Math 2331 – Linear Algebra 4.4 Coordinate Systems

Total Page:16

File Type:pdf, Size:1020Kb

Math 2331 – Linear Algebra 4.4 Coordinate Systems 4.4 Coordinate Systems Math 2331 { Linear Algebra 4.4 Coordinate Systems Jiwen He Department of Mathematics, University of Houston [email protected] math.uh.edu/∼jiwenhe/math2331 Jiwen He, University of Houston Math 2331, Linear Algebra 1 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates 4.4 Coordinate Systems Coordinate Systems Definition: Coordinates and Coordinate Vector Examples Change-of-Coordinates Matrix Definition Examples 3 Parallel Worlds of R and P2 Isomorphic Jiwen He, University of Houston Math 2331, Linear Algebra 2 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates Coordinate Systems In general, people are more comfortable working with the vector space Rn and its subspaces than with other types of vectors spaces and subspaces. The goal here is to impose coordinate systems on vector spaces, even if they are not in Rn. Theorem (7) Let β = fb1;:::; bng be a basis for a vector space V . Then for each x in V , there exists a unique set of scalars c1;:::; cn such that x = c1b1 + ··· + cnbn: Jiwen He, University of Houston Math 2331, Linear Algebra 3 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates Coordinate Systems: Definition Coordinates Suppose β = fb1;:::; bng is a basis for a vector space V and x is in V . The coordinates of x relative to the basis β (or the β− coordinates of x) are the weights c1;:::; cn such that x = c1b1 + ··· + cnbn: Coordinate Vector In this case, the vector in Rn 2 3 c1 [x] = 6 . 7 β 4 . 5 cn is called the coordinate vector of x (relative to β), or the β− coordinate vector of x. Jiwen He, University of Houston Math 2331, Linear Algebra 4 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates Coordinate Systems: Example Example 3 0 Let β = fb ; b g where b = and b = and let 1 2 1 1 2 1 1 0 E = fe ; e g where e = and e = . 1 2 1 0 2 1 Solution: 2 3 0 If [x] = , then x = + = . β 3 1 1 6 1 0 If [x] = , then x = + = : E 5 0 1 Jiwen He, University of Houston Math 2331, Linear Algebra 5 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates Coordinate Systems: Example (cont.) Standard graph paper β− graph paper Jiwen He, University of Houston Math 2331, Linear Algebra 6 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates Change-of-Coordinates Matrix From the last example, 6 3 0 2 = : 5 1 1 3 For a basis β = fb ;:::; b g, let 1 n 2 3 c1 6 c2 7 P = [b b ··· b ] and [x] = 6 7 β 1 2 n β 6 . 7 4 . 5 cn Then x =Pβ [x]β : We call Pβ the change-of-coordinates matrix from β to the standard basis in Rn. Then −1 [x]β = Pβ x −1 and therefore Pβ is a change-of-coordinates matrix from the standard basis in Rn to the basis β. Jiwen He, University of Houston Math 2331, Linear Algebra 7 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates Change-of-Coordinates Matrix: Example Example 3 0 6 Let b = , b = ; β = fb ; b g and x = . Find 1 1 2 1 1 2 8 the change-of-coordinates matrix Pβ from β to the standard basis 2 −1 in R and change-of-coordinates matrix Pβ from the standard basis in R2 to β. Solution : Pβ = [b1 b2] = and so −1 1 −1 3 0 3 0 Pβ = = 1 : 1 1 − 3 1 Jiwen He, University of Houston Math 2331, Linear Algebra 8 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates Change-of-Coordinates Matrix: Example (cont.) Example 6 2 If x = , then use P−1 to find [x] = . 