Linear Algebra: Solving Systems of Equations with Wedge Product?

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Discussion Overview

The discussion revolves around solving systems of equations using linear algebra techniques, specifically focusing on homogeneous and inhomogeneous systems. Participants explore methods such as Gaussian elimination, the uniqueness of solutions, and the application of the wedge product in solving these systems.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Exploratory

Main Points Raised

  • One participant describes the process of solving a homogeneous system of equations represented as A \mathbf{x} = \mathbf{0} and questions whether Gaussian elimination followed by back-substitution is appropriate.
  • Another participant clarifies that if the null space of A is not empty, the solution is an entire subspace rather than a single vector, noting that the zero vector is always included in the null space.
  • There is a suggestion to perform row reduction after writing the matrix, emphasizing that a column of zeros should not appear at the end of the process.
  • A participant introduces the concept of using the wedge product to solve systems of equations, referencing a previous example they contributed to a wiki page on Geometric Algebra.

Areas of Agreement / Disagreement

Participants generally agree on the methods for solving square systems and the implications of the determinant, but there is no consensus on the effectiveness or clarity of the wedge product approach, as well as the best way to present it in educational resources.

Contextual Notes

Some assumptions about the properties of the matrix A and the nature of the solutions are not fully explored, such as the conditions under which the null space is empty or non-empty. The discussion also does not resolve the appropriateness of the wedge product in this context.

jdstokes
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Hi all,

I learned this stuff years ago and wasn't brilliant at it even then so I think a refresher is in order.

Suppose I have n distinct homogeneous equations in n unknowns. I want to find the solution so I write down the matrix of coefficients multiplying my vector of variables as follows

[itex]A \mathbf{x} =\mathbf{0}[/itex].

Now, we don't want [itex]\deta A \neq 0[/itex] to happen otherwise the columns of A are linearly independent so the only solution to [itex]A \mathbf{x} = \mathbf{C}_1 x_1 + \cdots \mathbf{C}_n x_n = \mathbf{0}[/itex] is [itex]\mathbf{0}[/itex].

Now how do we actually solve this for [itex]\mathbf{x}[/itex], do we just do Gaussian elimination followed by back-substitution? Is the solution unique in this case?

Now suppose the system is inhomogeneous

[itex]A\mathbf{x} = \mathbf{b}[/itex] where [itex]\mathbf{b}\neq 0[/itex]. In this case we actually want [itex]\det A \neq 0[/itex] because then we can instantly write down the unique solution

[itex]\mathbf{x} = A^{-1}\mathbf{b}[/itex].

Have I gotten the solution to square systems about right? If yes, I'll try to figure out the non-square case.
 
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jdstokes said:
Now how do we actually solve this for [itex]\mathbf{x}[/itex], do we just do Gaussian elimination followed by back-substitution? Is the solution unique in this case?

If the null space of A is not empty, then it is a subspace, so the solution is an entire subspace of the space you're working with, not just a single vector. The subspace containing only the zero vector is the only degenerate subspace that does consist of a single vector, and it is always in the null space.

Now suppose the system is inhomogeneous

[itex]A\mathbf{x} = \mathbf{b}[/itex] where [itex]\mathbf{b}\neq 0[/itex]. In this case we actually want [itex]\det A \neq 0[/itex] because then we can instantly write down the unique solution

[itex]\mathbf{x} = A^{-1}\mathbf{b}[/itex].

Have I gotten the solution to square systems about right? If yes, I'll try to figure out the non-square case.
Yep, that's right.
 
Last edited:
i suggest that after you write your matrix
just make a row reduction
and you are not supposed to write a column of zeros in the end
the last column depends on the last number after the "=" sign
 
You can also solve systems of equations of this form with the wedge product (wedging the column vectors). I'd put an example of this in the wiki Geometric Algebra page a while back when I started learning the subject:

http://en.wikipedia.org/wiki/Geomet...rally_expressed_in_terms_of_the_wedge_product.

Looking at the example now, I don't think it's the greatest. It should also probably be in a wedge product page instead of GA ... but that was the context that I learned about it first (I chose to use the mostly empty wiki page to dump down my initial notes on the subject as I started learning it;)
 

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