1. Vectors.
|
|
- Hubert McKinney
- 5 years ago
- Views:
Transcription
1 1. Vectors
2 1.1 Vectors and Matrices Linear algebra is concerned with two basic kinds of quantities: vectors and matrices.
3 1.1 Vectors and Matrices Scalars and Vectors - Scalar: a numerical value denoted by lowercase italic type such as a, k, v, w, and x ex) temperature, length, and speed - Vector: a numerical value and a direction denoted by lowercase boldface type such as a, k, v, w, and x ex) velocity, force, and displacement
4 1.1 Vectors and Matrices Scalars and Vectors Terminal point Initial point Magnitude Direction
5 1.1 Vectors and Matrices Equivalent Vectors Bound vector Free vector In this text we will focus exclusively on free vectors, leaving the study of bound vectors for courses in engineering and physics. Two vectors v and w are equal (also called equivalent) if they are represented by parallel arrows with the same length and direction. v=w. The vector whose initial and terminal points coincide has length zero, so we call this zero vector and denote it by 0.
6 1.1 Vectors and Matrices Vector Addition
7 1.1 Vectors and Matrices Vector Addition
8 1.1 Vectors and Matrices Vector Subtraction
9 1.1 Vectors and Matrices Scalar Multiplication
10 1.1 Vectors and Matrices Vectors in Coordinate Systems Rectangular coordinate system in 2-space y-axis origin x-axis One-to-one correspondence between points in the plane and ordered pairs of real numbers P point a, b coordinates P a, b
11 1.1 Vectors and Matrices Vectors in Coordinate Systems Rectangular coordinate system in 3-space x-axis, y-axis, z-axis Left-handed Right-handed In this text we will work exclusively with right-handed coordinate systems.
12 1.1 Vectors and Matrices Vectors in Coordinate Systems If a vector v in 2-space or 3-space is positioned with its initial point at the origin of a rectangular coordinate system, then the vector is completely determined by the coordinates of its terminal point, and we call these coordinates the components of v relative to the coordinate system. The set of all vectors in 2-space: R 2 The set of all vectors in 3-space: R 3
13 1.1 Vectors and Matrices Components of a Vector Whose Initial Point Is Not At The Origin v is a vector in R 2 with initial point P 1 (x 1,y 1 ) and terminal point P 2 (x 2,y 2 ). v P P OP OP x x, y y The components of v are obtained by subtracting the coordinates of the initial point from the corresponding coordinates of the terminal point.
14 1.1 Vectors and Matrices Vectors in R n You can think of the numbers in an n-tuple (v 1, v 2,, v n ) as either the coordinates of a generalized point or the components of a generalized vector. 0=(0,0,0,,0) We will call this the zero vector or sometimes the origin of R n.
15 1.1 Vectors and Matrices Equality of Vectors
16 1.1 Vectors and Matrices Equality of Vectors
17 1.1 Vectors and Matrices Sums of Three or More Vectors
18 1.1 Vectors and Matrices Parallel and Collinear Vectors
19 1.1 Vectors and Matrices Linear Combinations
20 1.1 Vectors and Matrices Application to Computer Color Models Colors on computer monitors are commonly based on what is called the RGB color model. Each color vector c in RGB space or the RGB color cube is expressible as a linear combination of the form
21 1.1 Vectors and Matrices Alternative Notations for Vectors Comma-delimited form Row-vector form Column-vector form
22 1.1 Vectors and Matrices Matrices We define a matrix to be a rectangular array of numbers, called the entries of the matrix. You can also think of a matrix as a list of row vectors or column vectors.
23 1.2 Dot Product and Orthogonality Norm of A Vector The length of a vector v in R 2 and R 3 is commonly denoted by the symbol v. From the theorem of Pythagoras,
24 1.2 Dot Product and Orthogonality Norm of A Vector
25 1.2 Dot Product and Orthogonality Unit Vectors A vector of length 1 is called a unit vector. Normalizing v: Example 2 Find the unit vector u that has the same direction as v=(2,2,-1).
