Show that if S and T are linear
transformations on a finite dimensional
vector space, then rank (ST)<= rank (S).
As "V" is a finite dimensional vector space, let dimension of "V \\ is \\ n" .
According to question,
Let , "T:V\\rightarrow V" and "S:V\\rightarrow V" are two linear transformation from a finite dimensional vector space "V" to itself.
Then "S\\circ T :V \\rightarrow V" is a linear transformation.
Now, "dim(ImT)=rank(T) \\ ,\\ dim(ImS)=rank(S)"
and "dim(Im(ST))=dim(Im(S\\circ T))=rank (ST)."
"dim(Ker (S))=nullity (S),dim(ker(T))=nullity(T)"
and "dim(ker(ST))=nullity (ST)" .
Note: Generally,"\\ S\\circ T" written as "ST" .
Now ,"Ker(ST)=\\{x\\in V :(ST)(x)=0 \\}" .
Where is a zero vector of "V."
"=\\{ x\\in V:S(T(x))=0 \\}"
Now , if "x\\in ker(T)" then
"T(x)=0 \\ \\implies S(T(x))=S(0)=0" .
Hence, "x\\in Ker(ST)."
Therefore,"Ker(T)\\sube Ker(ST)."
Thus,"dim(Ker(T))\\leq dim(Ker(ST))"
I,e,"nullity (T)\\leq nullity (ST)...............(1)"
Now , from the rank-nullity theorem ,
"rank(T)+nullity(T)=rank(ST)+nullity (ST)=n"
Now , from (1) we concluded ,
"rank(ST)\\leq rank(T)" .
2. Consider the matrix ,
"S=\\begin{pmatrix}\n 1 & 0 \\\\\n 0 & 0\n\\end{pmatrix}" and "T=\\begin{pmatrix}\n 0 & 0 \\\\\n 1 & 0\n\\end{pmatrix}" and "ST=\\begin{pmatrix}\n 0 & 0 \\\\\n 0 & 0\n\\end{pmatrix}"
Then ,clearly "S,T:\\R^2\\rightarrow \\R^2" are two linear transformation.
Since , every "n\u00d7n" matrix from "\\R^n \\ to \\ \\R^n" is a linear transformation.
But "rank(ST)=0<rank(T)=1" .
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