Can a 3x5 Matrix with 3 Free Variables Ever Have No Solution for Any Vector b?

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  • #1
sami23
76
1

Homework Statement


Suppose a non-homogeneous system, Ax = b, of 3 linear equations in 5 unknowns (3x5 matrix) and 3 free variables, prove there is no solution for any vector b.


Homework Equations


Using the rank theroem:
n = rank A + dim Nul(A) where n = # of columns; dim Nul(A) = # free variables


The Attempt at a Solution


rank A = n - dim Nul(A) = 5-3 = 2 (which represents the pivot columns)

How do I know there are no solutions for any vector b knowing there can be at most 3 pivot columns?
 
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  • #2
This doesn't make sense. Most systems of 3 equations in 5 unknowns have many solutions.
 
  • #3
sami23 said:

Homework Statement


Suppose a non-homogeneous system, Ax = b, of 3 linear equations in 5 unknowns (3x5 matrix) and 3 free variables, prove there is no solution for any vector b.


Homework Equations


Using the rank theroem:
n = rank A + dim Nul(A) where n = # of columns; dim Nul(A) = # free variables


The Attempt at a Solution


rank A = n - dim Nul(A) = 5-3 = 2 (which represents the pivot columns)

How do I know there are no solutions for any vector b knowing there can be at most 3 pivot columns?


If the matrix has rank 3 there are infinitely many solutions.

RGV
 

FAQ: Can a 3x5 Matrix with 3 Free Variables Ever Have No Solution for Any Vector b?

What is the Rank Theorem in Linear Algebra?

The Rank Theorem in Linear Algebra is a fundamental theorem that states the relationship between the dimensions of the column space and null space of a matrix. It states that the rank of a matrix is equal to the number of linearly independent columns, and the nullity (dimension of the null space) is equal to the number of free variables in the corresponding linear system.

How is the Rank Theorem useful in solving linear equations?

The Rank Theorem is useful in solving linear equations because it allows us to determine the number of solutions to a system of linear equations. If the rank of the coefficient matrix is equal to the rank of the augmented matrix, then the system has a unique solution. If the ranks are not equal, then the system has either infinitely many solutions or no solutions.

Can the Rank Theorem be applied to non-square matrices?

Yes, the Rank Theorem can be applied to non-square matrices. The column space and null space of a non-square matrix can still be determined, and the theorem can be used to find the rank and nullity of the matrix.

How does the Rank Theorem relate to linear independence?

The Rank Theorem is closely related to linear independence. It states that the rank of a matrix is equal to the number of linearly independent columns. This means that the rank of a matrix can be used to determine the linear independence of its columns. If the rank of a matrix is equal to the number of columns, then the columns are linearly independent.

Are there any real-world applications of the Rank Theorem?

Yes, the Rank Theorem has many real-world applications. It is used in fields such as computer science, engineering, and economics to solve systems of linear equations. It is also used in data analysis and machine learning to reduce the dimensionality of data sets and to find relationships between variables.

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