Understanding LA: Linear Transformation of Matrix A

In summary, the mapping L_A is a linear transformation from F^m to F^n, defined by L_A(x) = Ax for an m*n matrix A. This is proven by Theorem 2.12, which shows that L_A satisfies all properties of a linear transformation.
  • #1
jeff1evesque
312
0
How is LA a linear function? What kind of operation is action on A? I thought L denotes a linear transformation. So if we have a matrix A, how is the LA a transformation? Is it just a definition (notation wise) or is there more to it?


Thanks,

JL
 
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  • #2
jeff1evesque said:
How is LA a linear function? What kind of operation is action on A? I thought L denotes a linear transformation. So if we have a matrix A, how is the LA a transformation? Is it just a definition (notation wise) or is there more to it?


Thanks,

JL
I have no idea what you mean by "LA". How is it defined?
 
  • #3
If A is an m*n matrix, then the mapping [tex]L_A[/tex] is from F^m to F^n and is defined by [tex] L_A(x) = Ax [/tex]. If [tex] x_1 [/tex] and [tex] x_2 ][/tex] are vectors in F^n, then [tex] L_A (x_1 + x_2) = A(x_1 + x_2) = Ax_1 + Ax_2 = L_A(x_1) + L_A(x_2) [/tex]. Also, for any vector x in F^n and any scalar c, we have [tex] L_A(cx) = A(cx) = cAx = cL_A(x) [/tex]. Thus [tex] L_A [/tex] is a linear transformation.
 
  • #4
JG89 said:
If A is an m*n matrix, then the mapping [tex]L_A[/tex] is from F^m to F^n and is defined by [tex] L_A(x) = Ax [/tex]. If [tex] x_1 [/tex] and [tex] x_2 ][/tex] are vectors in F^n, then [tex] L_A (x_1 + x_2) = A(x_1 + x_2) = Ax_1 + Ax_2 = L_A(x_1) + L_A(x_2) [/tex]. Also, for any vector x in F^n and any scalar c, we have [tex] L_A(cx) = A(cx) = cAx = cL_A(x) [/tex]. Thus [tex] L_A [/tex] is a linear transformation.

THanks, that's exactly what I thought. However, in the text I am reading, it says L_A is linear immediately from theorem 2.12:

Theorem 2.12:
Let A be an mxn matrix, B and C be nxp matrices, and D and E be qxm matrices. Then,
(a) [tex]A(B + C) = AB + AC and (D + E)A = DA + EA. [/tex]
(b) [tex]a(AB) = (aA)B = A(aB) [/tex] for any scalar a.
(c) [tex]I_mA = A = AI_n[/tex]
(d) If V is an n-dimensional vector space with an ordered basis J, then [tex] [I_V]_J = I_n[/tex]

THanks again.
 

FAQ: Understanding LA: Linear Transformation of Matrix A

What is a linear transformation?

A linear transformation is a mathematical operation that takes in a vector or matrix as input and outputs a transformed vector or matrix. This transformation preserves the basic shape and structure of the input, such as lines remaining lines and planes remaining planes.

How does a linear transformation relate to matrix A?

A linear transformation can be represented by a matrix, and this matrix is commonly referred to as matrix A. Matrix A acts as a set of instructions for how to transform the input vector or matrix into the output vector or matrix.

What is the importance of understanding linear transformation of matrix A?

Understanding linear transformation of matrix A is crucial in many areas of mathematics, physics, and engineering. It allows for the manipulation and analysis of vectors and matrices, which are fundamental concepts in these fields. It also has practical applications in computer graphics, data analysis, and machine learning.

What are some common operations used in linear transformation of matrix A?

Some common operations used in linear transformation of matrix A include scaling, rotation, shearing, and reflection. These operations can be performed on both vectors and matrices and can be combined to create more complex transformations.

How can I visualize linear transformation of matrix A?

One way to visualize linear transformation of matrix A is by using geometric shapes or objects as inputs and observing how they are transformed into different shapes or objects as outputs. This can be done manually or with the help of software, such as a graphing calculator or a programming language like MATLAB.

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