Do Row Operations Maintain Equivalence in Matrix Subtraction?

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Row operations can be performed in any order on a matrix, maintaining equivalence, but subtracting one matrix from another is not an elementary row operation. The discussion centers on whether the equation (A' + B)' = (A + B)' holds true, where A' is a reduced form of A. Participants suggest testing this with specific examples, as the validity of the statement is questioned. It is noted that the only clear case where the statement fails is when A equals B, resulting in A - B equaling zero. Overall, the relationship between row operations and matrix subtraction remains a complex topic needing further exploration.
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If you have two arbitrary matrices, A and B, I was wondering if row operations can be performed in any order to produce the same results.

For example, you perform elementary row operations on A to produce A', then do A' - B, then also produce a new matrix through elementary row operations on this new matrix to produce a new matrix C.

Can matrix C still be obtained by doing A (not A') - B, and then performing row operations? By performing row operations, the matrix is still remained intact, correct? There's no distortion of the row space, right?
 
MathewsMD said:
If you have two arbitrary matrices, A and B, I was wondering if row operations can be performed in any order to produce the same results.
Let's limit the discussion to a single matrix A. You can do elementary row operations in any order. At each step along the way, the new matrix will be equivalent to the one you started with.
MathewsMD said:
For example, you perform elementary row operations on A to produce A', then do A' - B
Why? When you perform an elementary row operation on A, you get a new matrix A' that is equivalent to A, but not equal to it. Some of the entries in the new matrix are different from those in A.

The basic idea is that if Ax = 0, for instance, then A'x = 0 as well, even though A ##\neq## A'. Subtracting one matrix from another is not an elementary row operation, so I don't get the point of your question.
MathewsMD said:
, then also produce a new matrix through elementary row operations on this new matrix to produce a new matrix C.

Can matrix C still be obtained by doing A (not A') - B, and then performing row operations? By performing row operations, the matrix is still remained intact, correct? There's no distortion of the row space, right?
 
Mark44 said:
Let's limit the discussion to a single matrix A. You can do elementary row operations in any order. At each step along the way, the new matrix will be equivalent to the one you started with.
Why? When you perform an elementary row operation on A, you get a new matrix A' that is equivalent to A, but not equal to it. Some of the entries in the new matrix are different from those in A.

The basic idea is that if Ax = 0, for instance, then A'x = 0 as well, even though A ##\neq## A'. Subtracting one matrix from another is not an elementary row operation, so I don't get the point of your question.

Okay, thank you for the response. I was trying to get at the this question specifically: A' is a reduced form of A, then is (A' + B)' = (A + B)', where (A+B)' is some reduction of A+B. Does the above equation hold true? Note: the reductions on each matrix may involve completely different operations, there's just some kind of row operation being done.
 
MathewsMD said:
Okay, thank you for the response. I was trying to get at the this question specifically: A' is a reduced form of A, then is (A' + B)' = (A + B)', where (A+B)' is some reduction of A+B. Does the above equation hold true? Note: the reductions on each matrix may involve completely different operations, there's just some kind of row operation being done.
Why don't you try a couple of simple examples - say 2 x 2 matrices or 3 x 3 matrices?

What you wrote is, I think, garbled.
(A' + B)' = (A + B)'
Did you mean (A' + B') = (A + B)'?

Also, by "=" do you mean "is row equivalent to" or "equals"? A professor I had in a 400-level linear algebra class was always very careful to write ##\equiv## when he was doing row operations, a habit that I've followed ever since.
 
Mark44 said:
Why don't you try a couple of simple examples - say 2 x 2 matrices or 3 x 3 matrices?

What you wrote is, I think, garbled.

Did you mean (A' + B') = (A + B)'?

Also, by "=" do you mean "is row equivalent to" or "equals"? A professor I had in a 400-level linear algebra class was always very careful to write ##\equiv## when he was doing row operations, a habit that I've followed ever since.

Sorry, I meant "equals to." By " ' " I am just referring to any random sequence of elementary row operations (I am not sure if there is better notation). And the ' used for one matrix isn't necessarily the same exact sequence of row operations done on the other matrices.

The only case I can necessarily find where it is not true is when A = B, since then A - B = 0, and no row operations can be used to derive A' - B. I don't seem to be able to prove if it's true for the nontrivial case, though. It does seem to stand, though.
 
MathewsMD said:
The only case I can necessarily find where it is not true is when A = B, since then A - B = 0
Did you try it with a few specific examples, as I suggested? I don't think the statement is true at all.
MathewsMD said:
, and no row operations can be used to derive A' - B. I don't seem to be able to prove if it's true for the nontrivial case, though. It does seem to stand, though.
 
Here is a little puzzle from the book 100 Geometric Games by Pierre Berloquin. The side of a small square is one meter long and the side of a larger square one and a half meters long. One vertex of the large square is at the center of the small square. The side of the large square cuts two sides of the small square into one- third parts and two-thirds parts. What is the area where the squares overlap?

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