Aided Navigation: GPS with High Rate Sensors Errata list. Jay A. Farrell University of California, Riverside

Size: px
Start display at page:

Download "Aided Navigation: GPS with High Rate Sensors Errata list. Jay A. Farrell University of California, Riverside"

Transcription

1 Aided Navigation: GPS with High Rate Sensors Errata list Jay A. Farrell University of California, Riverside February 19, 16

2 Abstract. This document records and corrects errors in the book Aided Navigation: GPS with High Rate Sensors. The most up-to-date version of this document can be obtained from the authors website farrell. Thank you to the various readers who have requested clarifications or pointed out the errors corrected herein.

3 Chapter 1 Introduction p.8, eqn. (1.1) For consistency, the equation should read ˆv = â(t). The correction has no consequence in the remainder of the example since the discussion on the top of page 9 makes clear that (for this simple example) â = ã. 3

4 Chapter Reference Frames p.31, Table.1 The title for the second column should be Symbol. p. 4, text line 3 realized should be realize p.45, last eqn. of Example.4 The eqn. should read n e d t = R t e x y z e p.45 The sentence at the middle of the page should read Next, using eqns. (.78.79) it is straightforward... p.49, eqn. (.4) The eqn. should read u v w b = R b p.49 The penultimate sentence should read... rotations has singular points... p.5 The second sentence of Section.5.5 should read A small angle transformation is... p.5 The last sentence of Section.5.4 is poorly worded. The quantities being compared are x y z ω it = ω ie + ω et and ω ig = ω ie + ω eg. 4

5 In both cases, ω ie is constant. For the fixed tangent frame ω et is zero. For the geographic frame ω eg is dependent on the platform motion. The main point being that ω it ω ig, unless the platform is stationary. p.5 In the denominator of eqn. (.45) the R t b should be Rg b. p.5 Eqn. (.55) should read Ω g eg = λ sin(φ) φ λ sin(φ) λ cos(φ) φ λ cos(φ) The dot indicating the time derivative was misplaced on the (,3) element. p.55 The last of the three equations between eqns. (.6) and (.63) should read ( d t ) Ωdτ t e k 1 R a dt b (t) = where the dt has been corrected to dτ. p. 6 In Part 4 of Exercise.4, the last expression at the bottom of the page should be ( z + p (1 e ) ) 3 R M = b (1 e. ). 5

6 Chapter 3 Deterministic Systems p.74 In the penultimate sentence of Section 3.4, the eqn. reference should be to (3.41) (3.4). p.77 Eqn. (3.45) should read: x k+n = n 1 i= p.77 Eqn. (3.46) should read: n 1 Φ k+ix k + n 1 j= i=j y k+n = H k+nx k+n. Φ k+iγ k+ju k+j. p.79 In Example 3.1, the eigenvalues are at 1 ± j. The negative sign in the real part was missing in the original text. p.89 Near the middle of the page, the definition should be a i = u i Φ n p. The negative sign is missing in the text. p.89 The last matrix at the bottom of the page should be a a 1... UΦU 1 = a n a n... p.9 The role of the integer r o is confused in the discussion. The following changes should correct the confusion. 6

7 1. The dimensions of v 1 and v are reversed. They should be v 1 R ro and v R n ro.. The column dimensions for the matrices are backwards. They should be W 1 R n ro and W R n (n ro). p.93 In Example 3.18, in the state vector as shown in the right-hand side of the first equation at the middle of the page, the last two elements should be b a and b g, not ḃa and ḃg. p. 13 In the first line after eqn. (3.15), the definition should be δε g a = [ cos(θ) sin(θ) ] u p. 7

8 Chapter 4 Stochastic Processes p.111 The second equation on the page should read: P {W 1 < w W } = F w (W ) F w (W 1 ) = W W 1 p w (W )dw. p.117 Four lines below eqn. (4.) the the should be the. p.117 Five lines below eqn. (4.) small random affects should be small random effects. p.14 Eqn. (4.36) should read R v (τ) = σ vδ(τ). p.14 The last equation on the page should read: lim T 1 T v (t)dt = R v () = 1 T T π = 1 π S v (jω)dω σ vdω =. p.14 In the penultimate sentence of the penultimate paragraph (i.e., six lines from the bottom of the page), Gaussion should be Gaussian. p.18 Eqn. (4.5) should read S y (jω) = βσ w β + ω. The change is the ω in the denominator. 8

9 p.135 In the middle paragraph of the page, given that σ ω is the PSD should read given that σ ω is the PSD. p.141 In eqn. (15), readers have questioned the t in F 1 F 3 t t 1 t t 1 e F33s dsdt. This is correct as written. It can be more written as F 1 F 3 t t 1 τ t 1 e F33s dsdτ to indicate more clearly that τ is a dummy variable. p.143 In the last sentence of the first paragraph, Q d should be Qd. p.144 In Section 4.7.., all Q symbols should be replace by GQG. p.148 In the second line, principle should be principal. p.148 The second line of the main equation on the page should be ) 1 = exp ( v dv (π) 3 v c p.148 The phrase near the middle of the page should read... [39], but this value was... p.158 In the expression for Φ(t)P x ()Φ (t), the upper right element on the right-hand side should be P b t. p.158 The last sentence and a half should be: t Φ(τ)ΓQΓ Φ (τ)dτ = σ va t3 3 + σ ω t 5 b σ va t + σ ω t 4 b 8 σ va t + σ ω t 4 b 8 σv a t + σ ω t 3 b 3 σ ω b t 3 6 σ ω b t σ ω t 3 b 6 σ ω t b σ ω b t where we have used the fact that P() = diag(p p, P v, P b ). Therefore, the error variance of each of the three states is described by ( P p (t) = P p + P v t t 4 ) ( σ + P b + va t 3 + σ ω b t 5 ) 4 3 P v (t) = ( P v + P b t ) + (σ va t + σ ω b t 3 ) 3 P b (t) = P b + σ ω b t. p.16 The eigenvalues for the discrete-time system are.9 ±.7j, and.85. 9

