Is Rank(TS) Always Less Than or Equal to Rank(T)?

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SUMMARY

The discussion confirms that for linear maps S: U -> V and T: V -> W, the relationship Rank(TS) ≤ Rank(T) holds true. The reasoning is based on the fact that the image of the composition TS is a subspace of the image of T, thus establishing the inequality. It is clarified that this holds under the assumption of finite-dimensional vector spaces. Additionally, it is noted that if T is onto, then the image of T equals W.

PREREQUISITES
  • Understanding of linear maps and vector spaces
  • Familiarity with the concept of image and rank in linear algebra
  • Knowledge of finite-dimensional vector spaces
  • Basic definitions of subspaces and their properties
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  • Study the properties of linear transformations in detail
  • Learn about the Rank-Nullity Theorem in linear algebra
  • Explore the implications of onto (surjective) linear maps
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Students and professionals in mathematics, particularly those studying linear algebra, as well as educators teaching concepts related to vector spaces and linear transformations.

garyljc
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I came across a question saying
Let S: U -> V and T:V -> W be linear maps, where U V and W are vectors spaces over the same field K
Show that Rank(TS) =< Rank(T)

This is my attempt
the im(TS) is a subspace of W
and so is the im(T)

am I missing out something ?
 
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This is my second attempot oon it
im(TS) is a subspace of im(T)
and im(T) is a subspace of W
therefore rank(TS) =< rank (T)
is it correct ?

but isn't im(T) = W ?
 
Im(T) = W when T is onto.

Your second attempt is the right direction, you might want to explain more of the details.
 
You were pretty much done, but you did some extra stuff that wasn't necessary.

Assuming the subspaces involved are finite-dimensional, \operatorname{rank} TS \le \operatorname{rank} T means, by definition, \dim(\operatorname{im} TS) \le \dim(\operatorname{im} T). This happens exactly when \operatorname{im} TS \subseteq \operatorname{im} T, which you know is true.
 

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