Matrices and rank inequality exercise

In summary: What does ##AB = \begin{bmatrix} —A_1B— \\ \vdots \\ —A_jB— \\ \vdots \\—A_mB— \end{bmatrix}## mean? What does ##\alpha_j## mean?
  • #1
mahler1
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0
The problem statement

Let ##A ∈ K^{m×n}## and ##B ∈ K^{n×r}##
Prove that min##\{rg(A),rg(B)\}≥rg(AB)≥rg(A)+rg(B)−n##

My attempt at a solution

(1) ##AB=(AB_1|...|AB_j|...|AB_r)## (##B_j## is the ##j-th## column of ##B##), I don't know if the following statement is correct: the columns of ##AB## are a linear combination of the columns of ##B##, then ##rg(AB)≤rg(B)##.

(2)In a similar way, ##AB= \begin{bmatrix} —A_1B— \\ \vdots \\ —A_jB— \\ \vdots \\—A_mB— \end{bmatrix}## (##A_j## denotes the ##j-th## row of ##A##), so the rows of ##AB## are a linear combination of the rows of ##A##, from here one deduces ##rg(AB)≤rg(A)##.

From (1) and (2) it follows ##rg(AB)≤min\{rg(A),rg(B)\}##.

This is what I've done so far. I am having doubts with, for example (1), this statement I've conjectured: the columns of ##AB## are a linear combination of the columns of ##B##, then ##rg(AB)≤rg(B)##, but wouldn't this be the case iff ##AB=(α_1B_1|...|α_jB_j|...|α_rB_r)## with ##α_1,...,α_n ∈ K## instead of ##(AB_1|...|AB_j|...|AB_r)## ? This is a major doubt I have, the same goes for (2).

I need help to show the inequality ##rg(AB)≥rg(A)+rg(B)−n##
 
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  • #2
mahler1 said:
The problem statement

Let ##A ∈ K^{m×n}## and ##B ∈ K^{n×r}##
Prove that min##\{rg(A),rg(B)\}≥rg(AB)≥rg(A)+rg(B)−n##

Not sure I will answer your question, but I would suggest you might get more responses if you defined your terms. What is ##K^{mxn}##? What does ##rg(A)## mean?
 

Related to Matrices and rank inequality exercise

1. What are matrices and how are they used in scientific research?

Matrices are rectangular arrays of numbers or symbols that are used to represent and manipulate data in scientific research. They are commonly used in fields such as mathematics, physics, and engineering to model complex systems and solve equations.

2. What is rank inequality and why is it important in matrix operations?

Rank inequality is a property of matrices that describes the relationship between the dimensions of a matrix and its rank. It is important in matrix operations because it allows us to determine if a matrix is invertible, or if a system of equations has a unique solution.

3. How do matrices and rank inequality relate to linear transformations?

Matrices and rank inequality are closely related to linear transformations, which are mathematical operations that preserve the shape and structure of a matrix. The rank of a matrix can be used to determine the dimension of the vector space that is spanned by the columns of the matrix, which is important in understanding the effects of linear transformations.

4. Can you provide an example of a matrix and explain how rank inequality applies to it?

One example of a matrix is a 3x3 matrix with the following entries: [2 0 1; 1 3 5; 4 2 7]. The rank of this matrix is 2, which is less than the number of rows or columns. This shows that the matrix is not invertible, and that there are infinitely many solutions to any system of equations involving this matrix.

5. Is the concept of rank inequality limited to matrices with numerical entries?

No, the concept of rank inequality can also be applied to matrices with symbolic entries, such as variables or functions. In this case, the rank will depend on the specific values of the variables or functions, and can be used to determine the conditions for the existence of a unique solution to a system of equations.

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