10 Chapter 5 Optimal State Estimation p.171, eqn. (5.3) The hat is missing from the x + k 1 on the right-hand side. The equation should read ˆx k = Φˆx + k 1 + Gu k 1. p.18 Table 5.1 shows that, for large m, the computational and memory requirements of the batch algorithm are O(n m) and O(nm), respectively. Fig. 5.3, p. 188 The signs for the difference between y k and z k are reversed. Example 5.4, p.188 In the example, the notation P x (k) is used, when it should have been Pˆx (k). Example 5.4 Following is additional information regarding the derivation of Pˆx (k). Some preliminary statements are useful that aid the derivation. 1. The example goes through for two different sets of assumptions. First, x k and B can be considered deterministic, but unknown. Second, x k and B can be considered as random variables that are uncorrelated with each other and with n k and v k. Under either assumption, B is a constant. Those assumptions were not clearly stated, but either is reasonable. Under either of these assumptions: var(z k ) = E (z k E z k ) = E (x k + v k + B E x k + v k + B ) 1

11 = E (x k E x k + v k + B E B ) = E (x k E x k ) + E vk + E B E B ) = + (µσ) + (5.1). The estimate ˆx k is unbiased. This is easily shown: E x k ˆx k = E x k (z k ˆB ) k = E x k (x k + v k + B ˆB ) k ) = E ˆB k B + E v k = (5.) where the fact that E ˆB k B = is true from Example Note that z k and ˆB k are cross-correlated: E z k ˆBk = E = E = E ( z k ˆB k (r k k ˆB ) ) k 1 ( z k ˆB k (z k y k k ˆB ) ) k 1 ( z k ˆBk ) + 1 k k z k y kz k k = + µ σ +. k 4. Similarly, v k and ˆB k are cross-correlated: ( E v k ˆBk = E v k ˆB k (r k k ˆB ) ) k 1 ( = E v k ˆB k (z k y k k ˆB ) ) k 1 ( = E v k ˆBk ) + 1 k k z kv k y kv k k Derivation: = + µ σ +. k Pˆx (k) = E (x k ˆx k ) ( = E x k (z k ˆB ) k ) ( = E x k (x k + v k + B ˆB ) k ) 11

12 ( = E ( ˆB ) k B) v k = E ( ˆB k B) E ( ˆB k B)v k + E vk = = ( 1 + µ ) σ µ σ ( k ) k 1 µ + µ σ k + µ σ Checks: To investigate the validity of Pˆxk consider the following two checks on the result. 1. For k = 1, it is true that ˆx 1 = y 1. Therefore, Pˆxk = σ.. As k, Pˆxk (µσ). The result derived above satisfies both of these checks. 1

13 Chapter 6 Performance Analysis p.18, eqn. (6.4) The matrix ˆΦ should be ˆΦ k. p.18, Eqn. (6.11) The eqn. should be [ y x k = [H k, ] k ˆx k ] + ν k p., Step 3 The Qd k should be ˆQd k. p.5 In line 9, principal should be principle. p.33 Exercise should be Exercises. 13

14 Chapter 7 Navigation System Design p.41 The following paragraphs are meant to clarify the ideas of Section The discussion assumes that λ >. The text should state that the system is only observable over time intervals during which the acceleration u(t) is not constant. The following discussion supports this statement. Assuming that the acceleration measurement u(t) is constant, the observability matrix is λ y O = 1 λ y u λ 3 y λ 4 y as stated on page 41. At any time, given that u(t) is constant, rank(o) = 4, because the last two rows are linearly dependent. Given this observability matrix, using the methods of Section 3.6.3, the subspace spanned by the vectors: 1 q 1 =, q 1 =, q 3 = 1, q 4 = 1 u 14

15 is observable. Vectors in the subspace spanned by the vector: q 5 = u 1 are not observable. This mathematically shows the fact that for a constant acceleration, a constant bias of magnitude b u is indistinguishable from a scale factor error of magnitude α = bu u. For a constant acceleration, the estimation error converges to the subspace spanned by q 5. Note however that this subspace is different for different values of the acceleration (i.e., the vector q 5 (u) is a function of u). The only vector in the intersection of the subspace spanned by q 5 (u 1 ) and subspace spanned by q 5 (u ) for u 1 u is the zero vector. Therefore, over intervals of time where the acceleration u is changed, the system should be observable; however, the results in the Aided Navigation book only discuss observability for time invariant systems. For the following discussion of time varying systems, consider without loss of generality the time interval t [, t]. For a time varying system, we are interested in the rank of the observability grammian defined as M(t, ) = t Φ(τ, ) H HΦ(τ, )dτ where Φ(t, ) is the state transition matrix (see Section 3.5.3) for the time varying matrix F. For this example, with τ = and F as defined in eqn. (7.15), the state transition matrix is t 1 t t s u(τ)dτds t 1 t Φ(t, ) = u(τ)dτ 1 e λt. 1 Therefore, the observability grammian is M(t, ) = t 1 τ τ e λτ g(τ) τ τ τ 3 τe λτ τg(τ) τ τ 3 τ 4 τ 4 e λτ τ g(τ) e λτ τe λτ τ e λτ e λτ e λτ g(τ) g(τ) τg(τ) τ g(τ) g(τ) (g(τ)) dτ. 15

16 s where g(t) = t u(τ)dτds. This matrix has rank 5 unless u(t) is a constant vector, in which case the third and fifth columns are identical and the grammian has rank 4. Therefore, the system is observable over time intervals where the acceleration is not constant. p.46 In Table 7.1, the heading of the fourth column should be σ by. p.5 In eqn. (7.9), the left-hand side should be f(ˆx, ). 16

17 Chapter 8 Global Positioning System p.65 On line 14, course should be coarse. p.68 The second sentence in the last paragraph should read Therefore, the estimated value of x will be affected by the error ( p i ˆp i). p.74 Two lines after eqn. (8.18) the left-hand side of the equation should be R e a. p. 85 In the line after (8.48), h i = ˆp k ˆp i p k ˆp i should be h i = ˆp k ˆp i ˆp k ˆp i. p. 93 In the penultimate sentence of Section 8.4.5, (ĥ (p p )) s ĥ ) should be (ĥ (ˆp s p s ) ĥ p. 36 Following eqn. (8.84) the phrase should be where E cm is... than... p. 313 The equation above eqn. (118) should read as ρ i ro = (R(p r, ˆp i ) + c t rr + η i r + cδt i + cδt i a r + E i r) (R(p o, ˆp i ) + c t ro + η i o + cδt i + cδt i a o + E i o) The E i r and E i o were interchanged. p. 34 Eqn (8.147) has an extra opening parenthesis. 17