26 1.2 Dot Product and Orthogonality Standard Unit Vectors When a rectangular coordinate system is introduced in R 2 or R 3, the unit vectors in the positive directions of the coordinate axes are called the standard unit vectors. In R 2 these vectors are denoted by In R 3 these vectors are denoted by
27 1.2 Dot Product and Orthogonality Standard Unit Vectors Standard unit vectors in R n
28 1.2 Dot Product and Orthogonality Distance between Points in R n
29 1.2 Dot Product and Orthogonality Dot Products Example 3 International Standard Book Number or ISBN
30 1.2 Dot Product and Orthogonality Algebraic Properties of The Dot Products
31 1.2 Dot Product and Orthogonality Algebraic Properties of The Dot Products Example 4
32 1.2 Dot Product and Orthogonality Angle Between Vectors in R 2 and R 3 The angle between u and v: the smallest nonnegative angle θ through which one of the vectors can be rotated in the plane of the vectors until it coincides with the other. Algebraically, the radian measure of is in the interval 0 θ π, and in R 2 the angle is generated by a counterclockwise rotation.
33 1.2 Dot Product and Orthogonality Angle Between Vectors in R 2 and R 3
34 1.2 Dot Product and Orthogonality Angle Between Vectors in R 2 and R 3 Example 5 Find the angle θ between a diagonal of a cube and one of its edges.
35 1.2 Dot Product and Orthogonality Angle Between Vectors in R 2 and R 3 Example 6
36 1.2 Dot Product and Orthogonality Orthogonality Example 7 Example 8 If 0 is the zero vector in R n, then 0 v=0. Thus 0 is orthogonal to R n. w v=0 for every vector v in R n w=0
37 1.2 Dot Product and Orthogonality Orthonormal Sets
38 1.2 Dot Product and Orthogonality Euclidean Geometry in R n
39 1.2 Dot Product and Orthogonality Euclidean Geometry in R n
40 1.2 Dot Product and Orthogonality Euclidean Geometry in R n
41 1.2 Dot Product and Orthogonality Euclidean Geometry in R n
42 1.2 Dot Product and Orthogonality Euclidean Geometry in R n
43 1.3 Vector Equations of Lines and Planes Vector And Parametric Equations of Lines Example 1
44 1.3 Vector Equations of Lines and Planes Lines Through Two Points Example 1
45 1.3 Vector Equations of Lines and Planes Point-Normal Equations of Planes Example 3
46 1.3 Vector Equations of Lines and Planes Vector and Parametric Equations of Planes
47 1.3 Vector Equations of Lines and Planes Vector and Parametric Equations of Planes Example 4 Example 5 A plane is uniquely determined by three noncollinear points. Find the plane that passes through the points P(2,-4,5), Q(-1,4,-3), and R(1,10,-7).
48 1.3 Vector Equations of Lines and Planes Vector and Parametric Equations of Planes Example 6 Find a vector equation of the plane whose parametric equations are Example 7 Find parametric equations of the plane x-y+2z=5.
49 1.3 Vector Equations of Lines and Planes Lines and Planes in R n
50 1.3 Vector Equations of Lines and Planes Comments on Terminology A vector v lies on a line L in R 2 and R 3 if the terminal point of the vector lies on the line when the vector is positioned with its initial point at the origin.
Three-Dimensional Coordinate Systems. Three-Dimensional Coordinate Systems. Three-Dimensional Coordinate Systems. Three-Dimensional Coordinate Systems
To locate a point in a plane, two numbers are necessary. We know that any point in the plane can be represented as an ordered pair (a, b) of real numbers, where a is the x-coordinate and b is the y-coordinate.
