Problem with Elementary row operations and rank theorems.

In summary, the person is taking their first course in linear algebra and wishes their teacher and textbook were more rigorous. They have a question about the rank theorem and whether or not it applies to a specific example. They also have a related question about performing multiple elementary row operations simultaneously and wants to know why it is not possible.
  • #1
GaugeSymmetry
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0
Ok, so I am taking my first course in linear algebra, and even though I am not a math major (physics major actually), I can't help but wish my teacher and text were more rigorous. So let me start by telling you all the problem I am having:



(First question) My book states the following rank theorem: a system of m eqns in n variable, there will be exactly n-r parameters. r being rank of augmented matrix. But then I begun thinking why, the proof my book gave is a joke, just logical words, and I can't seem to understand any proofs online. So I thought, hey can't you have a matrix with for say a system of 5 equations in 5 variables and in the augmented matrix have only say, 3 leading ones, and instead of the fourth non leading variable, have a column of all zeros, and a zero row at the bottom. I hope you understand what I am saying. Like through applying the Gaussian algorithm that's what you end up with. Here there surely would not be a n-r parameters.

Now come my next question, which is somewhat related. Can you perform elementary many linear operations simultaneously. For example given augmented matrix (here brackets represent a row, and column number increases left to right):

[(1,1,2,3),(-2,-1,0,-4),(4,2,4,7)) can I simply ---> R1 - 1/2R2 and do R2-2R1 --> end up with matrix with an entire column complete cleared out and zeros in it's place.

I know this is probably in violation of some basic properties, so I would like someone to explain why this is so. Why you can't perform multiple elementary row operations at once. Would you still not end up with an equivalent system, since a solution should work for each equation in the original, so can't you keep adding original equations and stuff all at once. Also if someone could help me with my first question.

Ahh, this is killing me.
 
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  • #2
Ok NVM with original question, can someone show me why one cannot perform two elementary row operations type 3 (where equations or rows are added to multiples of one another), simultaneously

as in

matrix (R1, R2, R3) --> R1 +cR2, R3+dR1--> simultaneously Here R1 and R3 being modified simultaneously.
 

Related to Problem with Elementary row operations and rank theorems.

1. What are elementary row operations?

Elementary row operations are a set of three operations that can be performed on the rows of a matrix: 1) multiplying a row by a non-zero constant, 2) adding a multiple of one row to another row, and 3) swapping two rows. These operations can be used to manipulate a matrix to solve linear equations or find the rank of a matrix.

2. How do elementary row operations affect the rank of a matrix?

Elementary row operations do not change the rank of a matrix. This means that the number of non-zero rows in the reduced row echelon form (RREF) of a matrix will be the same as the number of non-zero rows in the original matrix. This is known as the rank theorem.

3. Can elementary row operations be used to solve systems of linear equations?

Yes, elementary row operations can be used to solve systems of linear equations. By transforming a system of equations into an augmented matrix and using elementary row operations to reduce it to RREF, the values of the variables can be obtained. This is known as the Gauss-Jordan elimination method.

4. Are there any limitations to using elementary row operations?

There are a few limitations to using elementary row operations. One limitation is that they can only be used on square matrices (same number of rows and columns). Another limitation is that they cannot be used on matrices with zero rows, as this would result in division by zero. Additionally, elementary row operations may not always be able to fully solve a system of equations, in which case additional techniques may be required.

5. How are elementary row operations related to linear dependence and independence?

Elementary row operations can be used to determine the linear dependence or independence of a set of vectors. By putting the vectors into a matrix and reducing it to RREF, the rank of the matrix can be found. If the rank is equal to the number of vectors, they are linearly independent. If the rank is less than the number of vectors, they are linearly dependent.

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