18 Chapter 9 GPS Aided Encoder-Based Dead-Reckoning p. 338 The sixth line should read... and two right increments.... p. 338 The phrase before eqn. (9.4) should be: Using the kinematic relationship v w = v b + ω b tb R where the subscript w denotes wheel and the superscript b denotes body, the body frame linear velocity of the center of each wheel is... p. 34 Eqn. (9.14) should read: p. 341 Eqn should read [ cos( ˆψk ) ˆp k = ˆp k 1 + sin( ˆψ k ) [ cos( ˆψk ) = ˆp k 1 + sin( ˆψ k ) ˆx = [ˆn, ê, ˆψ, ˆR L, ˆR R ], ] π C ] π C [ e L (k) ˆR L T k + ˆR e R (k) R T k [ ˆRL e L (k) + ˆR R e R (k)]. ] T k 18

19 Chapter 1 AHRS p. 354 In eqn. (1.1), the element in the fourth row, third column should be b 1. p. 357 The sentence after eqn. (1.15) should point to Section.5.4, not Section.5.3. p. 357 The sentence containing eqn. (1.18) should read: The initial yaw angle can be computed from the first two components of m w as ˆψ(T ) = atan( m w, m w 1 ). p. 358 In eqn. (1.3), the element in the fourth row, third column should be ˆb 1. p. 364 Eqn. (1.55) should read p.365 Eqn. (1.57) should read ρ = ˆR n b δω b ib ω n in. ρ = ˆR n b δx g ˆR n b ν g ω n in. p.365 The AHRS state space error model at the start of Section should read ρ δẋ g δẋ a = ˆR n b F g F a + I ˆR n b I I ρ δx g δx a ω n in ω g ν g ω a. 19

20 [p. 368 In eqn. (1.66) the element in the third row, third column should be P a, not P aa.

21 Chapter 11 Aided Inertial Navigation p. 381 The last phrase on the page should read In an inertial reference frame... p. 39 In eqn. (11.43), the rightmost factor in the right hand side should be v n e : v n e = R n b f b + g n (Ω n en + Ω n ie) v n e p. 39 In the line between (11.44) and (11.45), the factor R n b should be R b n. p. 395 In the first column, third row of F vp, the ˆv e should be v e. p. 396 In the sixth row, first column, the entry should be v e Ω D. p. 46 In the penultimate paragraph in the last sentence, the phrase Specific definitions for F vg and... should be Specific definitions for F ρg and... p. 41 Eqn. (11) is missing a transpose. p. 41 The eqn. for δnl a should read: k x1 fu + k x fv + k x3 fw + k x4 f u f v + k x5 f v f w + k x6 f w f u δnl a = k y1 fu + k y fv + k y3 fw + k y4 f u f v + k y5 f v f w + k y6 f w f u. k z1 fu + k z fv + k z3 fw + k z4 f u f v + k z5 f v f w + k z6 f w f u The f x at the right should be f u. p. 41 In eqn. (11.134), ν a should be ν g p. 41 In eqn. (11.134) and the subsequent equation, T p T p x bg. b g should be 1

22 p. 413 The R g b is eqn. (11.138) should be removed. p. 413 In the third row of δk g, the k p1 should be k r1. p. 419 After the definition of G, the sentence should state that ɛ D is unobservable. p. 419 At the end of the paragraph that defines G, the parenthetic comment should be (i.e., 1 milli-radian per 1 mg bias). p. 4 Eqn. (11.156) should be ˆR(ˆp e, ˆp i ) = ˆp e ˆp i. p. 45 The parenthese in eqn. (11.17) are incorrect. The second line should read: ( δy a = h δp n n [ˆr n ]ρ + ˆR n b δr b) + ν.

23 Chapter 1 LBL and Doppler Aided INS p. 447 In the second paragraph from the bottom, the end of the first sentence should read... it will always be the case that δˆx + (t ) will be zero. 3

24 Appendix A Appendix A. Notation No known errors. 4

25 Appendix B Appendix B. Linear Algebra Review p. 46 Between eqns. (B.15) and (B.16), the middle portion of the paragraph should read The equivalence expressed in eqn. (B.15) will be denoted... p. 467 Lemma B.5. eqn. (B.3) is missing an inverse. The equation should read (A + BCD) 1 = A 1 A 1 B ( DA 1 B + C 1) 1 DA 1. p. 47 In Section B.11, in the final result for P, the first column of the first row that reads d 11 + d u a + d 33 u 13 should read d 11 + d u 1 + d 33 u 13. 5

26 Appendix C Appendix C. Calculation of GPS Satellite Position & Velocity No known errors. 6

27 Appendix D Appendix D. Quaternions p. 5 The sentence containing eqn. (D.5) should read as follows: The norm of a quaternion is the square root of the scalar portion of b b b = b 1 + b + b 3 + b 4. (D.1) The vector portion of the quaternion b b is zero. p. 54 Section D..1 provides a single formula for the computation of a quaternion to represent a given rotation matrix. Alternative formulas may be preferred depending on the rotation matrix corresponding to a specific situation. Such formulas are available on the books website in the directory related to quaternions. 7

FAQ and Errata Sheet for The Global Positioning System and Inertial Navigation: Theory and Practice

FAQ and Errata Sheet for The Global Positioning System and Inertial Navigation: Theory and Practice FAQ and Errata Sheet for The Global Positioning System and Inertial Navigation: Theory and Practice Jay Farrell Deartment of Electrical Engineering University of California, Riverside jay.farrell@ucr.edu

More information

Corrections and Minor Revisions of Mathematical Methods in the Physical Sciences, third edition, by Mary L. Boas (deceased)

Corrections and Minor Revisions of Mathematical Methods in the Physical Sciences, third edition, by Mary L. Boas (deceased) Corrections and Minor Revisions of Mathematical Methods in the Physical Sciences, third edition, by Mary L. Boas (deceased) Updated December 6, 2017 by Harold P. Boas This list includes all errors known

More information

ERRATA TO: STRAPDOWN ANALYTICS, FIRST EDITION PAUL G. SAVAGE PARTS 1 AND 2 STRAPDOWN ASSOCIATES, INC. 2000

ERRATA TO: STRAPDOWN ANALYTICS, FIRST EDITION PAUL G. SAVAGE PARTS 1 AND 2 STRAPDOWN ASSOCIATES, INC. 2000 ERRATA TO: STRAPDOWN ANAYTICS, FIRST EDITION PAU G. SAVAGE PARTS 1 AND 2 STRAPDOWN ASSOCIATES, INC. 2000 Corrections Reported From April 2004 To Present Pg. 10-45 - Section 10.1.3, first paragraph - Frequency

