Section 4.6 (Rank).

Slides:



Advertisements
Similar presentations
5.4 Basis And Dimension.
Advertisements

5.2 Rank of a Matrix. Set-up Recall block multiplication:
Example: Given a matrix defining a linear mapping Find a basis for the null space and a basis for the range Pamela Leutwyler.
Chapter 3: Linear transformations
Gauss Elimination.
4 4.3 © 2012 Pearson Education, Inc. Vector Spaces LINEARLY INDEPENDENT SETS; BASES.
A quick example calculating the column space and the nullspace of a matrix. Isabel K. Darcy Mathematics Department Applied Math and Computational Sciences.
Eigenvalues and Eigenvectors
THE DIMENSION OF A VECTOR SPACE
Dimension of a Vector Space (11/9/05) Theorem. If the vector space V has a basis consisting of n vectors, then any set of more than n vectors in V must.
Chapter 1 Systems of Linear Equations
4 4.6 © 2012 Pearson Education, Inc. Vector Spaces RANK.
Lecture 10 Dimensions, Independence, Basis and Complete Solution of Linear Systems Shang-Hua Teng.
Subspaces, Basis, Dimension, Rank
Math Dept, Faculty of Applied Science, HCM University of Technology
Square n-by-n Matrix.
Linear Algebra Lecture 25.
4 4.2 © 2012 Pearson Education, Inc. Vector Spaces NULL SPACES, COLUMN SPACES, AND LINEAR TRANSFORMATIONS.
4.6 Matrix Equations and Systems of Linear Equations In this section, you will study matrix equations and how to use them to solve systems of linear equations.
Section 3.6 – Solving Systems Using Matrices
A matrix equation has the same solution set as the vector equation which has the same solution set as the linear system whose augmented matrix is Therefore:
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.
We will use Gauss-Jordan elimination to determine the solution set of this linear system.
Chapter Content Real Vector Spaces Subspaces Linear Independence
5.5 Row Space, Column Space, and Nullspace
4 4.6 © 2012 Pearson Education, Inc. Vector Spaces RANK.
4.6: Rank. Definition: Let A be an mxn matrix. Then each row of A has n entries and can therefore be associated with a vector in The set of all linear.
Section 2.3 Properties of Solution Sets
Vector Spaces RANK © 2016 Pearson Education, Inc..
4.8 Rank Rank enables one to relate matrices to vectors, and vice versa. Definition Let A be an m  n matrix. The rows of A may be viewed as row vectors.
A website that has programs that will do most operations in this course (an online calculator for matrices)
4 © 2012 Pearson Education, Inc. Vector Spaces 4.4 COORDINATE SYSTEMS.
2 2.9 © 2016 Pearson Education, Inc. Matrix Algebra DIMENSION AND RANK.
Solve a system of linear equations By reducing a matrix Pamela Leutwyler.
3.6 Solving Systems Using Matrices You can use a matrix to represent and solve a system of equations without writing the variables. A matrix is a rectangular.
Systems of Linear Equations in Vector Form Prepared by Vince Zaccone For Campus Learning Assistance Services at UCSB.
Arab Open University Faculty of Computer Studies M132: Linear Algebra
4.8 Rank Rank enables one to relate matrices to vectors, and vice versa. Definition Let A be an m  n matrix. The rows of A may be viewed as row vectors.
5 5.1 © 2016 Pearson Education, Ltd. Eigenvalues and Eigenvectors EIGENVECTORS AND EIGENVALUES.
4.5: The Dimension of a Vector Space. Theorem 9 If a vector space V has a basis, then any set in V containing more than n vectors must be linearly dependent.
4 4.5 © 2016 Pearson Education, Inc. Vector Spaces THE DIMENSION OF A VECTOR SPACE.
4 4.2 © 2016 Pearson Education, Inc. Vector Spaces NULL SPACES, COLUMN SPACES, AND LINEAR TRANSFORMATIONS.
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.
Chapter 5 Chapter Content 1. Real Vector Spaces 2. Subspaces 3. Linear Independence 4. Basis and Dimension 5. Row Space, Column Space, and Nullspace 6.
1 1.4 Linear Equations in Linear Algebra THE MATRIX EQUATION © 2016 Pearson Education, Ltd.
Def: A matrix A in reduced-row-echelon form if 1)A is row-echelon form 2)All leading entries = 1 3)A column containing a leading entry 1 has 0’s everywhere.
Reduced echelon form Matrix equations Null space Range Determinant Invertibility Similar matrices Eigenvalues Eigenvectors Diagonabilty Power.
 Matrix Operations  Inverse of a Matrix  Characteristics of Invertible Matrices …
Section 5.6 Rank and Nullity. FOUR FUNDAMENTAL MATRIX SPACES If we consider matrices A and A T together, then there are six vector spaces of interest:
REVIEW Linear Combinations Given vectors and given scalars
1.4 The Matrix Equation Ax = b
7.3 Linear Systems of Equations. Gauss Elimination
Row Space, Column Space, and Nullspace
Linear Algebra Lecture 4.
4.6: Rank.
What can we know from RREF?
Linear Algebra Lecture 21.
7.5 Solutions of Linear Systems:
Affine Spaces Def: Suppose
Mathematics for Signals and Systems
Row-equivalences again
Linear Algebra Lecture 24.
Maths for Signals and Systems Linear Algebra in Engineering Lectures 4-5, Tuesday 18th October 2016 DR TANIA STATHAKI READER (ASSOCIATE PROFFESOR) IN.
Linear Algebra Lecture 6.
Row-equivalences again
Eigenvalues and Eigenvectors
Vector Spaces RANK © 2012 Pearson Education, Inc..
THE DIMENSION OF A VECTOR SPACE
Null Spaces, Column Spaces, and Linear Transformations
NULL SPACES, COLUMN SPACES, AND LINEAR TRANSFORMATIONS
Presentation transcript:

