitions.......................................... 25 3.2 Matrix Representations of Linear Transformations................... 26 4 Change of Coordinates 31 4.1 Definitions.......................................... 31 4.2 Change of Coordinate Maps and Matrices........................ 31 1 Vector Spaces 1.1 Definitions 1.1.1 Basics A vector space (linear space) V over a field F is a set V on which the operations addition, + : V × V ! V , and left F -action or scalar multiplication, · : F × V ! V , satisfy: for all x; y; z 2 V and a; b; 1 2 F VS1 x + y = y + x 9 VS2 (x + y) + z = x + (y + z) => (V; +) is an abelian group VS3 90 2 V such that x + 0 = x > VS4 8x 2 V; 9y 2 V such that x + y = 0 ; VS5 1x = x VS6 (ab)x = a(bx) VS7 a(x + y) = ax + ay VS8 (a + b)x = ax + ay If V is a vector space over F , then a subset W ⊆ V is called a subspace of V if W is a vector space over the same field F and with addition and scalar multiplication +jW ×W and ·jF ×W .