Matrix Rank: Meaning, Importance & What It Tells You

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Matrix rank refers to the dimension of the vector space generated by its rows or columns, indicating the number of linearly independent rows or columns. It is crucial for understanding the solutions of linear systems, as a higher rank suggests more unique solutions while a lower rank indicates potential degeneracy. The rank can reveal properties such as whether a matrix is invertible or the nature of its null space. Examples illustrate how to calculate rank and its implications for system solutions. Overall, matrix rank is a fundamental concept in linear algebra with significant applications in various mathematical and engineering fields.
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What does a rank of a matrix mean? why it is important? what does it tells you about?
 
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Its described here:

http://www.cliffsnotes.com/math/algebra/linear-algebra/real-euclidean-vector-spaces/the-rank-of-a-matrix

and here:

http://en.wikipedia.org/wiki/Matrix_rank

Basically it will tell you the number of degenerate solutions. They provide some examples too.

Lastly, a video discussion of the matrix and its rank among other things:

 
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I am studying the mathematical formalism behind non-commutative geometry approach to quantum gravity. I was reading about Hopf algebras and their Drinfeld twist with a specific example of the Moyal-Weyl twist defined as F=exp(-iλ/2θ^(μν)∂_μ⊗∂_ν) where λ is a constant parametar and θ antisymmetric constant tensor. {∂_μ} is the basis of the tangent vector space over the underlying spacetime Now, from my understanding the enveloping algebra which appears in the definition of the Hopf algebra...

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