Section 4.6 (Rank)

Recap Dimension Dimension: # of vectors in basis dim Col A – number of pivot cols of A dim Nul A - # free variables in A (or number of non pivot cols of A) Note: dim Col A + dim Nul A = n

Row Space DEFINITION: The set of all linear combinations of the row vectors of a matrix A, denoted by Row A

Example Row A = span {r1, r2, r3} Where r1=(-1, 2, 3, 6) Note: Row A = Col AT

Row A Observations When we use row operations to reduce Matrix A to Matrix B we are taking linear combinations of the rows of A to come up with B. Recall Row A = span {r1, r2, r3} = all linear combinations of the rows of A This leads to the following Theorem

Theorem If two matrices A and B are row equivalent then, Row A = Row B If B in echelon form, the nonzero rows of B form a basis for Row A & Row B

Example are row equivalent. Find a basis for row space, column space and null space of A. Also state the dimension of each. Very similar to the test question for this section

Rank DEFINITION: All are equivalent dim Col A # of pivot columns of A dim Row A # nonzero rows in reduced matrix of A Rank A

Rank Property Since Row A = Col AT We Have dim Row A = dim Col AT Rank A = Rank AT

Rank Theorem For Amxn Rank A + dim Nul A = n OR # of pivot columns + # of non pivot columns = n

Full Rank For Amxn Rank A is as large as possible OR Rank = min(m,n)

Full Rank Example Given A5x7 with 5 pivot columns, is A of full rank? dim Col A = Rank A=5 = min(5,7) A is of Full Rank Pivot in each row

Full Rank Observation For Amxn and m≤n (see 4.6 #26 for m>n) Full Rank Pivot in each row Columns of A Span RM There is a solution for all b in Ax=b Important in Statistics

Section 4.6 Examples EXAMPLE: Suppose that a 5 x 8 matrix A has rank 5. Find dim Nul A, dim Row A and rank AT. Is Col A= R5? EXAMPLE: For a 9 x 12 matrix A, find the smallest possible value of dim Nul A.

Section 4.6 Example The Rank Theorem provides us with a powerful tool for determining information about a system of equations. EXAMPLE: A scientist solves a nonhomogeneous system of 50 equations in 54 variables and finds that exactly 4 of the unknowns are free variables. Can the scientist be certain that any associated nonhomogeneous system (with the same coefficients) has a solution? Very similar to the test question for this section

Additions to IMT For Amxn The columns of A form a basis of Rn Col A = Rn dim Col A = n Rank A = n Nul A = {0} dim Nul A = 0