Proving Matrix Transformation Property

In summary, the statement (λA+μB)^T = λA^T+μB^t can be proved by showing that the corresponding elements on both sides are equal, using the definition of transpose and the properties of scalar multiplication. The presence of the real numbers λ and μ does not affect the solution, as they distribute to each term in the matrix and can be pulled back out at the end.
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RJLiberator
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Homework Statement



Let A and B be n x m matrices, and λ and μ be real numbers. Prove that:
[itex](λA+μB)^T = λA^T+μB^t[/itex]

Homework Equations


:/

The Attempt at a Solution



I'm struggling to start here.

If there was no λ and μ, I think I'd be able to reasonably solve this. How do I show that these real numbers don't matter in this exchange?
 
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RJLiberator said:

Homework Statement



Let A and B be n x m matrices, and λ and μ be real numbers. Prove that:
[itex](λA+μB)^T = λA^T+μB^t[/itex]

Homework Equations


:/

The Attempt at a Solution



I'm struggling to start here.

If there was no λ and μ, I think I'd be able to reasonably solve this. How do I show that these real numbers don't matter in this exchange?

Let ##A = (a_{ij})## and ##B=(b_{ij})## and show that the corresponding elements on the two sides are equal.
 
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  • #3
RJLiberator said:
If there was no λ and μ, I think I'd be able to reasonably solve this. How do I show that these real numbers don't matter in this exchange?

There is really no difference between showing it with a scalar multiple and without the scalar multiple, since the scalars distribute to every term in the matrix. You could just as easily let ##A_\lambda = \lambda A## and ##B_\mu = \mu B## and show what you say you could show for the new matrices. Then the last step is to show that you can pull the scalars back out...since they are distributed to each term, they will not affect the solution.
 
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Lc and Ruber, thanks for the words of advice. What you stated resonates well with me. I will be working on this problem in the next 24 hours and I feel confident about it. I will post back with my update. :)
 
  • #5
Got it.
Using the transpose definition I make the original statement
(λA+μB)_ji
and then by previous axioms/operations it flows smoothly to λ(A_ji)+μ(B_ji)

A use of the transpose definition again
(λA^T+μB^T)

And we have shown what was needed to be shown.
:) Thank you.
 

Related to Proving Matrix Transformation Property

What is a matrix transformation property?

A matrix transformation property is a mathematical concept that describes how a matrix operates on a vector or another matrix. It is a set of rules that defines how the elements of a matrix will change after the transformation.

How is matrix transformation property proven?

Matrix transformation property is typically proven using the definition of a matrix transformation, which states that for every input vector, the output vector must be equal to the product of the transformation matrix and the input vector. This can be shown through mathematical proofs and using examples.

What are some examples of matrix transformation property?

Some examples of matrix transformation property include scaling, rotation, reflection, shearing, and translation. For example, a scaling transformation property would define how the elements of a matrix change when the matrix is multiplied by a scalar value.

Why is matrix transformation property important in science?

Matrix transformation property is important in science because it allows us to mathematically describe and analyze how a system or data set changes over time or through different operations. It is widely used in fields such as physics, engineering, and computer science for modeling and solving problems.

What are the applications of matrix transformation property?

Matrix transformation property has many applications in science and technology. It is used in image and signal processing, computer graphics, robotics, machine learning, and many other fields. It is also essential in solving systems of linear equations and performing operations on data sets in fields such as statistics and economics.

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