SlideShare a Scribd company logo
1 of 16
Download to read offline
Projector And Projection Onto Subspaces
Numerical Linear Algebra
Isaac Amornortey Yowetu
NIMS-GHANA
July 20, 2020
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
Outline
1 problem of the Day
2 Projection onto a Line
3 Projection Matrix
4 Projection onto Subspace
Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
Problem of the Day
Consider the matrix
A =


1 0
0 1
1 0


What is the orthogonal projection onto the range(A) and what
is the image under P of the vector (1, 2, 3)∗
?
Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
Graphical Example
Figure: By Nicholas Longo
Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
Example 1
Let
v =
−2
3
and u =
−1
1
What is the orthogonal projector P onto range(u), and what is
the image under P of the vector (v) ?
Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
Solution
Projection Matrix
Here we would like to find a projection matrix P:
P = P2
and PT
= P
P =
uuT
uT u
proju(v) = Pv =
uuT
uT u
v
Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
Solution Continue...
P =
uuT
uT u
u · uT
=
−1
1
(−1 1) =
1 −1
−1 1
uT
· u = (−1 1)
−1
1
= 2
∴ P =
1
2
1 −1
−1 1
=
0.5 −0.5
−0.5 0.5
Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
proju(v) = Pv =
0.5 −0.5
−0.5 0.5
−2
3
=
−2.5
2.5
Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
Problem of the Day
Consider the matrix
A =


1 0
0 1
1 0


What is the orthogonal projection onto the range(A) and what
is the image under P of the vector (1, 2, 3)∗
?
Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
Solution
Projection Matrix
Here we would like to find a projection matrix P:
P = P2
and PT
= P
P =
uuT
uT u
But considering u to be matrix A, then:
projA(v) = Pv =
AAT
AT A
v
Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
Solution Continue...
Remark
But we don’t divide matrices. Hence, we will then have
inverse instead of matrix division.
We can consider multiplying the matrix A by the
pseudo-inverse to get matrix P.
Pseudo-inverse = (AT
A)−1
AT
Then,
P = A(AT
A)−1
AT
Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
Solution Continue...
AT
A =
1 0 1
0 1 0


1 0
0 1
1 0

 =
2 0
0 1
(AT
A)−1
=
0.5 0
0 1
(AT
A)−1
AT
=
0.5 0
0 1
1 0 1
0 1 0
=
0.5 0 0.5
0 1 0
A(AT
A)−1
AT
=


1 0
0 1
1 0

 0.5 0 0.5
0 1 0
=


0.5 0 0.5
0 1 0
0.5 0 0.5


Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
Considering
P =


0.5 0 0.5
0 1 0
0.5 0 0.5

 and


1
2
3


Then,
Pv =


0.5 0 0.5
0 1 0
0.5 0 0.5




1
2
3

 =


2
2
2


Conclusion: P is our orthogonal Projection onto Range(A) and
(2, 2, 2)T
is the image under P of the vector(v).
Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
Example 2
Let A be the 2-dimensional subspace of R3
spanned by the
orthogonal vectors u1 = (1, 0, 1) and u2 = (0, 1, 0). Write the
vector v = (1, 2, 3) as the sum of vector in A and a vector
orthogonal to A.
Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
Solution
Let
x = proju1 v + proju2 v (1)
x = λu1 + βu2 (2)
x =
u1 · v
u1 · u1
u1 +
u2 · v
u2 · u2
u2 (3)
x =


1
0
1

 ·


1
2
3




1
0
1

 ·


1
0
1




1
0
1

 +


0
1
0

 ·


1
2
3




0
1
0

 ·


0
1
0




0
1
0

 (4)
Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces
problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace
Solution Continue...
x = 2


1
0
1

 + 2


0
1
0

 =


2
2
2

 (5)
v ⊥ A =


1
2
3

 −


2
2
2

 =


−1
0
1

 (6)
Conclusion: Vector x is our sum of vector v in A and (v ⊥ A)
is vector orthogonal to A.
Isaac Amornortey Yowetu NIMS-GHANA
Projector And Projection Onto Subspaces