More informationMTAEA Vectors in Euclidean Spaces
School of Economics, Australian National University January 25, 2010 Vectors. Economists usually work in the vector space R n. A point in this space is called a vector, and is typically defined by its
More informationReview of Linear Algebra
Review of Linear Algebra Definitions An m n (read "m by n") matrix, is a rectangular array of entries, where m is the number of rows and n the number of columns. 2 Definitions (Con t) A is square if m=
More informationApplied Linear Algebra in Geoscience Using MATLAB
Applied Linear Algebra in Geoscience Using MATLAB Contents Getting Started Creating Arrays Mathematical Operations with Arrays Using Script Files and Managing Data Two-Dimensional Plots Programming in
More informationMatrix Basic Concepts
Matrix Basic Concepts Topics: What is a matrix? Matrix terminology Elements or entries Diagonal entries Address/location of entries Rows and columns Size of a matrix A column matrix; vectors Special types
More information[ Here 21 is the dot product of (3, 1, 2, 5) with (2, 3, 1, 2), and 31 is the dot product of
. Matrices A matrix is any rectangular array of numbers. For example 3 5 6 4 8 3 3 is 3 4 matrix, i.e. a rectangular array of numbers with three rows four columns. We usually use capital letters for matrices,
More informationCSCI 239 Discrete Structures of Computer Science Lab 6 Vectors and Matrices
CSCI 239 Discrete Structures of Computer Science Lab 6 Vectors and Matrices This lab consists of exercises on real-valued vectors and matrices. Most of the exercises will required pencil and paper. Put
More informationLinear Algebra (Review) Volker Tresp 2017
Linear Algebra (Review) Volker Tresp 2017 1 Vectors k is a scalar (a number) c is a column vector. Thus in two dimensions, c = ( c1 c 2 ) (Advanced: More precisely, a vector is defined in a vector space.
More informationIntro Vectors 2D implicit curves 2D parametric curves. Graphics 2011/2012, 4th quarter. Lecture 2: vectors, curves, and surfaces
Lecture 2, curves, and surfaces Organizational remarks Tutorials: Tutorial 1 will be online later today TA sessions for questions start next week Practicals: Exams: Make sure to find a team partner very
More informationI&C 6N. Computational Linear Algebra
I&C 6N Computational Linear Algebra 1 Lecture 1: Scalars and Vectors What is a scalar? Computer representation of a scalar Scalar Equality Scalar Operations Addition and Multiplication What is a vector?
More informationLinear Algebra (Review) Volker Tresp 2018
Linear Algebra (Review) Volker Tresp 2018 1 Vectors k, M, N are scalars A one-dimensional array c is a column vector. Thus in two dimensions, ( ) c1 c = c 2 c i is the i-th component of c c T = (c 1, c
More informationDefinition of Equality of Matrices. Example 1: Equality of Matrices. Consider the four matrices
IT 131: Mathematics for Science Lecture Notes 3 Source: Larson, Edwards, Falvo (2009): Elementary Linear Algebra, Sixth Edition. Matrices 2.1 Operations with Matrices This section and the next introduce
More informationMATH.2720 Introduction to Programming with MATLAB Vector and Matrix Algebra
MATH.2720 Introduction to Programming with MATLAB Vector and Matrix Algebra A. Vectors A vector is a quantity that has both magnitude and direction, like velocity. The location of a vector is irrelevant;
More informationVectors. The standard geometric definition of vector is as something which has direction and magnitude but not position.
Vectors The standard geometric definition of vector is as something which has direction and magnitude but not position. Since vectors have no position we may place them wherever is convenient. Vectors
More informationVectors. 1 Basic Definitions. Liming Pang
Vectors Liming Pang 1 Basic Definitions Definition 1. A vector in a line/plane/space is a quantity which has both magnitude and direction. The magnitude is a nonnegative real number and the direction is
More informationIntro Vectors 2D implicit curves 2D parametric curves. Graphics 2012/2013, 4th quarter. Lecture 2: vectors, curves, and surfaces
Lecture 2, curves, and surfaces Organizational remarks Tutorials: TA sessions for tutorial 1 start today Tutorial 2 will go online after lecture 3 Practicals: Make sure to find a team partner very soon
More informationDS-GA 1002 Lecture notes 0 Fall Linear Algebra. These notes provide a review of basic concepts in linear algebra.
DS-GA 1002 Lecture notes 0 Fall 2016 Linear Algebra These notes provide a review of basic concepts in linear algebra. 1 Vector spaces You are no doubt familiar with vectors in R 2 or R 3, i.e. [ ] 1.1
More informationLast week we presented the following expression for the angles between two vectors u and v in R n ( ) θ = cos 1 u v
Orthogonality (I) Last week we presented the following expression for the angles between two vectors u and v in R n ( ) θ = cos 1 u v u v which brings us to the fact that θ = π/2 u v = 0. Definition (Orthogonality).
