Linear Transform Synonyms: Grouping Components and Spaces

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SUMMARY

The discussion focuses on linear transformations and their associated components, specifically addressing the mapping from R^d to R^k. Key definitions include the rank and nullity of a transformation T, which correspond to R^k and R^d, respectively. Participants clarify the relationships between matrix dimensions and vector components, emphasizing the importance of understanding row space, kernel, column space, and image of a matrix A. The final goal is to group synonyms related to these concepts, providing a structured approach to understanding linear transformations.

PREREQUISITES
  • Understanding of linear transformations and their properties
  • Familiarity with matrix multiplication rules
  • Knowledge of vector spaces, including R^d and R^k
  • Concepts of rank and nullity in linear algebra
NEXT STEPS
  • Study the properties of linear transformations in depth
  • Learn about the relationship between row space and column space
  • Explore the concepts of kernel and image in linear algebra
  • Investigate the implications of the Rank-Nullity Theorem
USEFUL FOR

Students and professionals in mathematics, particularly those studying linear algebra, as well as educators looking for structured definitions and groupings of linear transformation concepts.

Kaspelek
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Also just working on another question, especially stuck with the last part.

It's basically definitions.

View attachment 825

This is what I've got so far, correct me if I'm wrong.

a) k components, k components.
b) R^n to R^n
c) R^rank(T)
d)R^nullity(T)
e) Completely unsure (need help with this)
 

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Kaspelek said:
Also just working on another question, especially stuck with the last part.

It's basically definitions.

View attachment 825

This is what I've got so far, correct me if I'm wrong.

A few things wrong over here, let's get started:

Kaspelek said:
a) k components, k components.

Remember how matrix multiplication works: the number of columns of the matrix on the left has to match the number of rows of the matrix on the right. In this case, A has d rows, which means that x (a column vector) has to have d components. The product, b, will have the same number of columns as x (one column) and the same number of rows as A (k rows). So, b has k components.

Kaspelek said:
b) R^n to R^n

I don't see an "n" anywhere in this question. Since A takes a vector of d components and gives a vector of k components, A is a map from $\mathbb{R}^d$ to $\mathbb{R}^k$.

Kaspelek said:
c) R^rank(T)
d)R^nullity(T)
e) Completely unsure (need help with this)

The answers to c) and d) are $\mathbb{R}^k$ and $\mathbb{R}^d$ respectively.

I'll put e) as it's own post.
 
Now for e): Let's make a list for reference. For our purposes, I don't care about the difference between A and $T_A$ (which gives us a few answers for free, incidentally).

1. row space of A
2. kernel of A
3. column space of A
4. solution space of A
5. image of A
6. null space of A
7. rank of A
8. rank of A
9. dimension of image of A
10. nullity of A
11. nullity of A
12. dimension of the row space of A
13. dimension of the kernel of A
14. dimension of the image of A^T
15. nullity of A^T
16. number of rows of A
17. rank of A^T

The question is to "group all synonyms." Now I'm fairly sure the answer should be:
1
2,6
3,5
4
7,8,9,12,14,17
10,11,13
15
16

any questions on a particular grouping?
 

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