Grassman algebra matrix representation

In summary, the matrix representation of the grassman algebra is x^*x*=0, with x in the first row, x* in the second row, and x*x in the third row. The complex conjugate of x is x*( usually with matrices we take the hermitian conjugate, but the notation does say * and not dagger). To find the matrix representation of the grassman algebra, one needs 4x4 matrices, the identity matrix, and the complex conjugate of x.
  • #1
jacobrhcp
169
0

Homework Statement



I want to find a matrix representation of the grassman algebra {1,x,x*,x*x} (and linear combinations with complex coefficients)

defined by [x,x]+=[x,x*]+=[x*,x*]+=0

I really don't know how to make matrix representations of an algebra. Is any set of 4 matrices that obey the anti commutation rules OK? Is there a standard procedure or do you always have play with matrices until you have some slick trick to find a representation?

The Attempt at a Solution



I think I need 4x4 matrices, but I'm not sure why. I'd really like to know why I would need 4x4 matrices.

Furthermore, because '1' is in my algebra I suspect I need the identity matrix. I can even rewrite the definition into xx=x*x*=0 which my x and x* matrices need to obey. The notation suggest that the complex conjugate of x needs to be x* (usually with matrices we then take the hermitian conjugate, but the notation does say * and not dagger). At the same time x*x is not 0, so this would mean my matrices need to have both complex and real coefficients. This all doesn't give me a decent guess.

Can anyone give me some general hints or explanations? I am determined to adopt the 'way of thinking' for finding matrix representations of an algebra.
 
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  • #2
You can try to use tensor products of nilpotent matrices, Pauli matrices and the identity. The nilpotent matrices take care of the fact that powers larger than 1 have to be zero, the Pauli matrices implement the anti-commutativity.
 
  • #3
that's a great idea! Will do.
 
  • #4


Is there a representation, where [tex]x^{*}x[/tex] is not the zero matrix? In other words, is there a 4x4 2-nilpotent matrix [tex]x[/tex], so that [tex]y=x^{*}x[/tex] is a non-zero 2-nilpotent matrix?
 
  • #5
I ask, and I answer myself :) There exists such a matrix:
[tex]
\begin{pmatrix}
0&0&i&0\\
1&0&0&-i\\
0&0&0&0\\
0&0&1&0
\end{pmatrix}
[/tex]

However, if looked upon just like a basis transformation of some vector [tex]A=(a_1,a_2,a_3,a_4)[/tex] from [tex](1,\phi,\phi^*,\phi^*\phi)[/tex] to their matrix representation, we could do [tex]\phi A=(0,a_1,0,-a_3)[/tex] and from there deduce a matrix form of [tex]\phi[/tex] (and then the others). But then we would have to redefine "complex conjugation" in some other way, as the new basis matrices are real.

Could you please tell me, how to do the "basis transformation", preserving the usual definition of complex conjugation? Thanks a lot!
 

FAQ: Grassman algebra matrix representation

What is Grassman algebra matrix representation?

Grassman algebra matrix representation is a mathematical approach used to represent and manipulate multi-dimensional data. It is based on the rules of Grassman algebra, which involves the use of anti-commutative variables to perform operations such as addition, multiplication, and differentiation.

How is Grassman algebra matrix representation different from traditional matrix representation?

Unlike traditional matrix representation, which uses only commutative variables, Grassman algebra matrix representation uses both commutative and anti-commutative variables. This allows for the representation of complex and multidimensional data, making it a useful tool for scientific analysis and modeling.

What are the applications of Grassman algebra matrix representation?

Grassman algebra matrix representation has various applications in fields such as physics, engineering, and computer science. It is commonly used in quantum mechanics, signal processing, and data compression, among other areas.

How does Grassman algebra matrix representation handle non-commutative variables?

Grassman algebra matrix representation uses the wedge product (also known as exterior product) to handle non-commutative variables. This product is defined as an anti-symmetric operation that takes two vectors and produces a new vector that is perpendicular to both, representing the area spanned by the original vectors.

Are there any limitations to Grassman algebra matrix representation?

One limitation of Grassman algebra matrix representation is that it can only handle finite dimensions. In addition, it can be challenging to interpret and visualize the results, making it more suitable for theoretical rather than practical applications.

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