More information10.1 Vectors. c Kun Wang. Math 150, Fall 2017
10.1 Vectors Definition. A vector is a quantity that has both magnitude and direction. A vector is often represented graphically as an arrow where the direction is the direction of the arrow, and the magnitude
More information1 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 informationReview of Coordinate Systems
Vector in 2 R and 3 R Review of Coordinate Systems Used to describe the position of a point in space Common coordinate systems are: Cartesian Polar Cartesian Coordinate System Also called rectangular coordinate
More informationPractical Linear Algebra: A Geometry Toolbox
Practical Linear Algebra: A Geometry Toolbox Third edition Chapter 2: Here and There: Points and Vectors in 2D Gerald Farin & Dianne Hansford CRC Press, Taylor & Francis Group, An A K Peters Book www.farinhansford.com/books/pla
More informationx n -2.5 Definition A list is a list of objects, where multiplicity is allowed, and order matters. For example, as lists
Vectors, Linear Combinations, and Matrix-Vector Mulitiplication In this section, we introduce vectors, linear combinations, and matrix-vector multiplication The rest of the class will involve vectors,
More information1.1 Single Variable Calculus versus Multivariable Calculus Rectangular Coordinate Systems... 4
MATH2202 Notebook 1 Fall 2015/2016 prepared by Professor Jenny Baglivo Contents 1 MATH2202 Notebook 1 3 1.1 Single Variable Calculus versus Multivariable Calculus................... 3 1.2 Rectangular Coordinate
More informationEuclidean Spaces. Euclidean Spaces. Chapter 10 -S&B
Chapter 10 -S&B The Real Line: every real number is represented by exactly one point on the line. The plane (i.e., consumption bundles): Pairs of numbers have a geometric representation Cartesian plane
More informationVector Geometry. Chapter 5
Chapter 5 Vector Geometry In this chapter we will look more closely at certain geometric aspects of vectors in R n. We will first develop an intuitive understanding of some basic concepts by looking at
More information4.1 Distance and Length
Chapter Vector Geometry In this chapter we will look more closely at certain geometric aspects of vectors in R n. We will first develop an intuitive understanding of some basic concepts by looking at vectors
More informationVECTORS vectors & scalars vector direction magnitude scalar only magnitude
VECTORS Physical quantities are classified in two big classes: vectors & scalars. A vector is a physical quantity which is completely defined once we know precisely its direction and magnitude (for example:
More informationv = ( 2)
Chapter : Introduction to Vectors.. Vectors and linear combinations Let s begin by saying what vectors are: They are lists of numbers. If there are numbers in the list, there is a natural correspondence
More informationMatrix Algebra: Vectors
A Matrix Algebra: Vectors A Appendix A: MATRIX ALGEBRA: VECTORS A 2 A MOTIVATION Matrix notation was invented primarily to express linear algebra relations in compact form Compactness enhances visualization
More informationReview: Linear and Vector Algebra
Review: Linear and Vector Algebra Points in Euclidean Space Location in space Tuple of n coordinates x, y, z, etc Cannot be added or multiplied together Vectors: Arrows in Space Vectors are point changes
More informationRigid Geometric Transformations
Rigid Geometric Transformations Carlo Tomasi This note is a quick refresher of the geometry of rigid transformations in three-dimensional space, expressed in Cartesian coordinates. 1 Cartesian Coordinates
More informationLinear Algebra & Geometry why is linear algebra useful in computer vision?