8 β β 6 Solution: 1 −1 3 0 6 [x]β = Pβ x = 1 = − 3 1 8 Jiwen He, University of Houston Math 2331, Linear Algebra 9 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates Change-of-Coordinates Matrix: Example Coordinate mappings allow us to introduce coordinate systems for unfamiliar vector spaces. Example 2 Standard basis for P2 : fp1; p2; p3g = 1; t; t . Polynomials in P2 behave like vectors in R3. Since 2 a 3 2 2 a + bt + ct = p1 + p2 + p3, a + bt + ct β = 4 b 5 c 3 We say that the vector space R is isomorphic to P2. Jiwen He, University of Houston Math 2331, Linear Algebra 10 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates 3 Parallel Worlds of R and P2 3 Vector Space R Vector Space P2 2 a 3 Vector Form: 4 b 5 Vector Form: a + bt + bt2 c Vector Addition Example Vector Addition Example 2 −1 3 2 2 3 2 1 3 −1 + 2t − 3t2 + 2 + 3t + 5t2 2 + 3 = 5 4 5 4 5 4 5 = 1 + 5t + 2t2 −3 5 2 Jiwen He, University of Houston Math 2331, Linear Algebra 11 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates Isomorphic Isomorphic Informally, we say that vector space V is isomorphic to W if every vector space calculation in V is accurately reproduced in W , and vice versa. Assume β is a basis set for vector space V . Exercise 25 (page 223) shows that a set fu1; u2;:::; upg in V is linearly independent if and only n o n if [u1]β ; [u2]β ;:::; [up]β is linearly independent in R . Jiwen He, University of Houston Math 2331, Linear Algebra 12 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates Coordinate Vectors: Example Example Use coordinate vectors to determine if fp1; p2; p3g is a linearly 2 2 independent set: p1 = 1 − t, p2 = 2 − t + t , p3 = 2t + 3t . 2 Solution: The standard basis set for P2 is β = 1; t; t . So 2 3 2 3 2 3 [p1]β = 4 5 ; [p2]β = 4 5 ; [p3]β = 4 5 Then 2 1 2 0 3 2 1 2 0 3 4 −1 −1 2 5 s ··· s 4 0 1 2 5 0 1 3 0 0 1 n o By the IMT, [p1]β ; [p2]β ; [p3]β is linearly and therefore fp1; p2; p3g is linearly . Jiwen He, University of Houston Math 2331, Linear Algebra 13 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates Coordinate Vectors: Example Coordinate vectors also allow us to associate vector spaces with subspaces of other vectors spaces. Example 2 3 3 2 0 3 Let β = fb1; b2g where b1 = 4 3 5 and b2 = 4 1 5. 1 3 2 9 3 Let H =spanfb1; b2g. Find [x]β, if x = 4 13 5. 15 Solution: (a) Find c1 and c2 such that 2 3 3 2 0 3 2 9 3 c1 4 3 5 + c2 4 1 5 = 4 13 5 1 3 15 Jiwen He, University of Houston Math 2331, Linear Algebra 14 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates Coordinate Vectors: Example (cont.) Corresponding augmented matrix: 2 3 0 9 3 2 1 0 3 3 4 3 1 13 5 v 4 0 1 4 5 1 3 15 0 0 0 Therefore c1 = and c2 = and so [x]β = . Jiwen He, University of Houston Math 2331, Linear Algebra 15 / 16 4.4 Coordinate Systems Coordinate Systems Change-of-Coordinates Coordinate Vectors: Example (cont.) 2 9 3 3 13 in R3 is associated with the vector in R2 4 5 4 15 H is isomorphic to R2 Jiwen He, University of Houston Math 2331, Linear Algebra 16 / 16.