More information

Elementary maths for GMT

Elementary 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 information

ME 234, Lyapunov and Riccati Problems. 1. This problem is to recall some facts and formulae you already know. e Aτ BB e A τ dτ

ME 234, Lyapunov and Riccati Problems. 1. This problem is to recall some facts and formulae you already know. e Aτ BB e A τ dτ ME 234, Lyapunov and Riccati Problems. This problem is to recall some facts and formulae you already know. (a) Let A and B be matrices of appropriate dimension. Show that (A, B) is controllable if and

More information

MP463 QUANTUM MECHANICS

MP463 QUANTUM MECHANICS MP463 QUANTUM MECHANICS Introduction Quantum theory of angular momentum Quantum theory of a particle in a central potential - Hydrogen atom - Three-dimensional isotropic harmonic oscillator (a model of

More information

Tensor Analysis in Euclidean Space

Tensor Analysis in Euclidean Space Tensor Analysis in Euclidean Space James Emery Edited: 8/5/2016 Contents 1 Classical Tensor Notation 2 2 Multilinear Functionals 4 3 Operations With Tensors 5 4 The Directional Derivative 5 5 Curvilinear

More information

Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition

Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition Prof. Tesler Math 283 Fall 2016 Also see the separate version of this with Matlab and R commands. Prof. Tesler Diagonalizing

More information

Zeros and zero dynamics

Zeros and zero dynamics CHAPTER 4 Zeros and zero dynamics 41 Zero dynamics for SISO systems Consider a linear system defined by a strictly proper scalar transfer function that does not have any common zero and pole: g(s) =α p(s)

More information

Linear Algebra March 16, 2019

Linear 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 information

DIGITAL FILTERS Analysis, Design, and Applications by A. Antoniou ERRATA. Page 10, Table 1.1: The upper limit of the summation should be K.

DIGITAL FILTERS Analysis, Design, and Applications by A. Antoniou ERRATA. Page 10, Table 1.1: The upper limit of the summation should be K. DIGITAL FILTERS Analysis, Design, and Applications by A. Antoniou ERRATA Printing #1 Page vii, line 4 : Replace Geophysicists by Geoscientists. Page 1, Table 1.1: The upper limit of the summation should

More information

Mathematical Introduction

Mathematical Introduction Chapter 1 Mathematical Introduction HW #1: 164, 165, 166, 181, 182, 183, 1811, 1812, 114 11 Linear Vector Spaces: Basics 111 Field A collection F of elements a,b etc (also called numbers or scalars) with

More information

Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition

Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition Prof. Tesler Math 283 Fall 2018 Also see the separate version of this with Matlab and R commands. Prof. Tesler Diagonalizing

More information

Controllability, Observability & Local Decompositions

Controllability, Observability & Local Decompositions ontrollability, Observability & Local Decompositions Harry G. Kwatny Department of Mechanical Engineering & Mechanics Drexel University Outline Lie Bracket Distributions ontrollability ontrollability Distributions

More information

Lecture 9: Modeling and motion models

Lecture 9: Modeling and motion models Sensor Fusion, 2014 Lecture 9: 1 Lecture 9: Modeling and motion models Whiteboard: Principles and some examples. Slides: Sampling formulas. Noise models. Standard motion models. Position as integrated

More information

Lecture notes: Applied linear algebra Part 1. Version 2

Lecture notes: Applied linear algebra Part 1. Version 2 Lecture notes: Applied linear algebra Part 1. Version 2 Michael Karow Berlin University of Technology karow@math.tu-berlin.de October 2, 2008 1 Notation, basic notions and facts 1.1 Subspaces, range and

More information

ENGR352 Problem Set 02

ENGR352 Problem Set 02 engr352/engr352p02 September 13, 2018) ENGR352 Problem Set 02 Transfer function of an estimator 1. Using Eq. (1.1.4-27) from the text, find the correct value of r ss (the result given in the text is incorrect).

More information

1 Matrices and matrix algebra

1 Matrices and matrix algebra 1 Matrices and matrix algebra 1.1 Examples of matrices A matrix is a rectangular array of numbers and/or variables. For instance 4 2 0 3 1 A = 5 1.2 0.7 x 3 π 3 4 6 27 is a matrix with 3 rows and 5 columns

More information

CSL361 Problem set 4: Basic linear algebra

CSL361 Problem set 4: Basic linear algebra CSL361 Problem set 4: Basic linear algebra February 21, 2017 [Note:] If the numerical matrix computations turn out to be tedious, you may use the function rref in Matlab. 1 Row-reduced echelon matrices

More information

First of all, the notion of linearity does not depend on which coordinates are used. Recall that a map T : R n R m is linear if

First of all, the notion of linearity does not depend on which coordinates are used. Recall that a map T : R n R m is linear if 5 Matrices in Different Coordinates In this section we discuss finding matrices of linear maps in different coordinates Earlier in the class was the matrix that multiplied by x to give ( x) in standard

More information

Tensors, and differential forms - Lecture 2

Tensors, and differential forms - Lecture 2 Tensors, and differential forms - Lecture 2 1 Introduction The concept of a tensor is derived from considering the properties of a function under a transformation of the coordinate system. A description

More information

Lecture Notes in Linear Algebra

Lecture 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 information

Errata for Implementing Spectral Methods: Algorithms for Scientists and Engineers

Errata for Implementing Spectral Methods: Algorithms for Scientists and Engineers Errata for Implementing Spectral Methods: Algorithms for Scientists and Engineers David A. Kopriva May 1, 017 1 Chapter 1 1. Page 7. Eq. (1.8) should be (Thans to Yaning Liu) f(x) P N f(x) = = = π. Page

More information

ELEMENTS OF PROBABILITY THEORY

ELEMENTS OF PROBABILITY THEORY ELEMENTS OF PROBABILITY THEORY Elements of Probability Theory A collection of subsets of a set Ω is called a σ algebra if it contains Ω and is closed under the operations of taking complements and countable

More information

1. Let m 1 and n 1 be two natural numbers such that m > n. Which of the following is/are true?

1. 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 information

Errata as of September 4, 2003 Biostatistical Methods: The Assessment of Relative Risks John M. Lachin