Linear Algebra & Geometry why is linear algebra useful in computer vision? References: -Any book on linear algebra! -[HZ] chapters 2, 4 Some of the slides in this lecture are courtesy to Prof. Octavia
More informationChapter 13: Vectors and the Geometry of Space
Chapter 13: Vectors and the Geometry of Space 13.1 3-Dimensional Coordinate System 13.2 Vectors 13.3 The Dot Product 13.4 The Cross Product 13.5 Equations of Lines and Planes 13.6 Cylinders and Quadratic
More informationChapter 13: Vectors and the Geometry of Space
Chapter 13: Vectors and the Geometry of Space 13.1 3-Dimensional Coordinate System 13.2 Vectors 13.3 The Dot Product 13.4 The Cross Product 13.5 Equations of Lines and Planes 13.6 Cylinders and Quadratic
More informationRelationships Between Planes
Relationships Between Planes Definition: consistent (system of equations) A system of equations is consistent if there exists one (or more than one) solution that satisfies the system. System 1: {, System
More informationVectors in R n. P. Danziger
1 Vectors in R n P. Danziger 1 Vectors The standard geometric definition of ector is as something which has direction and magnitude but not position. Since ectors hae no position we may place them whereer
More informationImage Registration Lecture 2: Vectors and Matrices
Image Registration Lecture 2: Vectors and Matrices Prof. Charlene Tsai Lecture Overview Vectors Matrices Basics Orthogonal matrices Singular Value Decomposition (SVD) 2 1 Preliminary Comments Some of this
More informationThe Geometry of R n. Supplemental Lecture Notes for Linear Algebra Courses at Georgia Tech
The Geometry of R n Supplemental Lecture Notes for Linear Algebra Courses at Georgia Tech Contents Vectors in R n. Vectors....................................... The Length and Direction of a Vector......................3
More informationMatrix Operations. Linear Combination Vector Algebra Angle Between Vectors Projections and Reflections Equality of matrices, Augmented Matrix
Linear Combination Vector Algebra Angle Between Vectors Projections and Reflections Equality of matrices, Augmented Matrix Matrix Operations Matrix Addition and Matrix Scalar Multiply Matrix Multiply Matrix
More informationReview of linear algebra
Review of linear algebra 1 Vectors and matrices We will just touch very briefly on certain aspects of linear algebra, most of which should be familiar. Recall that we deal with vectors, i.e. elements of
More informationContents. 1 Vectors, Lines and Planes 1. 2 Gaussian Elimination Matrices Vector Spaces and Subspaces 124
Matrices Math 220 Copyright 2016 Pinaki Das This document is freely redistributable under the terms of the GNU Free Documentation License For more information, visit http://wwwgnuorg/copyleft/fdlhtml Contents
More informationLinear 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 informationLinear Algebra Review. Vectors
Linear Algebra Review 9/4/7 Linear Algebra Review By Tim K. Marks UCSD Borrows heavily from: Jana Kosecka http://cs.gmu.edu/~kosecka/cs682.html Virginia de Sa (UCSD) Cogsci 8F Linear Algebra review Vectors
More informationwhich has a check digit of 9. This is consistent with the first nine digits of the ISBN, since
vector Then the check digit c is computed using the following procedure: 1. Form the dot product. 2. Divide by 11, thereby producing a remainder c that is an integer between 0 and 10, inclusive. The check
More informationChapter 2. Ma 322 Fall Ma 322. Sept 23-27
Chapter 2 Ma 322 Fall 2013 Ma 322 Sept 23-27 Summary ˆ Matrices and their Operations. ˆ Special matrices: Zero, Square, Identity. ˆ Elementary Matrices, Permutation Matrices. ˆ Voodoo Principle. What is
More informationCalculus II - Basic Matrix Operations
Calculus II - Basic Matrix Operations Ryan C Daileda Terminology A matrix is a rectangular array of numbers, for example 7,, 7 7 9, or / / /4 / / /4 / / /4 / /6 The numbers in any matrix are called its
More informationAnalysis-3 lecture schemes
Analysis-3 lecture schemes (with Homeworks) 1 Csörgő István November, 2015 1 A jegyzet az ELTE Informatikai Kar 2015. évi Jegyzetpályázatának támogatásával készült Contents 1. Lesson 1 4 1.1. The Space
More informationThis appendix provides a very basic introduction to linear algebra concepts.
APPENDIX Basic Linear Algebra Concepts This appendix provides a very basic introduction to linear algebra concepts. Some of these concepts are intentionally presented here in a somewhat simplified (not
More information7.5 Operations with Matrices. Copyright Cengage Learning. All rights reserved.
7.5 Operations with Matrices Copyright Cengage Learning. All rights reserved. What You Should Learn Decide whether two matrices are equal. Add and subtract matrices and multiply matrices by scalars. Multiply
More informationLinear Algebra & Geometry why is linear algebra useful in computer vision?