Recommended publications
  • Introduction to Linear Bialgebra
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of New Mexico University of New Mexico UNM Digital Repository Mathematics and Statistics Faculty and Staff Publications Academic Department Resources 2005 INTRODUCTION TO LINEAR BIALGEBRA Florentin Smarandache University of New Mexico, [email protected] W.B. Vasantha Kandasamy K. Ilanthenral Follow this and additional works at: https://digitalrepository.unm.edu/math_fsp Part of the Algebra Commons, Analysis Commons, Discrete Mathematics and Combinatorics Commons, and the Other Mathematics Commons Recommended Citation Smarandache, Florentin; W.B. Vasantha Kandasamy; and K. Ilanthenral. "INTRODUCTION TO LINEAR BIALGEBRA." (2005). https://digitalrepository.unm.edu/math_fsp/232 This Book is brought to you for free and open access by the Academic Department Resources at UNM Digital Repository. It has been accepted for inclusion in Mathematics and Statistics Faculty and Staff Publications by an authorized administrator of UNM Digital Repository. For more information, please contact [email protected], [email protected], [email protected]. INTRODUCTION TO LINEAR BIALGEBRA W. B. Vasantha Kandasamy Department of Mathematics Indian Institute of Technology, Madras Chennai – 600036, India e-mail: [email protected] web: http://mat.iitm.ac.in/~wbv Florentin Smarandache Department of Mathematics University of New Mexico Gallup, NM 87301, USA e-mail: [email protected] K. Ilanthenral Editor, Maths Tiger, Quarterly Journal Flat No.11, Mayura Park, 16, Kazhikundram Main Road, Tharamani, Chennai – 600 113, India e-mail: [email protected] HEXIS Phoenix, Arizona 2005 1 This book can be ordered in a paper bound reprint from: Books on Demand ProQuest Information & Learning (University of Microfilm International) 300 N.
    [Show full text]
  • 21. Orthonormal Bases
    21. Orthonormal Bases The canonical/standard basis 011 001 001 B C B C B C B0C B1C B0C e1 = B.C ; e2 = B.C ; : : : ; en = B.C B.C B.C B.C @.A @.A @.A 0 0 1 has many useful properties. • Each of the standard basis vectors has unit length: q p T jjeijj = ei ei = ei ei = 1: • The standard basis vectors are orthogonal (in other words, at right angles or perpendicular). T ei ej = ei ej = 0 when i 6= j This is summarized by ( 1 i = j eT e = δ = ; i j ij 0 i 6= j where δij is the Kronecker delta. Notice that the Kronecker delta gives the entries of the identity matrix. Given column vectors v and w, we have seen that the dot product v w is the same as the matrix multiplication vT w. This is the inner product on n T R . We can also form the outer product vw , which gives a square matrix. 1 The outer product on the standard basis vectors is interesting. Set T Π1 = e1e1 011 B C B0C = B.C 1 0 ::: 0 B.C @.A 0 01 0 ::: 01 B C B0 0 ::: 0C = B. .C B. .C @. .A 0 0 ::: 0 . T Πn = enen 001 B C B0C = B.C 0 0 ::: 1 B.C @.A 1 00 0 ::: 01 B C B0 0 ::: 0C = B. .C B. .C @. .A 0 0 ::: 1 In short, Πi is the diagonal square matrix with a 1 in the ith diagonal position and zeros everywhere else.
    [Show full text]
  • 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].
    [Show full text]
  • Bases for Infinite Dimensional Vector Spaces Math 513 Linear Algebra Supplement
    BASES FOR INFINITE DIMENSIONAL VECTOR SPACES MATH 513 LINEAR ALGEBRA SUPPLEMENT Professor Karen E. Smith We have proven that every finitely generated vector space has a basis. But what about vector spaces that are not finitely generated, such as the space of all continuous real valued functions on the interval [0; 1]? Does such a vector space have a basis? By definition, a basis for a vector space V is a linearly independent set which generates V . But we must be careful what we mean by linear combinations from an infinite set of vectors. The definition of a vector space gives us a rule for adding two vectors, but not for adding together infinitely many vectors. By successive additions, such as (v1 + v2) + v3, it makes sense to add any finite set of vectors, but in general, there is no way to ascribe meaning to an infinite sum of vectors in a vector space. Therefore, when we say that a vector space V is generated by or spanned by an infinite set of vectors fv1; v2;::: g, we mean that each vector v in V is a finite linear combination λi1 vi1 + ··· + λin vin of the vi's. Likewise, an infinite set of vectors fv1; v2;::: g is said to be linearly independent if the only finite linear combination of the vi's that is zero is the trivial linear combination. So a set fv1; v2; v3;:::; g is a basis for V if and only if every element of V can be be written in a unique way as a finite linear combination of elements from the set.