Errata as of September 4, 2003 Biostatistical Methods: The Assessment of Relative Risks John M. Lachin i Errata as of September 4, 2003 Biostatistical Methods: The Assessment of Relative Risks John M. Lachin John Wiley and Sons, 2000 ISBN: 0-471-36996-9 The following errors have been detected to date in

More information

REVIEW - Vectors. Vectors. Vector Algebra. Multiplication by a scalar

REVIEW - Vectors. Vectors. Vector Algebra. Multiplication by a scalar J. Peraire Dynamics 16.07 Fall 2004 Version 1.1 REVIEW - Vectors By using vectors and defining appropriate operations between them, physical laws can often be written in a simple form. Since we will making

More information

Statistics 992 Continuous-time Markov Chains Spring 2004

Statistics 992 Continuous-time Markov Chains Spring 2004 Summary Continuous-time finite-state-space Markov chains are stochastic processes that are widely used to model the process of nucleotide substitution. This chapter aims to present much of the mathematics

More information

Stat 159/259: Linear Algebra Notes

Stat 159/259: Linear Algebra Notes Stat 159/259: Linear Algebra Notes Jarrod Millman November 16, 2015 Abstract These notes assume you ve taken a semester of undergraduate linear algebra. In particular, I assume you are familiar with the

More information

Gaussian Filtering Strategies for Nonlinear Systems

Gaussian Filtering Strategies for Nonlinear Systems Gaussian Filtering Strategies for Nonlinear Systems Canonical Nonlinear Filtering Problem ~u m+1 = ~ f (~u m )+~ m+1 ~v m+1 = ~g(~u m+1 )+~ o m+1 I ~ f and ~g are nonlinear & deterministic I Noise/Errors

More information

Chapter 0. Preliminaries. 0.1 Things you should already know

Chapter 0. Preliminaries. 0.1 Things you should already know Chapter 0 Preliminaries These notes cover the course MATH45061 (Continuum Mechanics) and are intended to supplement the lectures. The course does not follow any particular text, so you do not need to buy

More information

Robotics I. February 6, 2014

Robotics I. February 6, 2014 Robotics I February 6, 214 Exercise 1 A pan-tilt 1 camera sensor, such as the commercial webcams in Fig. 1, is mounted on the fixed base of a robot manipulator and is used for pointing at a (point-wise)

More information

Numerical Methods for Engineers, Second edition: Chapter 1 Errata

Numerical Methods for Engineers, Second edition: Chapter 1 Errata Numerical Methods for Engineers, Second edition: Chapter 1 Errata 1. p.2 first line, remove the Free Software Foundation at 2. p.2 sixth line of the first proper paragraph, fe95.res should be replaced

More information

Math Camp II. Basic Linear Algebra. Yiqing Xu. Aug 26, 2014 MIT

Math 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 information

On the Observability and Self-Calibration of Visual-Inertial Navigation Systems

On the Observability and Self-Calibration of Visual-Inertial Navigation Systems Center for Robotics and Embedded Systems University of Southern California Technical Report CRES-08-005 R B TIC EMBEDDED SYSTEMS LABORATORY On the Observability and Self-Calibration of Visual-Inertial

More information

Linear Algebra V = T = ( 4 3 ).

Linear Algebra V = T = ( 4 3 ). Linear Algebra Vectors A column vector is a list of numbers stored vertically The dimension of a column vector is the number of values in the vector W is a -dimensional column vector and V is a 5-dimensional

More information

. D CR Nomenclature D 1

. D CR Nomenclature D 1 . D CR Nomenclature D 1 Appendix D: CR NOMENCLATURE D 2 The notation used by different investigators working in CR formulations has not coalesced, since the topic is in flux. This Appendix identifies the

More information

4.2. ORTHOGONALITY 161

4.2. ORTHOGONALITY 161 4.2. ORTHOGONALITY 161 Definition 4.2.9 An affine space (E, E ) is a Euclidean affine space iff its underlying vector space E is a Euclidean vector space. Given any two points a, b E, we define the distance

More information

Vectors To begin, let us describe an element of the state space as a point with numerical coordinates, that is x 1. x 2. x =

Vectors To begin, let us describe an element of the state space as a point with numerical coordinates, that is x 1. x 2. x = Linear Algebra Review Vectors To begin, let us describe an element of the state space as a point with numerical coordinates, that is x 1 x x = 2. x n Vectors of up to three dimensions are easy to diagram.

More information

Appendix A: Matrices

Appendix A: Matrices Appendix A: Matrices A matrix is a rectangular array of numbers Such arrays have rows and columns The numbers of rows and columns are referred to as the dimensions of a matrix A matrix with, say, 5 rows

More information

with Application to Autonomous Vehicles

with Application to Autonomous Vehicles Nonlinear with Application to Autonomous Vehicles (Ph.D. Candidate) C. Silvestre (Supervisor) P. Oliveira (Co-supervisor) Institute for s and Robotics Instituto Superior Técnico Portugal January 2010 Presentation

More information

Mathematical foundations - linear algebra

Mathematical foundations - linear algebra Mathematical foundations - linear algebra Andrea Passerini passerini@disi.unitn.it Machine Learning Vector space Definition (over reals) A set X is called a vector space over IR if addition and scalar

More information

Mathematical Foundations of Quantum Mechanics

Mathematical Foundations of Quantum Mechanics Mathematical Foundations of Quantum Mechanics 2016-17 Dr Judith A. McGovern Maths of Vector Spaces This section is designed to be read in conjunction with chapter 1 of Shankar s Principles of Quantum Mechanics,

More information

Lecture 13 The Fundamental Forms of a Surface

Lecture 13 The Fundamental Forms of a Surface Lecture 13 The Fundamental Forms of a Surface In the following we denote by F : O R 3 a parametric surface in R 3, F(u, v) = (x(u, v), y(u, v), z(u, v)). We denote partial derivatives with respect to the

More information

Chapter III. Stability of Linear Systems

Chapter III. Stability of Linear Systems 1 Chapter III Stability of Linear Systems 1. Stability and state transition matrix 2. Time-varying (non-autonomous) systems 3. Time-invariant systems 1 STABILITY AND STATE TRANSITION MATRIX 2 In this chapter,

More information

Vectors and Matrices

Vectors and Matrices Vectors and Matrices Scalars We often employ a single number to represent quantities that we use in our daily lives such as weight, height etc. The magnitude of this number depends on our age and whether

