- #1
hooyeh
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I've been thinking about the following question:
if [tex]x\in R^n[/tex] and [tex]y=Cx\in R^p[/tex] where matrix [tex]C[/tex] describes the linear map from n dimensional reals to p dimensional reals. If we only have access to [tex]y[/tex] and want to recover the information about [tex]x[/tex], which components in [tex]x[/tex] are needed? I kind of figured out that the number of components that we need is [tex]n-rank(C)[/tex].
For example, [tex]x=[x_1; x_2; x_3][/tex] and [tex]y=[x_1; x_1+x_2+x_3][/tex]. Obviously, either [tex]x_2[/tex] or [tex]x_3[/tex] could help us recover the information of [tex]x[/tex]. Another trivial example, [tex]x=[x_1; x_2; x_3][/tex] and [tex]y=[x_1; x_1][/tex], then we need both [tex]x_2[/tex] and [tex]x_3[/tex].
But how to state this observation rigorously and neatly in the mathematical language for any two such vectors [tex]x\in R^n[/tex] and [tex]y=Cx\in R^p[/tex]?
Any helpful suggestions are appreciated!
if [tex]x\in R^n[/tex] and [tex]y=Cx\in R^p[/tex] where matrix [tex]C[/tex] describes the linear map from n dimensional reals to p dimensional reals. If we only have access to [tex]y[/tex] and want to recover the information about [tex]x[/tex], which components in [tex]x[/tex] are needed? I kind of figured out that the number of components that we need is [tex]n-rank(C)[/tex].
For example, [tex]x=[x_1; x_2; x_3][/tex] and [tex]y=[x_1; x_1+x_2+x_3][/tex]. Obviously, either [tex]x_2[/tex] or [tex]x_3[/tex] could help us recover the information of [tex]x[/tex]. Another trivial example, [tex]x=[x_1; x_2; x_3][/tex] and [tex]y=[x_1; x_1][/tex], then we need both [tex]x_2[/tex] and [tex]x_3[/tex].
But how to state this observation rigorously and neatly in the mathematical language for any two such vectors [tex]x\in R^n[/tex] and [tex]y=Cx\in R^p[/tex]?
Any helpful suggestions are appreciated!