More Related Content

What's hot

TAO Fayan_X-Ray and MIP volume rendering
TAO Fayan_X-Ray and MIP volume renderingTAO Fayan_X-Ray and MIP volume rendering
TAO Fayan_X-Ray and MIP volume renderingFayan TAO
 
Independent Functions of Euler Totient Cayley Graph
Independent Functions of Euler Totient Cayley GraphIndependent Functions of Euler Totient Cayley Graph
Independent Functions of Euler Totient Cayley Graphijceronline
 
Image trnsformations
Image trnsformationsImage trnsformations
Image trnsformationsJohn Williams
 
Nonconvex Compressed Sensing with the Sum-of-Squares Method
Nonconvex Compressed Sensing with the Sum-of-Squares MethodNonconvex Compressed Sensing with the Sum-of-Squares Method
Nonconvex Compressed Sensing with the Sum-of-Squares MethodTasuku Soma
 
Visualizing Data Using t-SNE
Visualizing Data Using t-SNEVisualizing Data Using t-SNE
Visualizing Data Using t-SNEDavid Khosid
 
Regret Minimization in Multi-objective Submodular Function Maximization
Regret Minimization in Multi-objective Submodular Function MaximizationRegret Minimization in Multi-objective Submodular Function Maximization
Regret Minimization in Multi-objective Submodular Function MaximizationTasuku Soma
 
Stochastic Frank-Wolfe for Constrained Finite Sum Minimization @ Montreal Opt...
Stochastic Frank-Wolfe for Constrained Finite Sum Minimization @ Montreal Opt...Stochastic Frank-Wolfe for Constrained Finite Sum Minimization @ Montreal Opt...
Stochastic Frank-Wolfe for Constrained Finite Sum Minimization @ Montreal Opt...Geoffrey Négiar
 
High Dimensional Data Visualization using t-SNE
High Dimensional Data Visualization using t-SNEHigh Dimensional Data Visualization using t-SNE
High Dimensional Data Visualization using t-SNEKai-Wen Zhao
 
Iterative Compression
Iterative CompressionIterative Compression
Iterative CompressionASPAK2014
 
Paths and Polynomials
Paths and PolynomialsPaths and Polynomials
Paths and PolynomialsASPAK2014
 
Lec-08 Feature Aggregation II: Fisher Vector, AKULA and Super Vector
Lec-08 Feature Aggregation II: Fisher Vector, AKULA and Super VectorLec-08 Feature Aggregation II: Fisher Vector, AKULA and Super Vector
Lec-08 Feature Aggregation II: Fisher Vector, AKULA and Super VectorUnited States Air Force Academy
 
Markov chain monte_carlo_methods_for_machine_learning
Markov chain monte_carlo_methods_for_machine_learningMarkov chain monte_carlo_methods_for_machine_learning
Markov chain monte_carlo_methods_for_machine_learningAndres Mendez-Vazquez
 
SPDE presentation 2012
SPDE presentation 2012SPDE presentation 2012
SPDE presentation 2012Zheng Mengdi
 
05 history of cv a machine learning (theory) perspective on computer vision
05  history of cv a machine learning (theory) perspective on computer vision05  history of cv a machine learning (theory) perspective on computer vision
05 history of cv a machine learning (theory) perspective on computer visionzukun
 
Nonlinear Manifolds in Computer Vision
Nonlinear Manifolds in Computer VisionNonlinear Manifolds in Computer Vision
Nonlinear Manifolds in Computer Visionzukun
 
Patch Matching with Polynomial Exponential Families and Projective Divergences
Patch Matching with Polynomial Exponential Families and Projective DivergencesPatch Matching with Polynomial Exponential Families and Projective Divergences
Patch Matching with Polynomial Exponential Families and Projective DivergencesFrank Nielsen
 
fourier series and fourier transform
fourier series and fourier transformfourier series and fourier transform
fourier series and fourier transformVikas Rathod
 

What's hot (20)