Linear Algebra & Geometry why is linear algebra useful in computer vision? References: -Any book on linear algebra! -[HZ] chapters 2, 4 Some of the slides in this lecture are courtesy to Prof. Octavia
More informationx 1 x 2. x 1, x 2,..., x n R. x n
WEEK In general terms, our aim in this first part of the course is to use vector space theory to study the geometry of Euclidean space A good knowledge of the subject matter of the Matrix Applications
More informationSUMMARY OF MATH 1600
SUMMARY OF MATH 1600 Note: The following list is intended as a study guide for the final exam. It is a continuation of the study guide for the midterm. It does not claim to be a comprehensive list. You
More informationb 1 b 2.. b = b m A = [a 1,a 2,...,a n ] where a 1,j a 2,j a j = a m,j Let A R m n and x 1 x 2 x = x n
Lectures -2: Linear Algebra Background Almost all linear and nonlinear problems in scientific computation require the use of linear algebra These lectures review basic concepts in a way that has proven
More informationThis pre-publication material is for review purposes only. Any typographical or technical errors will be corrected prior to publication.
This pre-publication material is for review purposes only. Any typographical or technical errors will be corrected prior to publication. Copyright Pearson Canada Inc. All rights reserved. Copyright Pearson
More informationChapter 8: Polar Coordinates and Vectors
Chapter 8: Polar Coordinates and Vectors 8.1 Polar Coordinates This is another way (in addition to the x-y system) of specifying the position of a point in the plane. We give the distance r of the point
More informationChapter 3 Transformations
Chapter 3 Transformations An Introduction to Optimization Spring, 2014 Wei-Ta Chu 1 Linear Transformations A function is called a linear transformation if 1. for every and 2. for every If we fix the bases
More informationCS123 INTRODUCTION TO COMPUTER GRAPHICS. Linear Algebra /34
Linear Algebra /34 Vectors A vector is a magnitude and a direction Magnitude = v Direction Also known as norm, length Represented by unit vectors (vectors with a length of 1 that point along distinct axes)
More informationWe know how to identify the location of a point by means of coordinates: (x, y) for a point in R 2, or (x, y,z) for a point in R 3.
Vectors We know how to identify the location of a point by means of coordinates: (x, y) for a point in R 2, or (x, y,z) for a point in R 3. More generally, n-dimensional real Euclidean space R n is the
More informationNotation, Matrices, and Matrix Mathematics
Geographic Information Analysis, Second Edition. David O Sullivan and David J. Unwin. 010 John Wiley & Sons, Inc. Published 010 by John Wiley & Sons, Inc. Appendix A Notation, Matrices, and Matrix Mathematics
More informationINNER PRODUCT SPACE. Definition 1
INNER PRODUCT SPACE Definition 1 Suppose u, v and w are all vectors in vector space V and c is any scalar. An inner product space on the vectors space V is a function that associates with each pair of
More informationLinear Algebra I. Ronald van Luijk, 2015
Linear Algebra I Ronald van Luijk, 2015 With many parts from Linear Algebra I by Michael Stoll, 2007 Contents Dependencies among sections 3 Chapter 1. Euclidean space: lines and hyperplanes 5 1.1. Definition
More informationDefinition: A vector is a directed line segment which represents a displacement from one point P to another point Q.
THE UNIVERSITY OF NEW SOUTH WALES SCHOOL OF MATHEMATICS AND STATISTICS MATH Algebra Section : - Introduction to Vectors. You may have already met the notion of a vector in physics. There you will have
More informationVectors Coordinate frames 2D implicit curves 2D parametric curves. Graphics 2008/2009, period 1. Lecture 2: vectors, curves, and surfaces
Graphics 2008/2009, period 1 Lecture 2 Vectors, curves, and surfaces Computer graphics example: Pixar (source: http://www.pixar.com) Computer graphics example: Pixar (source: http://www.pixar.com) Computer
More informationLS.1 Review of Linear Algebra
LS. LINEAR SYSTEMS LS.1 Review of Linear Algebra In these notes, we will investigate a way of handling a linear system of ODE s directly, instead of using elimination to reduce it to a single higher-order
More informationORTHOGONALITY AND LEAST-SQUARES [CHAP. 6]
ORTHOGONALITY AND LEAST-SQUARES [CHAP. 6] Inner products and Norms Inner product or dot product of 2 vectors u and v in R n : u.v = u 1 v 1 + u 2 v 2 + + u n v n Calculate u.v when u = 1 2 2 0 v = 1 0
