How Can Scalars Represent Any Linear Transformation in R^3?

In summary, to show that there exist scalars a, b, and c such that T(x, y , z) = ax + by + cz for all (x, y, z) in R^3, we can use the fact that T is linear and the basis of R^3 to determine how T acts on every vector in R^3. Similarly, for T: F^n -> F^m, we can use the same approach of multiplying by a matrix and apply the fact that T is linear to prove the existence of scalars in this case as well.
  • #1
loli12
Let T:R^3 -> R be linear. Show that there exist scalars a, b, and c such that T(x, y , z) = ax + by + cz for all (x, y, z) in R^3. State and prove an analogous result for T: F^n -> F^m.

I know that we just have to multiply by a matrix then we can get the desired transformation. But how would I go around to show that such scalars a, b and c exists?
 
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  • #2
Use the fact that T is linear. This means that T(v+w)=T(v)+T(w).
So if you know a basis for R^3 (there's an obvious one) and you know how T acts on this basis, you know how T acts on every vector in R^3.
 
  • #3


As a scientist, it is important to provide a clear and rigorous response. To show that there exist scalars a, b, and c such that T(x, y, z) = ax + by + cz for all (x, y, z) in R^3, we need to prove that T is a linear transformation.

Firstly, let's define T as a linear transformation from R^3 to R. This means that T must satisfy two properties:

1. T(u + v) = T(u) + T(v) for all u, v in R^3
2. T(ku) = kT(u) for all u in R^3 and k in R

To prove the first property, let u = (x1, y1, z1) and v = (x2, y2, z2) be two arbitrary vectors in R^3. Then,

T(u + v) = T(x1 + x2, y1 + y2, z1 + z2)
= a(x1 + x2) + b(y1 + y2) + c(z1 + z2) (since T is a linear transformation)
= (ax1 + by1 + cz1) + (ax2 + by2 + cz2)
= T(u) + T(v)

Therefore, T satisfies the first property.

To prove the second property, let u = (x1, y1, z1) be a vector in R^3 and k be a scalar in R. Then,

T(ku) = T(kx1, ky1, kz1)
= a(kx1) + b(ky1) + c(kz1) (since T is a linear transformation)
= k(ax1 + by1 + cz1)
= kT(u)

Therefore, T satisfies the second property.

Since T satisfies both properties, it is a linear transformation. Now, let's define the scalars a, b, and c as a = T(1,0,0), b = T(0,1,0), and c = T(0,0,1).

Then, for any vector (x, y, z) in R^3, we have

T(x, y, z) = T(x, 0, 0) + T(0, y
 

FAQ: How Can Scalars Represent Any Linear Transformation in R^3?

What is a linear transformation problem?

A linear transformation problem is a mathematical problem that involves transforming one set of data into another set of data using a linear function. This type of problem is commonly seen in algebra and geometry, and it is used to model real-world scenarios and make predictions.

What is the difference between a linear transformation problem and a nonlinear transformation problem?

The main difference between these two types of problems is the type of function used to transform the data. A linear transformation uses a linear function, which is a function that produces a straight line on a graph. Nonlinear transformations, on the other hand, use nonlinear functions, which produce curved lines on a graph.

What are some real-life examples of linear transformation problems?

Linear transformation problems can be found in various fields, such as economics, physics, and statistics. For example, in economics, linear transformations can be used to model the relationship between supply and demand in a market. In physics, they can be used to represent the motion of an object in a straight line. In statistics, linear transformations can be used to transform data to fit a linear regression model.

How can I solve a linear transformation problem?

To solve a linear transformation problem, you need to identify the input and output variables, as well as the linear function that relates them. Then, you can use algebraic techniques, such as substitution or elimination, to find the value of the output variable for a given input variable. You can also use graphical methods, such as plotting points and drawing a line, to solve the problem.

What are some common mistakes to avoid when solving a linear transformation problem?

One common mistake is incorrectly identifying the input and output variables or mixing them up. Another mistake is using the wrong linear function, either by not understanding the problem or using the wrong formula. It is also important to check your work and make sure your answer makes sense in the context of the problem.

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