Plücker coordinates: Difference between revisions

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See also: if anything the original page is more mathematically advanced lol
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{{Expert}}
{{Expert|Wedgie}}
[[File:Plucker_embedding.png|thumb|600px|right|Schematic illustration of the Plücker embedding. Linear subspaces of <math>\mathbb{R}^n</math> (here lines) get mapped to points on a quadric surface in projective space.]]
[[File:Plucker_embedding.png|thumb|600px|right|Schematic illustration of the Plücker embedding. Linear subspaces of <math>\mathbb{R}^n</math> (here lines) get mapped to points on a quadric surface in projective space.]]
{{Wikipedia|Plücker embedding}}
{{Wikipedia|Plücker embedding}}
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In Euclidean space, one usually takes advantage of the dot product to measure angles.
In Euclidean space, one usually takes advantage of the dot product to measure angles.
Given vectors <math>a, b \in \mathbb{R^n}</math>, we famously have
Given vectors <math>a, b \in \mathbb{R}^n</math>, we famously have


:<math>
:<math>
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In projective space, there is an analogous formula, using the wedge product instead.
In projective space, there is an analogous formula, using the wedge product instead.
Given some real point <math>j \in \mathbb{R^n}</math> with homogeneous coordinates <math>y</math>, and a linear subspace <math>P \in \mathrm{Gr} (k, n)</math> with Plücker coordinates <math>X</math>, we define the projective distance as
Given some real point <math>j \in \mathbb{R}^n</math> with homogeneous coordinates <math>y</math>, and a linear subspace <math>P \in \mathrm{Gr} (k, n)</math> with Plücker coordinates <math>X</math>, we define the projective distance as


:<math>
:<math>