How Can Generalized Inverse Help Analyze Non-Uniform Tidal Data?

In summary, the Generalised Inverse, also known as the Moore-Penrose Inverse, is a mathematical concept used to extend the traditional inverse of a matrix to non-square and non-full rank matrices. It can be calculated using various methods such as SVD, QR Factorization, and Cholesky Decomposition. Its purpose includes solving linear equations, finding least-squares solutions, and data analysis. It can only be applied to non-singular matrices and may not result in a unique inverse. The Generalised Inverse has important properties such as idempotence and satisfying the Moore-Penrose conditions.
  • #1
henrybrent
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Homework Statement



A magnetic data set is believed to be dominated by a strong periodic tidal signal of known tidal period[tex]\Omega[/tex] The field strength [tex]F(t)[/tex] is assumed to follow the relation:

[tex]F=a+b\cos\Omega t + c\sin\Omega t[/tex]

If the data were evenly spaced in time, then Fourier analysis would enable simple determination of the three parameters {a, b, c}. For non-uniform data, one technique to obtain the parameters is to calculate a generalized matrix inverse.

a) Define the model vector m for this problem.
b) Assume we have three measurements [tex]{F_1, F_2, F_3}[/tex] at times [tex]{t_1, t_2,t_3}[/tex]. Write down the data vector [tex]\gamma[/tex] and matrix A you would derive for these three measurements.

c) Hence, calculate the normal equations Matrix [tex]A^T A[/tex] and right-hand side vector [tex]A^T \gamma[/tex].

d) By generalizing your argument to N data, write down the normal equations matrix.

f) Imagine you now have many evenly spaced data over one full period of the oscillation. Explain why the off leading-diagonal terms of the matrix are now 0. What are the diagonal terms?

g) when the data are evenly spaced, explain why the estimates of the parameters {a,b,c} are independent.

h) What physical properties of the tidal signal could be derived from the values for b and c?

(20 marks)

Homework Equations



Given a vector of model parameters m, a data vector [tex]\gamma[/tex] and a matrix A to connect the two vectors, such that [tex]\gamma = Am[/tex]

a solution for the model parameters can be obtained by solving (inverting) the equation [tex](A^T A)m = A^T \gamma[/tex]

The Attempt at a Solution


[/B]
Starting with a), I'm trying to define my model vector.

[tex] m = 1/(A^T A) * A^T \gamma [/tex] ??
 
Last edited:
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  • #2
henrybrent said:

Homework Statement



A magnetic data set is believed to be dominated by a strong periodic tidal signal of known tidal period[tex]\Omega[/tex] The field strength [tex]F(t)[/tex] is assumed to follow the relation:

[tex]F=a+b\cos\Omega t + c\sin\Omega t[/tex]

If the data were evenly spaced in time, then Fourier analysis would enable simple determination of the three parameters {a, b, c}. For non-uniform data, one technique to obtain the parameters is to calculate a generalized matrix inverse.

a) Define the model vector m for this problem.
b) Assume we have three measurements [tex]{F_1, F_2, F_3}[/tex] at times [tex]{t_1, t_2,t_3}[/tex]. Write down the data vector [tex]\gamma[/tex] and matrix A you would derive for these three measurements.

c) Hence, calculate the normal equations Matrix [tex]A^T A[/tex] and right-hand side vector [tex]A^T \gamma[/tex].

d) By generalizing your argument to N data, write down the normal equations matrix.

f) Imagine you now have many evenly spaced data over one full period of the oscillation. Explain why the off leading-diagonal terms of the matrix are now 0. What are the diagonal terms?

g) when the data are evenly spaced, explain why the estimates of the parameters {a,b,c} are independent.

h) What physical properties of the tidal signal could be derived from the values for b and c?

(20 marks)

Homework Equations



Given a vector of model parameters m, a data vector [tex]\gamma[/tex] and a matrix A to connect the two vectors, such that [tex]\gamma = Am[/tex]

a solution for the model parameters can be obtained by solving (inverting) the equation [tex](A^T A)m = A^T \gamma[/tex]

The Attempt at a Solution


[/B]
Starting with a), I'm trying to define my model vector.

[tex] m = 1/(A^T A) * A^T \gamma [/tex] ??
What do you know about the matrix A? Is it a square matrix? If so, is it invertible?

If A is invertible, then so is AT, so solving the equation ##A^TAm = A^T\nu## involves nothing more than left-multiplying both sides of the equation by ##(A^T)^{-1}##, and then left-multiplying both sides by ##A^{-1}##. There is no division operation for matrices.
 
Last edited:

Related to How Can Generalized Inverse Help Analyze Non-Uniform Tidal Data?

1. What is the definition of the Generalised Inverse?

The Generalised Inverse, also known as the Moore-Penrose Inverse, is a mathematical concept that extends the traditional inverse of a matrix to non-square matrices and matrices with non-full rank. It is a powerful tool used in various fields such as statistics, engineering, and economics.

2. How is the Generalised Inverse calculated?

The Generalised Inverse can be calculated using various methods such as the Singular Value Decomposition (SVD), the QR Factorization, and the Cholesky Decomposition. These methods involve matrix operations and linear algebra techniques to find the pseudo-inverse of a given matrix.

3. What is the purpose of the Generalised Inverse?

The Generalised Inverse has many applications, including solving systems of linear equations, finding least-squares solutions, and performing data analysis. It is also used in regression analysis, optimization problems, and image processing.

4. Can the Generalised Inverse be applied to any matrix?

No, the Generalised Inverse can only be applied to matrices that are not singular, meaning they have a non-zero determinant. It can also be applied to matrices with non-full rank, but the resulting inverse may not be unique.

5. What are the properties of the Generalised Inverse?

The Generalised Inverse has several important properties, including idempotence, meaning that multiplying a matrix by its pseudo-inverse results in the original matrix. It also satisfies the four Moore-Penrose conditions, making it a unique and useful mathematical concept.

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