Linear least square s problem
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Linear Least Square s Problem. Over-determined systems Minimization problem: Least squares norm Normal Equations Singular Value Decomposition. Linear Least Square s: Example. Consider an equation for a stretched beam: Y = x 1 + x 2 T

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Linear Least Square s Problem

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Linear least square s problem

Linear Least Squares Problem

  • Over-determined systems

  • Minimization problem: Least squares norm

  • Normal Equations

  • Singular Value Decomposition

Lecture 4


Linear least square s example

Linear Least Squares: Example

Consider an equation for a stretched beam:

Y = x1 + x2 T

Where x1 is the original length, T is the force applied and x2 is the inverse coefficient of stiffness.

Suppose that the following measurements where taken:

T 10 15 20

Y 11.60 11.85 12.25

Corresponding to the overcomplete system:

11.60 = x1 + x2 10

11.85 = x1 + x2 15 - can not be satisfied exactly…

12.25 = x1 + x2 20

Lecture 4


Linear least square s definition

Linear Least Squares: Definition

Problem:

Given A(m x n), m≥n, b(m x 1) find x(n x 1) to minimize

||Ax-b||2.

  • If m > n, we have more equations than the number ofunknowns, there is generally no x satisfying Ax=b exactly.

  • This is an overcomplete system.

Lecture 4


Solution approaches

Solution Approaches

There are three different algorithms for computing the least square minimum.

  • Normal Equations (Cheap,less Accurate).

  • QR decomposition.

  • SVD(expensive,more reliable).

    The first algorithm in the fastest and the least accurateamong the three. On the other hand SVD is the slowest and most accurate.

Lecture 4


Normal equations 1

Normal Equations 1

Minimize the squared Euclidean norm of the residual vector:

To minimize we take the derivative with respect to x and set it to zero:

Which reduces to an (n x n) linear system commonly known as NORMAL EQUATIONS:

Lecture 4


Normal equations 2

x

b

A

Normal Equations 2

11.60 = x1 + x2 10

11.85 = x1 + x2 15

12.25 = x1 + x2 20

min||Ax-b||2

Lecture 4


Normal equations 3

Normal Equations 3

We must solve the system

For the following values

(ATA)-1ATis called a Pseudo-inverse

Lecture 4


Qr factorization 1

QR factorization 1

  • A matrix Q is said to be orthogonal if its columns are orthonormal, i.e. QT·Q=I.

  • Orthogonal transformations preserve the Euclidean norm since

  • Orthogonal matrices can transform vectors in various ways, such as rotation or reflections but they do not change the Euclidean length of the vector. Hence, they preserve the solution to a linear least squares problem.

Lecture 4


Qr factorization 2

QR factorization 2

Any matrix A(m·n) can be represented as

A = Q·R

,where Q(m·n) is orthonormal and R(n·n) is upper triangular:

Lecture 4


Qr factorization 21

QR factorization 2

  • Given A , let its QR decomposition be given as A=Q·R, where Q is an (m x n) orthonormal matrix and R is upper triangular.

  • QR factorization transform the linear least square problem into a triangular least squares.

    Q·R·x = b

    R·x = QT·b

    x=R-1·QT·b

Matlab Code:

Lecture 4


Singular value decomposition

Singular Value Decomposition

  • Normal equations and QR decomposition only work for fully-ranked matrices (i.e. rank( A) = n). If A is rank-deficient, that there are infinite number of solutions to the least squares problems and we can use algorithms based on SVD's.

  • Given the SVD:

    U(m x m) , V(n x n) are orthogonal

    Σ is an (m x n) diagonal matrix (singular values of A)

    The minimal solution corresponds to:

Lecture 4


Singular value decomposition1

Singular Value Decomposition

Matlab Code:

Lecture 4


Linear algebra review svd

Linear algebra review - SVD

Lecture 4


Approximation by a low rank matrix

Approximation by a low-rank matrix

Lecture 4


Geometric interpretation of the svd

σ·v1

v1

σ·v2

v2

Geometric Interpretation of the SVD

The image of the unit sphere under any mxn matrix is a hyperellipse

Lecture 4


Left and right singular vectors

S

AS

v1

σ·u1

σ·u2

v2

Left and Right singular vectors

We can define the properties of A in terms of the shape of AS

Singular values of A are the lengths of principal axes of AS, usually

written in non-increasing order σ1 ≥ σ2 ≥ … ≥ σn

n left singular vectorsof A are the unit vectors {u1,…, un}, oriented in the

directions of the principal semiaxes of AS numbered in correspondance with {σi}

n right singular vectorsof A are the unit vectors {v1,…, vn}, of S, which are the

preimages of the principal semiaxes of AS:Avi= σiui

Lecture 4


Singular value decomposition2

Singular Value Decomposition

Avi= σiui,

1 ≤ i ≤n

- Singular Value decomposition

Matrices U,V are orthogonal and Σ is diagonal

Lecture 4


Matrices in the diagonal form

Matrices in the Diagonal Form

Every matrix is diagonal in appropriate basis:

Any vector b(m,1) can be expanded in the basis of left singular vectors of A {ui};

Any vector x(n,1) can be expanded in the basis of right singular vectors of A {vi};

Their coordinates in these new expansions are:

Then the relation b=Ax can be expressed in terms of b’ and x’:

Lecture 4


Rank of a

Rank of A

Let p=min{m,n}, let r≤p denote the number of nonzero singlular values of A,

Then:

The rank of A equals to r, the number of nonzero singular values

Proof:

The rank of a diagonal matrix equals to the number of its nonzero entries, and in the decomposition A=UΣV* ,U and V are of full rank

Lecture 4


Determinant of a

Determinant of A

For A(m,m),

Proof:

The determinant of a product of square matrices is the product of their determinants. The determinant of a Unitary matrix is 1 in absolute value, since: U*U=I. Therefore,

Lecture 4


A in terms of singular vectors

A in terms of singular vectors

For A(m,n), can be written as a sum of r rank-one matrices:

(1)

Proof:

If we write Σ as a sum of Σi, where Σi=diag(0,..,σi,..0), then (1)

Follows from

(2)

Lecture 4


Norm of the matrix

Norm of the matrix

The L2 norm of the vector is defined as:

(1)

The L2 norm of the matrix is defined as:

Therefore

,where λi are the eigenvalues

Lecture 4


Matrix approximation in svd basis

Matrix Approximation in SVD basis

For any ν with 0 ≤ ν ≤r, define

(1)

If ν=p=min{m,n}, define σv+1=0. Then

Lecture 4


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