Applications of singular matricies

In summary, there are many applications that involve noninvertible or singular matrices, such as cohomology, homology, Riemann surfaces and more. These applications define an obstruction to a problem having a solution, which is represented by a linear map with a kernel of solvable problems.
  • #1
quasi426
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Are there any applications that always involve the use of noninvertible or singular matrices?? I know there are plenty for invertible ones. Thanks.
 
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  • #2
quasi426 said:
Are there any applications that always involve the use of noninvertible or singular matrices?? I know there are plenty for invertible ones. Thanks.

If A is a matrix, then the eigenvalues a of A make the matrix A - aI singular.

Also, projections.

There must must be many more applications.

Regards,
George
 
  • #3
the concept of cohomology is a group that is the kernel of some usually geometrically meaningful linear map.

having cohomology means the map is not invertible, i.e. ahs a kernel.

for example (homology) there is a "boundary" map from (linear combinations of) paths to linear combinations of points, taking each path to the difference of its endpoints, hence closed paths are characterized by having boundary equal to zero.

thus the space of closed paths is the kernel of the boundary map. now there is also a boundary map from (linear combinations of) parametrized surfaces, to (linear combinations of) paths, taking each surface to its boundary path. moreover all boundaries oif surfaces are closed paths.

thus boundaries of surfaces are a subgroup of the closed loops. the quotient of these two spaces is called the (singualr) homology of the space.


in de rham cohomology, the derivative map takes a differential form to d of it. in dimensions 1, 2, and 3, the exterior drivative is called variously "gradient", "curl", and "divergence", for physical reasons. It is an easy calculation to show that the divergence of a curl is zero as is the curlk of a gradient.

tyhe extebnt to which the converse holds measures exactly the same topology as the homology groups. and is called the cohomology groups.


in sheaf cohomology, tyhe psace of globals, ections of a oine buyndle is the kernel of a coboundary map as well. the representation of all lineat bundles of the same dergee allows one to represent all their cohomology groups as the kernel;s of a family of matrices.

the coboundary map having a kernel is equivalent to the line bundlke having a section, so the singualr matrices paramewtrize the most interesting line bundkles those having sections.

this is mumford's approach to riemann's theta function. hence the theta divisor on a jacobian varietya ssociated to a riemann surface has a determinantal equation, realizing it as the pullback of the family of singualr matrices of a certain dimension.

the list goes on and on. in almost all of mathematics, a problem is studied by defining an obstruction to that problem having a solution. thus the obstruction amp is a linear map from the space of all problems to som e other space, and the kernel of this map is the space of solvable problems,...
 

What is a singular matrix?

A singular matrix is a square matrix that does not have an inverse. This means that it cannot be inverted to find a unique solution for a system of equations.

What are some applications of singular matrices?

Singular matrices are commonly used in linear algebra to represent systems of equations and solve for unknown variables. They are also used in computer graphics, image processing, and data compression.

How are singular matrices different from non-singular matrices?

A non-singular matrix has a unique solution for a system of equations, while a singular matrix does not. Non-singular matrices also have a determinant that is not equal to zero, whereas the determinant of a singular matrix is equal to zero.

Can a singular matrix be used to represent a linear transformation?

Yes, a singular matrix can represent a linear transformation, but it will not have an inverse. This means that the transformation cannot be reversed or undone.

What is the relationship between singular matrices and eigenvalues?

The eigenvalues of a singular matrix are equal to zero. This is because the determinant of a singular matrix is equal to zero, and the eigenvalues are the roots of the characteristic equation, which includes the determinant.

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