TAO Fayan_X-Ray and MIP volume rendering
TAO Fayan_X-Ray and MIP volume renderingTAO Fayan_X-Ray and MIP volume rendering
TAO Fayan_X-Ray and MIP volume rendering
 
Independent Functions of Euler Totient Cayley Graph
Independent Functions of Euler Totient Cayley GraphIndependent Functions of Euler Totient Cayley Graph
Independent Functions of Euler Totient Cayley Graph
 
Image trnsformations
Image trnsformationsImage trnsformations
Image trnsformations
 
Nonconvex Compressed Sensing with the Sum-of-Squares Method
Nonconvex Compressed Sensing with the Sum-of-Squares MethodNonconvex Compressed Sensing with the Sum-of-Squares Method
Nonconvex Compressed Sensing with the Sum-of-Squares Method
 
Visualizing Data Using t-SNE
Visualizing Data Using t-SNEVisualizing Data Using t-SNE
Visualizing Data Using t-SNE
 
Regret Minimization in Multi-objective Submodular Function Maximization
Regret Minimization in Multi-objective Submodular Function MaximizationRegret Minimization in Multi-objective Submodular Function Maximization
Regret Minimization in Multi-objective Submodular Function Maximization
 
Image restoration and reconstruction
Image restoration and reconstructionImage restoration and reconstruction
Image restoration and reconstruction
 
Stochastic Frank-Wolfe for Constrained Finite Sum Minimization @ Montreal Opt...
Stochastic Frank-Wolfe for Constrained Finite Sum Minimization @ Montreal Opt...Stochastic Frank-Wolfe for Constrained Finite Sum Minimization @ Montreal Opt...
Stochastic Frank-Wolfe for Constrained Finite Sum Minimization @ Montreal Opt...
 
High Dimensional Data Visualization using t-SNE
High Dimensional Data Visualization using t-SNEHigh Dimensional Data Visualization using t-SNE
High Dimensional Data Visualization using t-SNE
 
Iterative Compression
Iterative CompressionIterative Compression
Iterative Compression
 
Paths and Polynomials
Paths and PolynomialsPaths and Polynomials
Paths and Polynomials
 
Lec-08 Feature Aggregation II: Fisher Vector, AKULA and Super Vector
Lec-08 Feature Aggregation II: Fisher Vector, AKULA and Super VectorLec-08 Feature Aggregation II: Fisher Vector, AKULA and Super Vector
Lec-08 Feature Aggregation II: Fisher Vector, AKULA and Super Vector
 
Lec17 sparse signal processing & applications
Lec17 sparse signal processing & applicationsLec17 sparse signal processing & applications
Lec17 sparse signal processing & applications
 
Markov chain monte_carlo_methods_for_machine_learning
Markov chain monte_carlo_methods_for_machine_learningMarkov chain monte_carlo_methods_for_machine_learning
Markov chain monte_carlo_methods_for_machine_learning
 
SPDE presentation 2012
SPDE presentation 2012SPDE presentation 2012
SPDE presentation 2012
 
05 history of cv a machine learning (theory) perspective on computer vision
05  history of cv a machine learning (theory) perspective on computer vision05  history of cv a machine learning (theory) perspective on computer vision
05 history of cv a machine learning (theory) perspective on computer vision
 
Nonlinear Manifolds in Computer Vision
Nonlinear Manifolds in Computer VisionNonlinear Manifolds in Computer Vision
Nonlinear Manifolds in Computer Vision
 
Patch Matching with Polynomial Exponential Families and Projective Divergences
Patch Matching with Polynomial Exponential Families and Projective DivergencesPatch Matching with Polynomial Exponential Families and Projective Divergences
Patch Matching with Polynomial Exponential Families and Projective Divergences
 
fourier series and fourier transform
fourier series and fourier transformfourier series and fourier transform
fourier series and fourier transform
 
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
 

Similar to Projectors and Projection Onto Subspaces

Projectors and Projection Onto a Line
Projectors and Projection Onto a LineProjectors and Projection Onto a Line
Projectors and Projection Onto a LineIsaac Yowetu
 
