Proving an image and annihilator of a kernel are equal

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SUMMARY

The discussion centers on proving that the image of a linear transformation T, denoted as Im T, is equal to the annihilator of its kernel, expressed as (Ker T)0. The proof begins by establishing that Im T is a subset of (Ker T)0, demonstrating that all elements of Im T map elements of Ker T to zero. The proof then transitions to showing that the dimensions of these spaces are equal, specifically that dim((Ker T)0) equals dim(Im T), leveraging the finite dimensionality of vector space V. The discussion highlights the importance of avoiding redundant assumptions in mathematical proofs.

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Adgorn
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Homework Statement


Suppose T:V→U is linear and V has finite dimension. Prove that I am Tt = (Ker T)0

Homework Equations


dim(W)+dim(W0)=dim(V) where W is a subspace of V and V has finite dimension.

The Attempt at a Solution


I first proved I am Tt ⊆ (Ker T)0. Let u be an arbitrary element of Ker T and Tt(Φ )∈ I am Tt, then Tt(Φ)(u)=Φ(T(u))=Φ(0)=0. Thus, since all elements of I am Tt map all elements of Ker T into 0, I am Tt ⊆ (Ker T)0.

I now only need to proof dim((Ker T)0) = dim I am Tt. Since V has finite dimension, dim((Ker T)0) = dim(V)-dim(Ker T), but dim(Im T) = dim(V)-dim(Ker T). Thus, dim((Ker T)0)=dim(Im T). This is where I got stuck, how do I prove dim (Im T) = dim ((Im T)0)?
 
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Adgorn said:
V has finite dimension
this assumption is redundant. Redundant assumptions just obscure a core of the theorem and make proof more complicated than it really is.
 
Last edited:
Hint.
First prove a
Theorem. Let ##A:V\to U,\quad w:V\to\mathbb{R}## be lenear mappings such that ##\mathrm{ker}\,A\subseteq \mathrm{ker}\,w##. Then there exist a linear mapping ##\Lambda:U\to\mathbb{R}## such that ##w=\Lambda A##.

It is actually a special case of some another general theorem
 

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