A Question in Linear Tranformations

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In summary, the conversation discusses linear transformations and the relationship between the basis for the column space of a matrix and the range space of a vector space. It also mentions the dimensions of the null space and range space of a vector space. The solution is to write the vectors in the vector space as linear combinations of the basis vectors and multiply them by the matrix.
  • #1
beanz
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hi

I am a college student who has just started doing Linear Algebra. This is not a homework question, just something abot Linear Transformations that i don't understand. I hope someone can help me. Here it goes:

Consider Vector spaces V and W (over R^4) and a matrix 'A' which maps an element from V to W.

1. Why is it that the basis for the column space for A is exactly the basis for the range space of V in W? [i.e why is dim(col(A))=dim (range(V))]

2. Why is dim (V) = dim (Null space of V) + dim (range space of V in W)?

Thanx in advance :)
 
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  • #2
The basis for V is, of course, (1, 0, 0, 0), (0, 1, 0,0), (0, 0, 1, 0), (0, 0, 0, 1). Write those as columns and multiply each by A. Do you see that multiplying A times (1, 0, 0, 0), you get exactly the first column of A? And that multiplying A time (0, 1, 0, 0) gives exactly the second column of A? Write any vector in V as a linear combination of (1, 0, 0, 0), etc. and multiplying by A gives the result, in W, as a linear combination of the columns of A.
 
  • #3
Hahaha, Thanks. It is so obvious now that you mention it.
 

1. What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another. It preserves the essential structure of the original vector space, such as the concept of lines being mapped to lines and the origin remaining fixed.

2. Why are linear transformations important in science?

Linear transformations are important in science because they provide a powerful tool for representing and analyzing relationships between different variables. They can be used to model real-world phenomena and make predictions, and are especially useful in fields such as physics, engineering, and economics.

3. How do you determine if a transformation is linear?

A transformation is linear if it satisfies two properties: additivity and homogeneity. Additivity means that the transformation of the sum of two vectors is equal to the sum of their individual transformations. Homogeneity means that the transformation of a scalar multiple of a vector is equal to the scalar multiple of that vector's transformation.

4. What is the difference between a linear transformation and a nonlinear transformation?

A linear transformation preserves the structure of a vector space, while a nonlinear transformation does not. This means that a linear transformation maps lines to lines and the origin remains fixed, while a nonlinear transformation can distort or change these properties.

5. How are linear transformations used in data analysis?

Linear transformations are used in data analysis to transform and analyze data sets in a more meaningful way. They can be used to reduce dimensionality, normalize data, and identify relationships between variables. They are commonly used in techniques such as principal component analysis and linear regression.

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