PROJECT EVERYTHNG
PROJECT EVERYTHNGPROJECT EVERYTHNG
PROJECT EVERYTHNGFalade John
 
Projection.pdf is CAD CAM engg is that the
Projection.pdf is CAD CAM engg is that theProjection.pdf is CAD CAM engg is that the
Projection.pdf is CAD CAM engg is that theGunjanKolhe5
 
Ortographic projection - ENGINEERING DRAWING/GRAPHICS
Ortographic projection - ENGINEERING DRAWING/GRAPHICSOrtographic projection - ENGINEERING DRAWING/GRAPHICS
Ortographic projection - ENGINEERING DRAWING/GRAPHICSAbhishek Kandare
 
Putting Objects in Perspective
Putting Objects in PerspectivePutting Objects in Perspective
Putting Objects in Perspectiveuisp dsin
 
Orthogonal porjection in statistics
Orthogonal porjection in statisticsOrthogonal porjection in statistics
Orthogonal porjection in statisticsSahidul Islam
 
Conversion of Pictorial View into Orthographic Views.ppt
Conversion of Pictorial View into Orthographic Views.pptConversion of Pictorial View into Orthographic Views.ppt
Conversion of Pictorial View into Orthographic Views.pptNjokuGabriel1
 
A Novel Methodology for Designing Linear Phase IIR Filters
A Novel Methodology for Designing Linear Phase IIR FiltersA Novel Methodology for Designing Linear Phase IIR Filters
A Novel Methodology for Designing Linear Phase IIR FiltersIDES Editor
 
Vcla - Inner Products
Vcla - Inner ProductsVcla - Inner Products
Vcla - Inner ProductsPreetshah1212
 
Optical sensing techniques and signal processing 5
Optical sensing techniques and signal processing 5Optical sensing techniques and signal processing 5
Optical sensing techniques and signal processing 5ali alavi
 
Transformations
TransformationsTransformations
Transformationsestelav
 
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
3. Linear Algebra for Machine Learning: Factorization and Linear TransformationsCeni Babaoglu, PhD
 
JAISTサマースクール2016「脳を知るための理論」講義01 Single neuron models
JAISTサマースクール2016「脳を知るための理論」講義01 Single neuron modelsJAISTサマースクール2016「脳を知るための理論」講義01 Single neuron models
JAISTサマースクール2016「脳を知るための理論」講義01 Single neuron modelshirokazutanaka
 

Similar to Projectors and Projection Onto Subspaces (20)

Projectors and Projection Onto a Line
Projectors and Projection Onto a LineProjectors and Projection Onto a Line
Projectors and Projection Onto a Line
 
PROJECT EVERYTHNG
PROJECT EVERYTHNGPROJECT EVERYTHNG
PROJECT EVERYTHNG
 
Projection.pdf is CAD CAM engg is that the
Projection.pdf is CAD CAM engg is that theProjection.pdf is CAD CAM engg is that the
Projection.pdf is CAD CAM engg is that the
 
Ortographic projection - ENGINEERING DRAWING/GRAPHICS
Ortographic projection - ENGINEERING DRAWING/GRAPHICSOrtographic projection - ENGINEERING DRAWING/GRAPHICS
Ortographic projection - ENGINEERING DRAWING/GRAPHICS
 
Lec-3 DIP.pptx
Lec-3 DIP.pptxLec-3 DIP.pptx
Lec-3 DIP.pptx
 
Putting Objects in Perspective
Putting Objects in PerspectivePutting Objects in Perspective
Putting Objects in Perspective
 
Orthogonal porjection in statistics
Orthogonal porjection in statisticsOrthogonal porjection in statistics
Orthogonal porjection in statistics
 
Conversion of Pictorial View into Orthographic Views.ppt
Conversion of Pictorial View into Orthographic Views.pptConversion of Pictorial View into Orthographic Views.ppt
Conversion of Pictorial View into Orthographic Views.ppt
 