    [Show full text]
  • Determinants Math 122 Calculus III D Joyce, Fall 2012
    Determinants Math 122 Calculus III D Joyce, Fall 2012 What they are. A determinant is a value associated to a square array of numbers, that square array being called a square matrix. For example, here are determinants of a general 2 × 2 matrix and a general 3 × 3 matrix. a b = ad − bc: c d a b c d e f = aei + bfg + cdh − ceg − afh − bdi: g h i The determinant of a matrix A is usually denoted jAj or det (A). You can think of the rows of the determinant as being vectors. For the 3×3 matrix above, the vectors are u = (a; b; c), v = (d; e; f), and w = (g; h; i). Then the determinant is a value associated to n vectors in Rn. There's a general definition for n×n determinants. It's a particular signed sum of products of n entries in the matrix where each product is of one entry in each row and column. The two ways you can choose one entry in each row and column of the 2 × 2 matrix give you the two products ad and bc. There are six ways of chosing one entry in each row and column in a 3 × 3 matrix, and generally, there are n! ways in an n × n matrix. Thus, the determinant of a 4 × 4 matrix is the signed sum of 24, which is 4!, terms. In this general definition, half the terms are taken positively and half negatively. In class, we briefly saw how the signs are determined by permutations.
    [Show full text]
  • 28. Exterior Powers
    28. Exterior powers 28.1 Desiderata 28.2 Definitions, uniqueness, existence 28.3 Some elementary facts 28.4 Exterior powers Vif of maps 28.5 Exterior powers of free modules 28.6 Determinants revisited 28.7 Minors of matrices 28.8 Uniqueness in the structure theorem 28.9 Cartan's lemma 28.10 Cayley-Hamilton Theorem 28.11 Worked examples While many of the arguments here have analogues for tensor products, it is worthwhile to repeat these arguments with the relevant variations, both for practice, and to be sensitive to the differences. 1. Desiderata Again, we review missing items in our development of linear algebra. We are missing a development of determinants of matrices whose entries may be in commutative rings, rather than fields. We would like an intrinsic definition of determinants of endomorphisms, rather than one that depends upon a choice of coordinates, even if we eventually prove that the determinant is independent of the coordinates. We anticipate that Artin's axiomatization of determinants of matrices should be mirrored in much of what we do here. We want a direct and natural proof of the Cayley-Hamilton theorem. Linear algebra over fields is insufficient, since the introduction of the indeterminate x in the definition of the characteristic polynomial takes us outside the class of vector spaces over fields. We want to give a conceptual proof for the uniqueness part of the structure theorem for finitely-generated modules over principal ideal domains. Multi-linear algebra over fields is surely insufficient for this. 417 418 Exterior powers 2. Definitions, uniqueness, existence Let R be a commutative ring with 1.