More informationData Mining and Analysis
978--5-766- - Data Mining and Analysis: Fundamental Concepts and Algorithms CHAPTER Data Mining and Analysis Data mining is the process of discovering insightful, interesting, and novel patterns, as well
More information1111: Linear Algebra I
1111: Linear Algebra I Dr. Vladimir Dotsenko (Vlad) Michaelmas Term 2015 Dr. Vladimir Dotsenko (Vlad) 1111: Linear Algebra I Michaelmas Term 2015 1 / 15 From equations to matrices For example, if we consider
More informationRigid Geometric Transformations
Rigid Geometric Transformations Carlo Tomasi This note is a quick refresher of the geometry of rigid transformations in three-dimensional space, expressed in Cartesian coordinates. 1 Cartesian Coordinates
More informationLarge Scale Data Analysis Using Deep Learning
Large Scale Data Analysis Using Deep Learning Linear Algebra U Kang Seoul National University U Kang 1 In This Lecture Overview of linear algebra (but, not a comprehensive survey) Focused on the subset
More informationAn overview of key ideas
An overview of key ideas This is an overview of linear algebra given at the start of a course on the mathematics of engineering. Linear algebra progresses from vectors to matrices to subspaces. Vectors
More information(Mathematical Operations with Arrays) Applied Linear Algebra in Geoscience Using MATLAB
Applied Linear Algebra in Geoscience Using MATLAB (Mathematical Operations with Arrays) Contents Getting Started Matrices Creating Arrays Linear equations Mathematical Operations with Arrays Using Script
More informationInner Product, Length, and Orthogonality
Inner Product, Length, and Orthogonality Linear Algebra MATH 2076 Linear Algebra,, Chapter 6, Section 1 1 / 13 Algebraic Definition for Dot Product u 1 v 1 u 2 Let u =., v = v 2. be vectors in Rn. The
More informationMatrix Algebra: Definitions and Basic Operations
Section 4 Matrix Algebra: Definitions and Basic Operations Definitions Analyzing economic models often involve working with large sets of linear equations. Matrix algebra provides a set of tools for dealing
More informationChapter 1. Introduction to Vectors. Po-Ning Chen, Professor. Department of Electrical and Computer Engineering. National Chiao Tung University
Chapter 1 Introduction to Vectors Po-Ning Chen, Professor Department of Electrical and Computer Engineering National Chiao Tung University Hsin Chu, Taiwan 30010, R.O.C. Notes for this course 1-1 A few
More informationThe Transpose of a Vector
8 CHAPTER Vectors The Transpose of a Vector We now consider the transpose of a vector in R n, which is a row vector. For a vector u 1 u. u n the transpose is denoted by u T = [ u 1 u u n ] EXAMPLE -5 Find
More informationChapter 12 Review Vector. MATH 126 (Section 9.5) Vector and Scalar The University of Kansas 1 / 30
Chapter 12 Review Vector MATH 126 (Section 9.5) Vector and Scalar The University of Kansas 1 / 30 iclicker 1: Let v = PQ where P = ( 2, 5) and Q = (1, 2). Which of the following vectors with the given
More informationMathematical Foundations: Intro
Mathematical Foundations: Intro Graphics relies on 3 basic objects: 1. Scalars 2. Vectors 3. Points Mathematically defined in terms of spaces: 1. Vector space 2. Affine space 3. Euclidean space Math required:
More informationVectors. (same vector)
Vectors Our very first topic is unusual in that we will start with a brief written presentation. More typically we will begin each topic with a videotaped lecture by Professor Auroux and follow that with
More informationMath 3191 Applied Linear Algebra
Math 191 Applied Linear Algebra Lecture 1: Inner Products, Length, Orthogonality Stephen Billups University of Colorado at Denver Math 191Applied Linear Algebra p.1/ Motivation Not all linear systems have
More informationExtra 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 informationLINEAR SYSTEMS, MATRICES, AND VECTORS
ELEMENTARY LINEAR ALGEBRA WORKBOOK CREATED BY SHANNON MARTIN MYERS LINEAR SYSTEMS, MATRICES, AND VECTORS Now that I ve been teaching Linear Algebra for a few years, I thought it would be great to integrate
More informationCM2202: Scientific Computing and Multimedia Applications Lab Class Week 5. School of Computer Science & Informatics