Theory of conformal optics
Theory of conformal opticsTheory of conformal optics
Theory of conformal optics
 
A Novel Methodology for Designing Linear Phase IIR Filters
A Novel Methodology for Designing Linear Phase IIR FiltersA Novel Methodology for Designing Linear Phase IIR Filters
A Novel Methodology for Designing Linear Phase IIR Filters
 
Vcla - Inner Products
Vcla - Inner ProductsVcla - Inner Products
Vcla - Inner Products
 
Signals and Systems Assignment Help
Signals and Systems Assignment HelpSignals and Systems Assignment Help
Signals and Systems Assignment Help
 
Lect21
Lect21Lect21
Lect21
 
Vector space
Vector spaceVector space
Vector space
 
Optical sensing techniques and signal processing 5
Optical sensing techniques and signal processing 5Optical sensing techniques and signal processing 5
Optical sensing techniques and signal processing 5
 
Transformations
TransformationsTransformations
Transformations
 
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
 
Projection
ProjectionProjection
Projection
 
01 ray-optics-mm
01 ray-optics-mm01 ray-optics-mm
01 ray-optics-mm
 
JAISTサマースクール2016「脳を知るための理論」講義01 Single neuron models
JAISTサマースクール2016「脳を知るための理論」講義01 Single neuron modelsJAISTサマースクール2016「脳を知るための理論」講義01 Single neuron models
JAISTサマースクール2016「脳を知るための理論」講義01 Single neuron models
 

More from Isaac Yowetu

Inverse-power-method.pdf
Inverse-power-method.pdfInverse-power-method.pdf
Inverse-power-method.pdfIsaac Yowetu
 
Approximating Dominant Eivenvalue By The Power Method
Approximating Dominant Eivenvalue By The Power MethodApproximating Dominant Eivenvalue By The Power Method
Approximating Dominant Eivenvalue By The Power MethodIsaac Yowetu
 
Singular Value Decompostion (SVD): Worked example 3
Singular Value Decompostion (SVD): Worked example 3Singular Value Decompostion (SVD): Worked example 3
Singular Value Decompostion (SVD): Worked example 3Isaac Yowetu
 
Singular Value Decompostion (SVD): Worked example 2
Singular Value Decompostion (SVD): Worked example 2Singular Value Decompostion (SVD): Worked example 2
Singular Value Decompostion (SVD): Worked example 2Isaac Yowetu
 
Singular Value Decompostion (SVD): Worked example 1
Singular Value Decompostion (SVD): Worked example 1Singular Value Decompostion (SVD): Worked example 1
Singular Value Decompostion (SVD): Worked example 1Isaac Yowetu
 
Singular Value Decompostion (SVD)
Singular Value Decompostion (SVD)Singular Value Decompostion (SVD)
Singular Value Decompostion (SVD)Isaac Yowetu
 
Cayley-Hamilton Theorem, Eigenvalues, Eigenvectors and Eigenspace.
Cayley-Hamilton Theorem, Eigenvalues, Eigenvectors and Eigenspace.Cayley-Hamilton Theorem, Eigenvalues, Eigenvectors and Eigenspace.
Cayley-Hamilton Theorem, Eigenvalues, Eigenvectors and Eigenspace.Isaac Yowetu
 
Givens rotation method
Givens rotation methodGivens rotation method
Givens rotation methodIsaac Yowetu
 
Sherman-Morrison Formula Proof
Sherman-Morrison Formula ProofSherman-Morrison Formula Proof
Sherman-Morrison Formula ProofIsaac Yowetu
 
Householder transformation | Householder Reflection with QR Decomposition
Householder transformation | Householder Reflection with QR DecompositionHouseholder transformation | Householder Reflection with QR Decomposition
Householder transformation | Householder Reflection with QR DecompositionIsaac Yowetu
 
Gram-Schmidt and QR Decomposition (Factorization) of Matrices
Gram-Schmidt and QR Decomposition (Factorization) of MatricesGram-Schmidt and QR Decomposition (Factorization) of Matrices
Gram-Schmidt and QR Decomposition (Factorization) of MatricesIsaac Yowetu
 