    [Show full text]
  • Coordinatization
    MATH 355 Supplemental Notes Coordinatization Coordinatization In R3, we have the standard basis i, j and k. When we write a vector in coordinate form, say 3 v 2 , (1) “ »´ fi 5 — ffi – fl it is understood as v 3i 2j 5k. “ ´ ` The numbers 3, 2 and 5 are the coordinates of v relative to the standard basis ⇠ i, j, k . It has p´ q “p q always been understood that a coordinate representation such as that in (1) is with respect to the ordered basis ⇠. A little thought reveals that it need not be so. One could have chosen the same basis elements in a di↵erent order, as in the basis ⇠ i, k, j . We employ notation indicating the 1 “p q coordinates are with respect to the di↵erent basis ⇠1: 3 v 5 , to mean that v 3i 5k 2j, r s⇠1 “ » fi “ ` ´ 2 —´ ffi – fl reflecting the order in which the basis elements fall in ⇠1. Of course, one could employ similar notation even when the coordinates are expressed in terms of the standard basis, writing v for r s⇠ (1), but whenever we have coordinatization with respect to the standard basis of Rn in mind, we will consider the wrapper to be optional. r¨s⇠ Of course, there are many non-standard bases of Rn. In fact, any linearly independent collection of n vectors in Rn provides a basis. Say we take 1 1 1 4 ´ ´ » 0fi » 1fi » 1fi » 1fi u , u u u ´ . 1 “ 2 “ 3 “ 4 “ — 3ffi — 1ffi — 0ffi — 2ffi — ffi —´ ffi — ffi — ffi — 0ffi — 4ffi — 2ffi — 1ffi — ffi — ffi — ffi —´ ffi – fl – fl – fl – fl As per the discussion above, these vectors are being expressed relative to the standard basis of R4.
    [Show full text]
  • Review a Basis of a Vector Space 1
    Review • Vectors v1 , , v p are linearly dependent if x1 v1 + x2 v2 + + x pv p = 0, and not all the coefficients are zero. • The columns of A are linearly independent each column of A contains a pivot. 1 1 − 1 • Are the vectors 1 , 2 , 1 independent? 1 3 3 1 1 − 1 1 1 − 1 1 1 − 1 1 2 1 0 1 2 0 1 2 1 3 3 0 2 4 0 0 0 So: no, they are dependent! (Coeff’s x3 = 1 , x2 = − 2, x1 = 3) • Any set of 11 vectors in R10 is linearly dependent. A basis of a vector space Definition 1. A set of vectors { v1 , , v p } in V is a basis of V if • V = span{ v1 , , v p} , and • the vectors v1 , , v p are linearly independent. In other words, { v1 , , vp } in V is a basis of V if and only if every vector w in V can be uniquely expressed as w = c1 v1 + + cpvp. 1 0 0 Example 2. Let e = 0 , e = 1 , e = 0 . 1 2 3 0 0 1 3 Show that { e 1 , e 2 , e 3} is a basis of R . It is called the standard basis. Solution. 3 • Clearly, span{ e 1 , e 2 , e 3} = R . • { e 1 , e 2 , e 3} are independent, because 1 0 0 0 1 0 0 0 1 has a pivot in each column. Definition 3. V is said to have dimension p if it has a basis consisting of p vectors. Armin Straub 1 [email protected] This definition makes sense because if V has a basis of p vectors, then every basis of V has p vectors.
    [Show full text]
  • MATH 304 Linear Algebra Lecture 14: Basis and Coordinates. Change of Basis
    MATH 304 Linear Algebra Lecture 14: Basis and coordinates. Change of basis. Linear transformations. Basis and dimension Definition. Let V be a vector space. A linearly independent spanning set for V is called a basis. Theorem Any vector space V has a basis. If V has a finite basis, then all bases for V are finite and have the same number of elements (called the dimension of V ). Example. Vectors e1 = (1, 0, 0,..., 0, 0), e2 = (0, 1, 0,..., 0, 0),. , en = (0, 0, 0,..., 0, 1) form a basis for Rn (called standard) since (x1, x2,..., xn) = x1e1 + x2e2 + ··· + xnen. Basis and coordinates If {v1, v2,..., vn} is a basis for a vector space V , then any vector v ∈ V has a unique representation v = x1v1 + x2v2 + ··· + xnvn, where xi ∈ R. The coefficients x1, x2,..., xn are called the coordinates of v with respect to the ordered basis v1, v2,..., vn. The mapping vector v 7→ its coordinates (x1, x2,..., xn) is a one-to-one correspondence between V and Rn. This correspondence respects linear operations in V and in Rn. Examples. • Coordinates of a vector n v = (x1, x2,..., xn) ∈ R relative to the standard basis e1 = (1, 0,..., 0, 0), e2 = (0, 1,..., 0, 0),. , en = (0, 0,..., 0, 1) are (x1, x2,..., xn). a b • Coordinates of a matrix ∈ M2,2(R) c d 1 0 0 0 0 1 relative to the basis , , , 0 0 1 0 0 0 0 0 are (a, c, b, d). 0 1 • Coordinates of a polynomial n−1 p(x) = a0 + a1x + ··· + an−1x ∈Pn relative to 2 n−1 the basis 1, x, x ,..., x are (a0, a1,..., an−1).
