Inverse matrix mass computation

In summary, the speaker is having trouble computing the inverse matrix using spherical coordinates due to singularities and is seeking alternative solutions such as using different coordinates or parametrizing the constraint differently.
  • #1
ebrattr
17
0
Hi !
I've been thinking this problem a whole and I could not find an answer. I want to solve the following problem: suppose I have [itex]N[/itex] mass particles with absolute coordinates [itex] \mathbf{x}_1, \mathbf{x}_2, \ldots, \mathbf{x}_N [/itex]. Besides, I have the following contraints: for all [itex]i=1,2,\ldots,N-1[/itex], [itex] |\mathbf{x}_{i+1}-\mathbf{x}_i|=L [/itex] where [itex]L[/itex] is a length. In every particle I have a total force over it [itex] \mathbf{F}_i [/itex].

I used spherical coordinates to express every constraints and parametrize [itex] \mathbf{x}_i [/itex]. However, when I compute the inverse matrix through these method (https://www.dropbox.com/sh/y25m55jpzrh7kqz/Y8EGX25lOQ), I got troubles. Since, I express a generalized coordiante [itex] q_i [/itex] as [itex] \cos q_i = \dfrac{\mathbf{r}\cdot \mathbf{k}}{L} [/itex] and I get: [itex] \dfrac{\partial q_i}{\partial \mathbf r} = -\dfrac{\mathbf{k}}{L\sin q_i}.[/itex] Therefore, when [itex] q_i = 0,\pi [/itex] the denominator is 0, and -><-.

What can I do ?

Thanks !
 
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  • #2
One possible solution is to use other coordinates that are not affected by singularities. For example, you could try using cylindrical or cartesian coordinates instead of spherical coordinates. This would allow you to avoid the singularities when computing the inverse matrix. Another solution is to choose a different parametrization for your constraint. For example, in the spherical coordinates, you could parametrize the constraint as |x_i - x_{i+1}| = Lcos(theta_i), where theta_i is a parameter. This would allow you to avoid the singularities.
 

What is an inverse matrix?

An inverse matrix is a matrix that when multiplied with a given matrix, results in the identity matrix. In simpler terms, it is a matrix that "undoes" the operations performed by the given matrix.

Why is it important to compute the inverse matrix mass?

Computing the inverse matrix mass is important in various applications such as solving systems of linear equations, calculating determinants, and finding solutions to differential equations. It also plays a crucial role in data analysis and machine learning algorithms.

What is the mathematical process for computing an inverse matrix mass?

The process for computing an inverse matrix mass involves using various mathematical operations such as row reduction, matrix multiplication, and finding the determinant. The specific steps may vary depending on the size and complexity of the matrix.

What are the limitations of inverse matrix mass computation?

One limitation of inverse matrix mass computation is that it is not possible to compute the inverse matrix for every matrix. For example, matrices that are not square, or have a determinant of 0, do not have an inverse matrix. Additionally, the computation can become computationally intensive for very large matrices.

How is inverse matrix mass computation used in real-world applications?

Inverse matrix mass computation has various real-world applications, such as in engineering for solving systems of equations and in finance for portfolio optimization. It is also used in data analysis for dimensionality reduction and in machine learning algorithms for feature selection.

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