Gram schmidt orthogonalization | Orthonormal Process
Gram schmidt orthogonalization | Orthonormal Process Gram schmidt orthogonalization | Orthonormal Process
Gram schmidt orthogonalization | Orthonormal Process Isaac Yowetu
 
Regula Falsi (False position) Method
Regula Falsi (False position) MethodRegula Falsi (False position) Method
Regula Falsi (False position) MethodIsaac Yowetu
 
Secant Iterative method
Secant Iterative methodSecant Iterative method
Secant Iterative methodIsaac Yowetu
 
Newton Raphson iterative Method
Newton Raphson iterative MethodNewton Raphson iterative Method
Newton Raphson iterative MethodIsaac Yowetu
 
Fixed point iteration
Fixed point iterationFixed point iteration
Fixed point iterationIsaac Yowetu
 

More from Isaac Yowetu (18)

Inverse-power-method.pdf
Inverse-power-method.pdfInverse-power-method.pdf
Inverse-power-method.pdf
 
Approximating Dominant Eivenvalue By The Power Method
Approximating Dominant Eivenvalue By The Power MethodApproximating Dominant Eivenvalue By The Power Method
Approximating Dominant Eivenvalue By The Power Method
 
Singular Value Decompostion (SVD): Worked example 3
Singular Value Decompostion (SVD): Worked example 3Singular Value Decompostion (SVD): Worked example 3
Singular Value Decompostion (SVD): Worked example 3
 
Singular Value Decompostion (SVD): Worked example 2
Singular Value Decompostion (SVD): Worked example 2Singular Value Decompostion (SVD): Worked example 2
Singular Value Decompostion (SVD): Worked example 2
 
Singular Value Decompostion (SVD): Worked example 1
Singular Value Decompostion (SVD): Worked example 1Singular Value Decompostion (SVD): Worked example 1
Singular Value Decompostion (SVD): Worked example 1
 
Singular Value Decompostion (SVD)
Singular Value Decompostion (SVD)Singular Value Decompostion (SVD)
Singular Value Decompostion (SVD)
 
Cayley-Hamilton Theorem, Eigenvalues, Eigenvectors and Eigenspace.
Cayley-Hamilton Theorem, Eigenvalues, Eigenvectors and Eigenspace.Cayley-Hamilton Theorem, Eigenvalues, Eigenvectors and Eigenspace.
Cayley-Hamilton Theorem, Eigenvalues, Eigenvectors and Eigenspace.
 
Givens rotation method
Givens rotation methodGivens rotation method
Givens rotation method
 
Sherman-Morrison Formula Proof
Sherman-Morrison Formula ProofSherman-Morrison Formula Proof
Sherman-Morrison Formula Proof
 
Householder transformation | Householder Reflection with QR Decomposition
Householder transformation | Householder Reflection with QR DecompositionHouseholder transformation | Householder Reflection with QR Decomposition
Householder transformation | Householder Reflection with QR Decomposition
 
Gram-Schmidt and QR Decomposition (Factorization) of Matrices
Gram-Schmidt and QR Decomposition (Factorization) of MatricesGram-Schmidt and QR Decomposition (Factorization) of Matrices
Gram-Schmidt and QR Decomposition (Factorization) of Matrices
 
Gram schmidt orthogonalization | Orthonormal Process
Gram schmidt orthogonalization | Orthonormal Process Gram schmidt orthogonalization | Orthonormal Process
Gram schmidt orthogonalization | Orthonormal Process
 
Regula Falsi (False position) Method
Regula Falsi (False position) MethodRegula Falsi (False position) Method
Regula Falsi (False position) Method
 
Bisection method
Bisection methodBisection method
Bisection method
 
Secant Iterative method
Secant Iterative methodSecant Iterative method
Secant Iterative method
 
Aitken's Method
Aitken's MethodAitken's Method
Aitken's Method
 
Newton Raphson iterative Method
Newton Raphson iterative MethodNewton Raphson iterative Method
Newton Raphson iterative Method
 