More information

Optimization-Based Control

Optimization-Based Control Optimization-Based Control Richard M. Murray Control and Dynamical Systems California Institute of Technology DRAFT v1.7a, 19 February 2008 c California Institute of Technology All rights reserved. This

More information

arxiv: v5 [math.na] 16 Nov 2017

arxiv: v5 [math.na] 16 Nov 2017 RANDOM PERTURBATION OF LOW RANK MATRICES: IMPROVING CLASSICAL BOUNDS arxiv:3.657v5 [math.na] 6 Nov 07 SEAN O ROURKE, VAN VU, AND KE WANG Abstract. Matrix perturbation inequalities, such as Weyl s theorem

More information

Mathematics for Graphics and Vision

Mathematics for Graphics and Vision Mathematics for Graphics and Vision Steven Mills March 3, 06 Contents Introduction 5 Scalars 6. Visualising Scalars........................ 6. Operations on Scalars...................... 6.3 A Note on

More information

MIT Final Exam Solutions, Spring 2017

MIT Final Exam Solutions, Spring 2017 MIT 8.6 Final Exam Solutions, Spring 7 Problem : For some real matrix A, the following vectors form a basis for its column space and null space: C(A) = span,, N(A) = span,,. (a) What is the size m n of

More information

Problem Set (T) If A is an m n matrix, B is an n p matrix and D is a p s matrix, then show

Problem Set (T) If A is an m n matrix, B is an n p matrix and D is a p s matrix, then show MTH 0: Linear Algebra Department of Mathematics and Statistics Indian Institute of Technology - Kanpur Problem Set Problems marked (T) are for discussions in Tutorial sessions (T) If A is an m n matrix,

More information

Extra Problems for Math 2050 Linear Algebra I

Extra Problems for Math 2050 Linear Algebra I Extra Problems for Math 5 Linear Algebra I Find the vector AB and illustrate with a picture if A = (,) and B = (,4) Find B, given A = (,4) and [ AB = A = (,4) and [ AB = 8 If possible, express x = 7 as

More information

18.06 Problem Set 3 Due Wednesday, 27 February 2008 at 4 pm in

18.06 Problem Set 3 Due Wednesday, 27 February 2008 at 4 pm in 8.6 Problem Set 3 Due Wednesday, 27 February 28 at 4 pm in 2-6. Problem : Do problem 7 from section 2.7 (pg. 5) in the book. Solution (2+3+3+2 points) a) False. One example is when A = [ ] 2. 3 4 b) False.

More information

Errata List for the Second Printing

Errata List for the Second Printing Errata List for the Second Printing The Second Revised Edition has had two printings, and it also exists on the web. The second printing can be distinguished from the first printing by the fact that on

More information

Math 350 Fall 2011 Notes about inner product spaces. In this notes we state and prove some important properties of inner product spaces.

Math 350 Fall 2011 Notes about inner product spaces. In this notes we state and prove some important properties of inner product spaces. Math 350 Fall 2011 Notes about inner product spaces In this notes we state and prove some important properties of inner product spaces. First, recall the dot product on R n : if x, y R n, say x = (x 1,...,

More information

Errata Dynamic General Equilibrium Modelling, Springer: Berlin August 2015

Errata Dynamic General Equilibrium Modelling, Springer: Berlin August 2015 Errata Dynamic General Equilibrium Modelling, Springer: Berlin 2005 17 August 2015 Chapter 1.1 p. 9/10: Figure 1.1 was computed for the parameter values T = 59, α = 0.50, and ρ = 0.35 and not as stated

More information

Adaptive State Estimation Robert Stengel Optimal Control and Estimation MAE 546 Princeton University, 2018

Adaptive State Estimation Robert Stengel Optimal Control and Estimation MAE 546 Princeton University, 2018 Adaptive State Estimation Robert Stengel Optimal Control and Estimation MAE 546 Princeton University, 218! Nonlinearity of adaptation! Parameter-adaptive filtering! Test for whiteness of the residual!

More information

Foundations of Matrix Analysis

Foundations of Matrix Analysis 1 Foundations of Matrix Analysis In this chapter we recall the basic elements of linear algebra which will be employed in the remainder of the text For most of the proofs as well as for the details, the

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Chemistry 5.76 Revised February, 1982 NOTES ON MATRIX METHODS

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Chemistry 5.76 Revised February, 1982 NOTES ON MATRIX METHODS MASSACHUSETTS INSTITUTE OF TECHNOLOGY Chemistry 5.76 Revised February, 198 NOTES ON MATRIX METHODS 1. Matrix Algebra Margenau and Murphy, The Mathematics of Physics and Chemistry, Chapter 10, give almost

More information

Chapter 1. Rigid Body Kinematics. 1.1 Introduction

Chapter 1. Rigid Body Kinematics. 1.1 Introduction Chapter 1 Rigid Body Kinematics 1.1 Introduction This chapter builds up the basic language and tools to describe the motion of a rigid body this is called rigid body kinematics. This material will be the

More information

Discrete Wavelet Transformations: An Elementary Approach with Applications

Discrete Wavelet Transformations: An Elementary Approach with Applications Discrete Wavelet Transformations: An Elementary Approach with Applications Errata Sheet March 6, 009 Please report any errors you find in the text to Patrick J. Van Fleet at pjvanfleet@stthomas.edu. The

More information

Short Review of Basic Mathematics

Short Review of Basic Mathematics Short Review of Basic Mathematics Tomas Co January 1, 1 c 8 Tomas Co, all rights reserve Contents 1 Review of Functions 5 11 Mathematical Ientities 5 1 Drills 7 1 Answers to Drills 8 Review of ODE Solutions

More information

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

2. 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 information

Motion in Three Dimensions

Motion in Three Dimensions Motion in Three Dimensions We ve learned about the relationship between position, velocity and acceleration in one dimension Now we need to extend those ideas to the three-dimensional world In the 1-D

More information

Rotational Kinematics. Description of attitude kinematics using reference frames, rotation matrices, Euler parameters, Euler angles, and quaternions

Rotational Kinematics. Description of attitude kinematics using reference frames, rotation matrices, Euler parameters, Euler angles, and quaternions Rotational Kinematics Description of attitude kinematics using reference frames, rotation matrices, Euler parameters, Euler angles, and quaternions Recall the fundamental dynamics equations ~ f = d dt

