Linear Algebra Lecture 23.

Slides:



Advertisements
Similar presentations
Ordinary Least-Squares
Advertisements

8.3 Inverse Linear Transformations
Chapter 3: Linear transformations
Fun with Vectors. Definition A vector is a quantity that has both magnitude and direction Examples?
Linear Equations in Linear Algebra
THE DIMENSION OF A VECTOR SPACE
Coordinate Systems (11/4/05) It turns out that every vector space V which has a finite basis can be “realized” as one of the spaces R n as soon as we pick.
Chapter 4 Linear Transformations. Outlines Definition and Examples Matrix Representation of linear transformation Similarity.
Elementary Linear Algebra Anton & Rorres, 9 th Edition Lecture Set – 08 Chapter 8: Linear Transformations.
Linear Equations in Linear Algebra
Subspaces, Basis, Dimension, Rank
Mathematics1 Mathematics 1 Applied Informatics Štefan BEREŽNÝ.
4.1 Vector Spaces and Subspaces 4.2 Null Spaces, Column Spaces, and Linear Transformations 4.3 Linearly Independent Sets; Bases 4.4 Coordinate systems.
Sections 1.8/1.9: Linear Transformations
4 4.4 © 2012 Pearson Education, Inc. Vector Spaces COORDINATE SYSTEMS.
A vector space containing infinitely many vectors can be efficiently described by listing a set of vectors that SPAN the space. eg: describe the solutions.
Orthogonality and Least Squares
Elementary Linear Algebra Anton & Rorres, 9th Edition
LAHW#12 Due December 13, Bases and Dimension 42. –Criticize this argument: We have three vectors u 1 = (1, 3, 2), u 2 = (-2, 4, 5), and u 3.
Linear Algebra Diyako Ghaderyan 1 Contents:  Linear Equations in Linear Algebra  Matrix Algebra  Determinants  Vector Spaces  Eigenvalues.
4.3 Linearly Independent Sets; Bases
Linear Algebra Diyako Ghaderyan 1 Contents:  Linear Equations in Linear Algebra  Matrix Algebra  Determinants  Vector Spaces  Eigenvalues.
5.1 Eigenvectors and Eigenvalues 5. Eigenvalues and Eigenvectors.
Chapter 4 Vector Spaces Linear Algebra. Ch04_2 Definition 1: ……………………………………………………………………. The elements in R n called …………. 4.1 The vector Space R n Addition.
4 4.5 © 2016 Pearson Education, Inc. Vector Spaces THE DIMENSION OF A VECTOR SPACE.
4 Vector Spaces 4.1 Vector Spaces and Subspaces 4.2 Null Spaces, Column Spaces, and Linear Transformations 4.3 Linearly Independent Sets; Bases 4.4 Coordinate.
Sec Sec Sec 4.4 CHAPTER 4 Vector Spaces Let V be a set of elements with vector addition and multiplication by scalar is a vector space if these.
Graphics Graphics Korea University kucg.korea.ac.kr Mathematics for Computer Graphics 고려대학교 컴퓨터 그래픽스 연구실.
 Matrix Operations  Inverse of a Matrix  Characteristics of Invertible Matrices …
To understand the matrix factorization A=PDP -1 as a statement about linear transformation.
REVIEW Linear Combinations Given vectors and given scalars
8.1 General Linear Transformation
Linear Equations in Linear Algebra
Linear Algebra Lecture 26.
Linear Algebra Lecture 19.
Linear Algebra Lecture 36.
ECE 638: Principles of Digital Color Imaging Systems
Vectors, Linear Combinations and Linear Independence
Linear Algebra Lecture 22.
Coordinate System Hung-yi Lee.
Linear Equations in Linear Algebra
Elementary Linear Algebra
Linear Algebra Chapter 4 Vector Spaces.
1.3 Vector Equations.
Linear Algebra Lecture 40.
Linear Algebra Lecture 39.
Determinants CHANGE OF BASIS © 2016 Pearson Education, Inc.
Linear Algebra Lecture 21.
Linear Algebra Lecture 38.
Theorems about LINEAR MAPPINGS.
Linear Algebra Lecture 10.
Elementary Linear Algebra Anton & Rorres, 9th Edition
Elementary Linear Algebra
Linear Algebra Lecture 32.
Elementary Linear Algebra Anton & Rorres, 9th Edition
Linear Algebra Lecture 24.
Linear Algebra Lecture 20.
Linear Algebra Lecture 6.
Linear Algebra Lecture 9.
Linear Algebra Lecture 41.
Linear Equations and Vectors
Vector Spaces, Subspaces
Linear Algebra Lecture 35.
Eigenvalues and Eigenvectors
THE DIMENSION OF A VECTOR SPACE
NULL SPACES, COLUMN SPACES, AND LINEAR TRANSFORMATIONS
Linear Algebra Lecture 28.
Vector Spaces COORDINATE SYSTEMS © 2012 Pearson Education, Inc.
Linear Equations in Linear Algebra
Subspace Hung-yi Lee Think: beginning subspace.
Presentation transcript:

Linear Algebra Lecture 23

Vector Spaces

Coordinate Systems

basis for a vector space V. Then for each x in V, there Theorem Let B = { b1, … , bn } 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

Definition Suppose the set B = {b1, …, bn} is a basis for V and x is in V. The coordinates of x relative to the basis B (or the B-coordinates of x) are the weights c1, … , cn such that

If c1,c2,…,cn are the B-Coordinates of x, then the vector in Rn is the coordinate of x (relative to B) or the B-coordinate vector of x. The mapping x  [x]B is the coordinate mapping (determined by B)

Suppose an x in R2 has the coordinate vector Example 1 Consider a basis B = {b1, b2} for R2, where Suppose an x in R2 has the coordinate vector Find x.

Solution

Example 2

Let S = {v1, v2, v3} be the basis for R3, where v1 =(1, 2, 1), Example 3 Let S = {v1, v2, v3} be the basis for R3, where v1 =(1, 2, 1), v2 = (2, 9, 0), v3 = (3, 3, 4). …

(a) Find the coordinates vector of v = (5, -1, 9) with respect to S. (b) Find the vector v in R3 whose coordinate vector with respect to the basis S is [v]s = (-1, 3, 2)

Find the coordinates vector of the polynomial p = a0 + a1x + a2x2 Example 4 Find the coordinates vector of the polynomial p = a0 + a1x + a2x2 relative to the basis S = {1, x, x2} for p2.

Find the coordinate vector of A relative to the basis Example 5 Find the coordinate vector of A relative to the basis S = {A1, A2, A3, A4}, where

A Graphical Interpretation of Coordinates

Example 6

Find the coordinate vector [x]B of x relative to B. Example 7 Find the coordinate vector [x]B of x relative to B.

is a one-to-one linear transformation from V onto Rn Theorem Let B = {b1, … , bn} be a basis for a vector space V. Then the coordinate mapping is a one-to-one linear transformation from V onto Rn

Examples

Linear Algebra Lecture 23