Fixed point iteration
Fixed point iterationFixed point iteration
Fixed point iteration
 

Recently uploaded

Solving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxSolving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxOH TEIK BIN
 
Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactdawncurless
 
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxPOINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxSayali Powar
 
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...Marc Dusseiller Dusjagr
 
PSYCHIATRIC History collection FORMAT.pptx
PSYCHIATRIC   History collection FORMAT.pptxPSYCHIATRIC   History collection FORMAT.pptx
PSYCHIATRIC History collection FORMAT.pptxPoojaSen20
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxheathfieldcps1
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13Steve Thomason
 
Science 7 - LAND and SEA BREEZE and its Characteristics
Science 7 - LAND and SEA BREEZE and its CharacteristicsScience 7 - LAND and SEA BREEZE and its Characteristics
Science 7 - LAND and SEA BREEZE and its CharacteristicsKarinaGenton
 
Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxIntroduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxpboyjonauth
 
APM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAPM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAssociation for Project Management
 
Concept of Vouching. B.Com(Hons) /B.Compdf
Concept of Vouching. B.Com(Hons) /B.CompdfConcept of Vouching. B.Com(Hons) /B.Compdf
Concept of Vouching. B.Com(Hons) /B.CompdfUmakantAnnand
 
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...EduSkills OECD
 
Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991
Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991
Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991RKavithamani
 
Micromeritics - Fundamental and Derived Properties of Powders
Micromeritics - Fundamental and Derived Properties of PowdersMicromeritics - Fundamental and Derived Properties of Powders
Micromeritics - Fundamental and Derived Properties of PowdersChitralekhaTherkar
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeThiyagu K
 
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdfssuser54595a
 
MENTAL STATUS EXAMINATION format.docx
MENTAL     STATUS EXAMINATION format.docxMENTAL     STATUS EXAMINATION format.docx
MENTAL STATUS EXAMINATION format.docxPoojaSen20
 

Recently uploaded (20)

Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
 
Solving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxSolving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptx
 
Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impact
 
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxPOINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
 
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
 
PSYCHIATRIC History collection FORMAT.pptx
PSYCHIATRIC   History collection FORMAT.pptxPSYCHIATRIC   History collection FORMAT.pptx
PSYCHIATRIC History collection FORMAT.pptx
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13
 
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
TataKelola dan KamSiber Kecerdasan Buatan v022.pdfTataKelola dan KamSiber Kecerdasan Buatan v022.pdf
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
 
Science 7 - LAND and SEA BREEZE and its Characteristics
Science 7 - LAND and SEA BREEZE and its CharacteristicsScience 7 - LAND and SEA BREEZE and its Characteristics
Science 7 - LAND and SEA BREEZE and its Characteristics
 
Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxIntroduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptx
 
APM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAPM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across Sectors
 
Concept of Vouching. B.Com(Hons) /B.Compdf
Concept of Vouching. B.Com(Hons) /B.CompdfConcept of Vouching. B.Com(Hons) /B.Compdf
Concept of Vouching. B.Com(Hons) /B.Compdf
 
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
 
Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991
Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991
Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991
 
Micromeritics - Fundamental and Derived Properties of Powders
Micromeritics - Fundamental and Derived Properties of PowdersMicromeritics - Fundamental and Derived Properties of Powders
Micromeritics - Fundamental and Derived Properties of Powders
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and Mode
 
Model Call Girl in Bikash Puri Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Bikash Puri  Delhi reach out to us at 🔝9953056974🔝Model Call Girl in Bikash Puri  Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Bikash Puri Delhi reach out to us at 🔝9953056974🔝
 
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
 
MENTAL STATUS EXAMINATION format.docx
MENTAL     STATUS EXAMINATION format.docxMENTAL     STATUS EXAMINATION format.docx
MENTAL STATUS EXAMINATION format.docx
 

