A Couple Linear Algebra Problems

In summary, the conversation discusses the concept of nullity and rank in a 4x2 matrix and how they relate to the linear dependence of the rows in the matrix. The conclusion is that since the maximum rank of the matrix is 2, at least two of the rows must be linearly dependent. The possible values of nullity are 0, 1, or 2, depending on the rank of the matrix. This is determined through the formula nullity(A) = n - rank(A). Overall, the conversation confirms the understanding of the concept and its application in a specific scenario.
  • #1
maherelharake
261
0

Homework Statement


If A is a 4x2 matrix, explain why the rows of A must be linearly dependent.

Homework Equations


The Attempt at a Solution


I said that nullility(A)=n-rank(A). This tells you that nullility(A) could be equal to 0,1, or 2. Since there are 4 vectors, at least two of them have to be linearly dependent. Is this correct?

Homework Statement


If A is a 4x2 matrix, what are the possible values of nullity(A)?

Homework Equations


The Attempt at a Solution


I said that the nullility(A) is 2-2 or 2-1 or 2-0, causing the answer to be 0, 1 or 2.

I just don't know if I thought about these correctly. Thanks in advance.
 
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  • #2
I'm not totally clear what you are thinking. WHY is rank A=0,1,2? I'm not saying it's wrong, in fact, it's completely correct. Just why?
 
  • #3
I just visualized a 4x2 matrix and figured that the number of nonzero rows had to be either 0, 1 or 2. Is that the right way to think of it?
 
  • #4
Well, ALL of the rows might be nonzero. I'm guessing you are visualizing row reduction of the matrix, right? Sure, the rows are four vectors in a two dimensional space. At most two of them are linearly independent. So yes, rank=0,1 or 2.
 
  • #5
Yes I am visualizing row reduction, sorry I forgot to say that. And that is the proof of why the rows of A must be dependent, right? Since at most two are independent?
 
  • #6
Sure. That works for me.
 
  • #7
Ok thanks. Is the second part correct too? The part about nullility? They seem to go hand-in-hand.
 
  • #8
The maximum rank A can have is 2. AT has the same rank as A, so each row vector is linearly dependent on at least one other row vector. The nullity is the difference between the number of columns of A and the rank of A...so it looks like you got things right.
 
  • #9
maherelharake said:
Ok thanks. Is the second part correct too? The part about nullility? They seem to go hand-in-hand.

Yeah. You are right about that part. 4=rank+nullity. If rank=0,1,2, then nullity=0,1,2.
 
  • #10
Ok great. It seems like everything I had was correct, but I just needed to make sure. I might have to post another thread tomorrow too if some more problems come up. Thanks for your help guys.
 

FAQ: A Couple Linear Algebra Problems

What is linear algebra?

Linear algebra is a branch of mathematics that deals with the study of linear equations, matrices, vectors, and their properties. It is used to solve systems of equations, analyze geometric transformations, and study vector spaces.

What are some real-world applications of linear algebra?

Linear algebra has numerous applications in various fields including physics, engineering, computer graphics, economics, and statistics. It is used to model and analyze systems, make predictions, and solve optimization problems.

What is the difference between a vector and a matrix?

A vector is a mathematical object that represents a quantity with magnitude and direction, while a matrix is a rectangular array of numbers or symbols. Vectors can be thought of as a special case of matrices, with only one row or column.

How is linear algebra used in machine learning and data analysis?

Linear algebra is a fundamental tool in machine learning and data analysis. It is used to represent and manipulate data, perform dimensionality reduction, and build predictive models. Techniques like linear regression, principal component analysis, and singular value decomposition all rely on linear algebra.

What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important concepts in linear algebra. Eigenvalues are scalars that represent the stretching or shrinking of a vector when multiplied by a matrix. Eigenvectors are the corresponding vectors that do not change direction when multiplied by the matrix. They are used to understand the behavior of systems and solve differential equations.

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