More information

0.1 Tangent Spaces and Lagrange Multipliers

0.1 Tangent Spaces and Lagrange Multipliers 01 TANGENT SPACES AND LAGRANGE MULTIPLIERS 1 01 Tangent Spaces and Lagrange Multipliers If a differentiable function G = (G 1,, G k ) : E n+k E k then the surface S defined by S = { x G( x) = v} is called

More information

WELL-POSEDNESS FOR HYPERBOLIC PROBLEMS (0.2)

WELL-POSEDNESS FOR HYPERBOLIC PROBLEMS (0.2) WELL-POSEDNESS FOR HYPERBOLIC PROBLEMS We will use the familiar Hilbert spaces H = L 2 (Ω) and V = H 1 (Ω). We consider the Cauchy problem (.1) c u = ( 2 t c )u = f L 2 ((, T ) Ω) on [, T ] Ω u() = u H

More information

Problem 2 (Gaussian Elimination, Fundamental Spaces, Least Squares, Minimum Norm) Consider the following linear algebraic system of equations:

Problem 2 (Gaussian Elimination, Fundamental Spaces, Least Squares, Minimum Norm) Consider the following linear algebraic system of equations: EEE58 Exam, Fall 6 AA Rodriguez Rules: Closed notes/books, No calculators permitted, open minds GWC 35, 965-37 Problem (Dynamic Augmentation: State Space Representation) Consider a dynamical system consisting

More information

Algebra C Numerical Linear Algebra Sample Exam Problems

Algebra C Numerical Linear Algebra Sample Exam Problems Algebra C Numerical Linear Algebra Sample Exam Problems Notation. Denote by V a finite-dimensional Hilbert space with inner product (, ) and corresponding norm. The abbreviation SPD is used for symmetric

More information

Errata for Instructor s Solutions Manual for Gravity, An Introduction to Einstein s General Relativity 1st printing

Errata for Instructor s Solutions Manual for Gravity, An Introduction to Einstein s General Relativity 1st printing Errata for Instructor s Solutions Manual for Gravity, An Introduction to Einstein s General Relativity st printing Updated 7/7/003 (hanks to Scott Fraser who provided almost all of these.) Statement of

More information

Gravitation: Special Relativity

Gravitation: Special Relativity An Introduction to General Relativity Center for Relativistic Astrophysics School of Physics Georgia Institute of Technology Notes based on textbook: Spacetime and Geometry by S.M. Carroll Spring 2013

More information

1 Mathematical preliminaries

1 Mathematical preliminaries 1 Mathematical preliminaries The mathematical language of quantum mechanics is that of vector spaces and linear algebra. In this preliminary section, we will collect the various definitions and mathematical

More information

Errata for the book A First Course in Numerical Methods, by Uri Ascher and Chen Greif

Errata for the book A First Course in Numerical Methods, by Uri Ascher and Chen Greif Errata for the book A First Course in Numerical Methods, Uri Ascher and Chen Greif February 8, 016 In this file we have collected various changes to be made to the first edition of our book. Several have

More information

Linear Algebra, Spring 2005

Linear Algebra, Spring 2005 Linear Algebra, Spring 2005 Solutions May 4, 2005 Problem 4.89 To check for linear independence write the vectors as rows of a matrix. Reduce the matrix to echelon form and determine the number of non-zero

More information

ERRATA. Interest and Prices, Princeton University Press, updated February 2, 2008

ERRATA. Interest and Prices, Princeton University Press, updated February 2, 2008 ERRATA Interest and Prices, Princeton University Press, 2003 updated February 2, 2008 Chapter 1: - page 26, line 4 from bottom; page 27, line 12 from bottom; and p. 28, footnote 17: Woodford (2001c) should

More information

Linear Algebra 2 Spectral Notes

Linear Algebra 2 Spectral Notes Linear Algebra 2 Spectral Notes In what follows, V is an inner product vector space over F, where F = R or C. We will use results seen so far; in particular that every linear operator T L(V ) has a complex

More information

Introduction to Partial Differential Equations

Introduction to Partial Differential Equations Introduction to Partial Differential Equations by Peter J Olver Page ix Corrections to First Printing (2014) Last updated: May 2, 2017 Line 7: Change In order the make progress to In order to make progress

More information

Page 21, Exercise 6 should read: bysetting up a one-to-one correspondence between the set Z = {..., 3, 2, 1, 0, 1, 2, 3,...} and the set N.

Page 21, Exercise 6 should read: bysetting up a one-to-one correspondence between the set Z = {..., 3, 2, 1, 0, 1, 2, 3,...} and the set N. Errata for Elementary Classical Analysis, Second Edition Jerrold E. Marsden and Michael J. Hoffman marsden@cds.caltech.edu, mhoffma@calstatela.edu Version: August 10, 2001. What follows are the errata

More information

Lecture 6: Geometry of OLS Estimation of Linear Regession

Lecture 6: Geometry of OLS Estimation of Linear Regession Lecture 6: Geometry of OLS Estimation of Linear Regession Xuexin Wang WISE Oct 2013 1 / 22 Matrix Algebra An n m matrix A is a rectangular array that consists of nm elements arranged in n rows and m columns

More information

Errata for Robot Vision

Errata for Robot Vision Errata for Robot Vision This is a list of known nontrivial bugs in Robot Vision 1986 by B.K.P. Horn, MIT Press, Cambridge, MA ISBN 0-262-08159-8 and McGraw-Hill, New York, NY ISBN 0-07-030349-5. If you

More information

Linear Algebra and Robot Modeling

Linear Algebra and Robot Modeling Linear Algebra and Robot Modeling Nathan Ratliff Abstract Linear algebra is fundamental to robot modeling, control, and optimization. This document reviews some of the basic kinematic equations and uses

More information

A Randomized Algorithm for the Approximation of Matrices

A Randomized Algorithm for the Approximation of Matrices A Randomized Algorithm for the Approximation of Matrices Per-Gunnar Martinsson, Vladimir Rokhlin, and Mark Tygert Technical Report YALEU/DCS/TR-36 June 29, 2006 Abstract Given an m n matrix A and a positive

More information

MTH 309 Supplemental Lecture Notes Based on Robert Messer, Linear Algebra Gateway to Mathematics