    [Show full text]
  • Orthonormal Bases
    Math 108B Professor: Padraic Bartlett Lecture 3: Orthonormal Bases Week 3 UCSB 2014 In our last class, we introduced the concept of \changing bases," and talked about writ- ing vectors and linear transformations in other bases. In the homework and in class, we saw that in several situations this idea of \changing basis" could make a linear transformation much easier to work with; in several cases, we saw that linear transformations under a certain basis would become diagonal, which made tasks like raising them to large powers far easier than these problems would be in the standard basis. But how do we find these \nice" bases? What does it mean for a basis to be\nice?" In this set of lectures, we will study one potential answer to this question: the concept of an orthonormal basis. 1 Orthogonality To start, we should define the notion of orthogonality. First, recall/remember the defini- tion of the dot product: n Definition. Take two vectors (x1; : : : xn); (y1; : : : yn) 2 R . Their dot product is simply the sum x1y1 + x2y2 + : : : xnyn: Many of you have seen an alternate, geometric definition of the dot product: n Definition. Take two vectors (x1; : : : xn); (y1; : : : yn) 2 R . Their dot product is the product jj~xjj · jj~yjj cos(θ); where θ is the angle between ~x and ~y, and jj~xjj denotes the length of the vector ~x, i.e. the distance from (x1; : : : xn) to (0;::: 0). These two definitions are equivalent: 3 Theorem. Let ~x = (x1; x2; x3); ~y = (y1; y2; y3) be a pair of vectors in R .
    [Show full text]
  • Pl¨Ucker Basis Vectors
    Pluck¨ er Basis Vectors Roy Featherstone Department of Information Engineering The Australian National University Canberra, ACT 0200, Australia Email: [email protected] Abstract— 6-D vectors are routinely expressed in Pluck¨ er Pluck¨ er coordinate vector and the 6-D vector it represents; it coordinates; yet there is almost no mention in the literature of explains the relationship between a 6-D vector and the two the basis vectors that give rise to these coordinates. This paper 3-D vectors that define it; and it shows how to differentiate identifies the Pluck¨ er basis vectors, and uses them to explain the following: the relationship between a 6-D vector and its Pluck¨ er a 6-D vector in a moving Pluck¨ er coordinate system, using coordinates, the relationship between a 6-D vector and the pair of acceleration as an example. For the sake of a concrete expo- 3-D vectors used to define it, and the correct way to differentiate sition, this paper uses the notation and terminology of spatial a 6-D vector in a moving coordinate system. vectors; but the results apply generally to any 6-D vector that is expressed using Pluck¨ er coordinates. I. INTRODUCTION The rest of this paper is organized as follows. First, the 6-D vectors are used to describe the motions of rigid bodies Pluck¨ er basis vectors are described. Then the topic of dual and the forces acting upon them. They are therefore useful for coordinate systems is discussed. This is relevant to those 6-D describing the kinematics and dynamics of rigid-body systems vector formalisms in which force vectors are deemed to occupy in general, and robot mechanisms in particular.
    [Show full text]
  • 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.
    [Show full text]