Linear transformation, Linear algebra

In summary, the matrix A must be linearly independent for T to be one-to-one. Without a linearly independent matrix, the solution to the system would only consist of the zero vector.
  • #1
Nikitin
735
27

Homework Statement


Describe the possible echelon forms of the standard matrix for the linear transformation T.

T: |R3 --> |R4 is one to one.

The Attempt at a Solution



T(x)=Ax. Right? So A must be the standard matrix.

I got this: A =

| £ * * |
| 0 £ * |
| 0 0 £ |
| ? ? ? |

Where £ represents a pivot position, and * any random number.

The only problem is that I have no idea what to put in the lowest row.. Can you guys help?
 
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  • #2
First of all, do you know when a matrix is one-to-one? What conditions must be true?
 
  • #3
All the columns of A must be linearly independent. I don't understand why this is so, though.
 
  • #4
Yes, that is true. Another way of saying that the columns are L.I. is that there is a pivot in each column of A. If you don't understand this, you should check out the proof of the theorem that relates one-to-one and linearly independence.
 
  • #5
It is linearly independent when only a trivial solution to the matrix being equal to [0,0,0,0] exists (ie, all the unknowns must be zero).

What I don't understand is why the matrix A needs to be linearly independent for the function T(x) being one-to-one.

I also still don't know what the lowest row should be.. Just zeros? But in that case the vector isn't really being transformed to the |R4 codomain...
 
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  • #6
No, the trivial solution would not be [0,0,0,0]. There are only three variables in four equations.

The matrix A must have column vectors linearly independent for T to be one-to-one because that is the only instance where the solution to the system would consist only of the zero vector. Remember that by definition of one-to-one, the ##\textrm{ker(T)} = \{\vec 0\}##. In other words, the null space is spanned by only the zero vector. If the vectors were not linearly independent, then the solution would not consist of solely the zero vector.

As to your answer to the question at hand, yes the last row consisting of zeroes would work. After all, you are showing the row-reduced echelon form, right?

Yes, the codomain is ##\mathbb{R}^4## but what does that have to do with T being one-to-one?

EDIT: Correction, you're right, I misspoke. I read it too quickly and thought you were referring to the three variables.
 
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  • #7
Yes. The matrix is linearly independent when there only exists a trivial solution for all the equations being equal to zero. In other words, x= only [0,0,0] for Ax=[0,0,0,0]. I obviously know this, you were just not understanding me.

The question is WHY T(x) is 1-to-1 over its whole domain if its standard matrix is linearly independent. Repeating the definition of l.i. and the rule of 1-to-1 over and over doesn't help me.

----

Can somebody tell me what should be in the bottom-row of my matrix (straight zeroes?) in the OP, and why? The matrix I posted is already in a linearly independent form, so can I just put whatever I like in the bottom row?
 
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1. What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another, while preserving the properties of addition and scalar multiplication. It can be represented by a matrix, and is a fundamental concept in linear algebra.

2. How is a linear transformation different from a regular transformation?

A regular transformation can be any type of function that maps one set of values to another. A linear transformation, on the other hand, specifically follows the rules of linearity, which include preserving addition and scalar multiplication. This means that if you add two vectors and then apply the linear transformation, it will be the same as applying the transformation to each vector separately and then adding the results.

3. What is the purpose of linear algebra?

Linear algebra is a branch of mathematics that deals with linear equations, vector spaces, and linear transformations. It has numerous applications in fields such as physics, engineering, economics, and computer graphics. Its main purpose is to provide a framework for solving and understanding systems of linear equations and transformations.

4. Can you give an example of a linear transformation?

One example of a linear transformation is a 2D rotation. If we have a vector in the plane, we can rotate it by a certain angle while preserving its magnitude and direction. This can be represented by a 2x2 matrix and is an example of a linear transformation.

5. How is linear algebra used in machine learning?

Linear algebra is a crucial tool in machine learning, as it allows us to represent and manipulate data in higher dimensions. It is used to perform operations such as matrix multiplication, which is essential in tasks like gradient descent and dimensionality reduction. Linear algebra is also used in creating and training models, such as linear regression and support vector machines.

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