Projectors and Projection Onto Subspaces

  • 1. Projector And Projection Onto Subspaces Numerical Linear Algebra Isaac Amornortey Yowetu NIMS-GHANA July 20, 2020
  • 2. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace Outline 1 problem of the Day 2 Projection onto a Line 3 Projection Matrix 4 Projection onto Subspace Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces
  • 3. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace Problem of the Day Consider the matrix A =   1 0 0 1 1 0   What is the orthogonal projection onto the range(A) and what is the image under P of the vector (1, 2, 3)∗ ? Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces
  • 4. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace Graphical Example Figure: By Nicholas Longo Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces
  • 5. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace Example 1 Let v = −2 3 and u = −1 1 What is the orthogonal projector P onto range(u), and what is the image under P of the vector (v) ? Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces
  • 6. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace Solution Projection Matrix Here we would like to find a projection matrix P: P = P2 and PT = P P = uuT uT u proju(v) = Pv = uuT uT u v Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces
  • 7. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace Solution Continue... P = uuT uT u u · uT = −1 1 (−1 1) = 1 −1 −1 1 uT · u = (−1 1) −1 1 = 2 ∴ P = 1 2 1 −1 −1 1 = 0.5 −0.5 −0.5 0.5 Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces
  • 8. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace proju(v) = Pv = 0.5 −0.5 −0.5 0.5 −2 3 = −2.5 2.5 Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces
  • 9. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace Problem of the Day Consider the matrix A =   1 0 0 1 1 0   What is the orthogonal projection onto the range(A) and what is the image under P of the vector (1, 2, 3)∗ ? Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces
  • 10. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace Solution Projection Matrix Here we would like to find a projection matrix P: P = P2 and PT = P P = uuT uT u But considering u to be matrix A, then: projA(v) = Pv = AAT AT A v Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces
  • 11. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace Solution Continue... Remark But we don’t divide matrices. Hence, we will then have inverse instead of matrix division. We can consider multiplying the matrix A by the pseudo-inverse to get matrix P. Pseudo-inverse = (AT A)−1 AT Then, P = A(AT A)−1 AT Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces
  • 12. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace Solution Continue... AT A = 1 0 1 0 1 0   1 0 0 1 1 0   = 2 0 0 1 (AT A)−1 = 0.5 0 0 1 (AT A)−1 AT = 0.5 0 0 1 1 0 1 0 1 0 = 0.5 0 0.5 0 1 0 A(AT A)−1 AT =   1 0 0 1 1 0   0.5 0 0.5 0 1 0 =   0.5 0 0.5 0 1 0 0.5 0 0.5   Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces
  • 13. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace Considering P =   0.5 0 0.5 0 1 0 0.5 0 0.5   and   1 2 3   Then, Pv =   0.5 0 0.5 0 1 0 0.5 0 0.5     1 2 3   =   2 2 2   Conclusion: P is our orthogonal Projection onto Range(A) and (2, 2, 2)T is the image under P of the vector(v). Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces
  • 14. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace Example 2 Let A be the 2-dimensional subspace of R3 spanned by the orthogonal vectors u1 = (1, 0, 1) and u2 = (0, 1, 0). Write the vector v = (1, 2, 3) as the sum of vector in A and a vector orthogonal to A. Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces
  • 15. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace Solution Let x = proju1 v + proju2 v (1) x = λu1 + βu2 (2) x = u1 · v u1 · u1 u1 + u2 · v u2 · u2 u2 (3) x =   1 0 1   ·   1 2 3     1 0 1   ·   1 0 1     1 0 1   +   0 1 0   ·   1 2 3     0 1 0   ·   0 1 0     0 1 0   (4) Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces
  • 16. problem of the Day Projection onto a Line Projection Matrix Projection onto Subspace Solution Continue... x = 2   1 0 1   + 2   0 1 0   =   2 2 2   (5) v ⊥ A =   1 2 3   −   2 2 2   =   −1 0 1   (6) Conclusion: Vector x is our sum of vector v in A and (v ⊥ A) is vector orthogonal to A. Isaac Amornortey Yowetu NIMS-GHANA Projector And Projection Onto Subspaces