CM2202: Scientific Computing and Multimedia Applications Lab Class Week 5 School of Computer Science & Informatics Vector operators Definition (Vector Addition, Subtraction and Scalar Multiplication in
More informationLinear Algebra March 16, 2019
Linear Algebra March 16, 2019 2 Contents 0.1 Notation................................ 4 1 Systems of linear equations, and matrices 5 1.1 Systems of linear equations..................... 5 1.2 Augmented
More informationLinear Algebra I for Science (NYC)
Element No. 1: To express concrete problems as linear equations. To solve systems of linear equations using matrices. Topic: MATRICES 1.1 Give the definition of a matrix, identify the elements and the
More informationMATH Linear Algebra
MATH 304 - Linear Algebra In the previous note we learned an important algorithm to produce orthogonal sequences of vectors called the Gramm-Schmidt orthogonalization process. Gramm-Schmidt orthogonalization
More informationLinear Equations in Linear Algebra
1 Linear Equations in Linear Algebra 1.3 VECTOR EQUATIONS VECTOR EQUATIONS Vectors in 2 A matrix with only one column is called a column vector, or simply a vector. An example of a vector with two entries
More informationSection 1.6. M N = [a ij b ij ], (1.6.2)
The Calculus of Functions of Several Variables Section 16 Operations with Matrices In the previous section we saw the important connection between linear functions and matrices In this section we will
More informationThe Matrix Vector Product and the Matrix Product
The Matrix Vector Product and the Matrix Product As we have seen a matrix is just a rectangular array of scalars (real numbers) The size of a matrix indicates its number of rows and columns A matrix with
More informationEGR2013 Tutorial 8. Linear Algebra. Powers of a Matrix and Matrix Polynomial
EGR1 Tutorial 8 Linear Algebra Outline Powers of a Matrix and Matrix Polynomial Vector Algebra Vector Spaces Powers of a Matrix and Matrix Polynomial If A is a square matrix, then we define the nonnegative
More informationEcon Slides from Lecture 7
Econ 205 Sobel Econ 205 - Slides from Lecture 7 Joel Sobel August 31, 2010 Linear Algebra: Main Theory A linear combination of a collection of vectors {x 1,..., x k } is a vector of the form k λ ix i for
More informationj=1 u 1jv 1j. 1/ 2 Lemma 1. An orthogonal set of vectors must be linearly independent.
Lecture Notes: Orthogonal and Symmetric Matrices Yufei Tao Department of Computer Science and Engineering Chinese University of Hong Kong taoyf@cse.cuhk.edu.hk Orthogonal Matrix Definition. Let u = [u
More informationLinear Algebra Massoud Malek
CSUEB Linear Algebra Massoud Malek Inner Product and Normed Space In all that follows, the n n identity matrix is denoted by I n, the n n zero matrix by Z n, and the zero vector by θ n An inner product
More informationMathematics 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 informationChapter 2 - Vector Algebra
A spatial vector, or simply vector, is a concept characterized by a magnitude and a direction, and which sums with other vectors according to the Parallelogram Law. A vector can be thought of as an arrow
More informationMath Bootcamp An p-dimensional vector is p numbers put together. Written as. x 1 x =. x p
Math Bootcamp 2012 1 Review of matrix algebra 1.1 Vectors and rules of operations An p-dimensional vector is p numbers put together. Written as x 1 x =. x p. When p = 1, this represents a point in the
More informationLINEAR ALGEBRA - CHAPTER 1: VECTORS
LINEAR ALGEBRA - CHAPTER 1: VECTORS A game to introduce Linear Algebra In measurement, there are many quantities whose description entirely rely on magnitude, i.e., length, area, volume, mass and temperature.
More informationPOLI270 - Linear Algebra
POLI7 - Linear Algebra Septemer 8th Basics a x + a x +... + a n x n b () is the linear form where a, b are parameters and x n are variables. For a given equation such as x +x you only need a variable and
More information4.3 Equations in 3-space
4.3 Equations in 3-space istance can be used to define functions from a 3-space R 3 to the line R. Let P be a fixed point in the 3-space R 3 (say, with coordinates P (2, 5, 7)). Consider a function f :
More informationMAT1035 Analytic Geometry
MAT1035 Analytic Geometry Lecture Notes R.A. Sabri Kaan Gürbüzer Dokuz Eylül University 2016 2 Contents 1 Review of Trigonometry 5 2 Polar Coordinates 7 3 Vectors in R n 9 3.1 Located Vectors..............................................
More information