MTH 309 Supplemental Lecture Notes Based on Robert Messer, Linear Algebra Gateway to Mathematics MTH 309 Supplemental Lecture Notes Based on Robert Messer, Linear Algebra Gateway to Mathematics Ulrich Meierfrankenfeld Department of Mathematics Michigan State University East Lansing MI 48824 meier@math.msu.edu

More information

Engineering Dynamics: A Comprehensive Introduction Errata. N. Jeremy Kasdin & Derek A. Paley

Engineering Dynamics: A Comprehensive Introduction Errata. N. Jeremy Kasdin & Derek A. Paley Engineering Dynamics: A Comprehensive Introduction Errata N. Jeremy Kasdin & Derek A. Paley Last updated November 10, 2017 PRINCETON UNIVERSITY PRESS PRINCETON AND OXFORD Chapter 2: i) [First printing

More information

4 Linear Algebra Review

4 Linear Algebra Review Linear Algebra Review For this topic we quickly review many key aspects of linear algebra that will be necessary for the remainder of the text 1 Vectors and Matrices For the context of data analysis, the

More information

Math 110 (Fall 2018) Midterm II (Monday October 29, 12:10-1:00)

Math 110 (Fall 2018) Midterm II (Monday October 29, 12:10-1:00) Math 110 (Fall 2018) Midterm II (Monday October 29, 12:10-1:00) Name: SID: Please write clearly and legibly. Justify your answers. Partial credits may be given to Problems 2, 3, 4, and 5. The last sheet

More information

Linear System Theory

Linear System Theory Linear System Theory Wonhee Kim Chapter 6: Controllability & Observability Chapter 7: Minimal Realizations May 2, 217 1 / 31 Recap State space equation Linear Algebra Solutions of LTI and LTV system Stability

More information

Quaternion Dynamics, Part 1 Functions, Derivatives, and Integrals. Gary D. Simpson. rev 00 Dec 27, 2014.

Quaternion Dynamics, Part 1 Functions, Derivatives, and Integrals. Gary D. Simpson. rev 00 Dec 27, 2014. Quaternion Dynamics, Part 1 Functions, Derivatives, and Integrals Gary D. Simpson gsim100887@aol.com rev 00 Dec 27, 2014 Summary Definitions are presented for "quaternion functions" of a quaternion. Polynomial

More information

Aided Inertial Navigation With Geometric Features: Observability Analysis

Aided Inertial Navigation With Geometric Features: Observability Analysis Aided Inertial Navigation With Geometric Features: Observability Analysis Yulin Yang - yuyang@udeledu Guoquan Huang - ghuang@udeledu Department of Mechanical Engineering University of Delaware, Delaware,

More information

Errata and Updates for ASM Exam MAS-I (First Edition) Sorted by Date

Errata and Updates for ASM Exam MAS-I (First Edition) Sorted by Date Errata for ASM Exam MAS-I Study Manual (First Edition) Sorted by Date 1 Errata and Updates for ASM Exam MAS-I (First Edition) Sorted by Date Practice Exam 5 Question 6 is defective. See the correction

More information

Errata and Updates for ASM Exam MAS-I (First Edition) Sorted by Page

Errata and Updates for ASM Exam MAS-I (First Edition) Sorted by Page Errata for ASM Exam MAS-I Study Manual (First Edition) Sorted by Page 1 Errata and Updates for ASM Exam MAS-I (First Edition) Sorted by Page Practice Exam 5 Question 6 is defective. See the correction

More information

Chapter SSM: Linear Algebra. 5. Find all x such that A x = , so that x 1 = x 2 = 0.

Chapter SSM: Linear Algebra. 5. Find all x such that A x = , so that x 1 = x 2 = 0. Chapter Find all x such that A x : Chapter, so that x x ker(a) { } Find all x such that A x ; note that all x in R satisfy the equation, so that ker(a) R span( e, e ) 5 Find all x such that A x 5 ; x x

More information

Deterministic Operations Research Errata. Updated: October 17, 2013

Deterministic Operations Research Errata. Updated: October 17, 2013 Deterministic Operations Research Errata Updated: October 17, 2013 Chapter 1 1. (pg. 7) In line 2 (from the top of the page), the word flower should read flour. Chapter 2 1. (pg. 22) Line -7 (from the

More information

Errata for The Geometry and Topology of Coxeter Groups

Errata for The Geometry and Topology of Coxeter Groups Errata for The Geometry and Topology of Coxeter Groups Michael W. Davis October 16, 2017 Additions are indicated in italics. Table of Contents (1) page viii: The title of 9.2 should be When is Σ Simply

More information

MATH 221: SOLUTIONS TO SELECTED HOMEWORK PROBLEMS

MATH 221: SOLUTIONS TO SELECTED HOMEWORK PROBLEMS MATH 221: SOLUTIONS TO SELECTED HOMEWORK PROBLEMS 1. HW 1: Due September 4 1.1.21. Suppose v, w R n and c is a scalar. Prove that Span(v + cw, w) = Span(v, w). We must prove two things: that every element

More information

TI89 Titanium Exercises - Part 10.

TI89 Titanium Exercises - Part 10. TI89 Titanium Exercises - Part. Entering matrices directly in the HOME screen s entry line ) Enter your nxm matrix using this format: [[ a, a 2,,a m ][a 2, a 22, a 2m ] [a n, a n2,, a nm ]] This is a matrix

More information

Introduction to relativistic quantum mechanics

Introduction to relativistic quantum mechanics Introduction to relativistic quantum mechanics. Tensor notation In this book, we will most often use so-called natural units, which means that we have set c = and =. Furthermore, a general 4-vector will

More information

14 Singular Value Decomposition

14 Singular Value Decomposition 14 Singular Value Decomposition For any high-dimensional data analysis, one s first thought should often be: can I use an SVD? The singular value decomposition is an invaluable analysis tool for dealing

More information

Lecture Notes - Modeling of Mechanical Systems

Lecture Notes - Modeling of Mechanical Systems Thomas Bak Lecture Notes - Modeling of Mechanical Systems February 19, 2002 Aalborg University Department of Control Engineering Fredrik Bajers Vej 7C DK-9220 Aalborg Denmark 2 Table of Contents Table

More information

NORMS ON SPACE OF MATRICES

NORMS ON SPACE OF MATRICES NORMS ON SPACE OF MATRICES. Operator Norms on Space of linear maps Let A be an n n real matrix and x 0 be a vector in R n. We would like to use the Picard iteration method to solve for the following system

More information