What are the rank conditions for consistency of a linear algebraic system?

In summary, the rank condition for consistency of a linear algebraic system is that the coefficient matrix augmented with the column value matrix must have the same rank as the coefficient matrix. This applies to rectangular matrices as well, and the rank is calculated by determining the number of non-zero rows after row-reducing the matrix. The rank can also be thought of as the dimension of the largest square submatrix with a non-zero determinant. Row reduction, which involves swapping rows, multiplying by a constant, and adding a multiple of one row to another, is used to determine the rank.
  • #1
cheesefondue
17
0
what are the rank conditions for consistency of a linear algebraic system?
my proffessor said that the coefficient matrix augmented with the column value matrix must have the same rank as the coefficient matrix for consistency of the system of equations. however does the term rank apply to rectangular matrices such as an augmented matrix? I if so how is it calculated also I would like to know how to deduce this condition by reasoning [the derivation].
 
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  • #2
Well, what is your definition of "rank"? The definition I would use is that the rank of a matrix is the number of non-zero rows left after you row-reduce the matrix. Obviously, that idea applies to non-square matrices. In fact, if you append a new column to a square matrix, to form the "augmented matrix", any non-zero row, after row-reduction, for the square matrix will still be non-zero for the augmented matrix- add values on the end can't destroy non-zero values already there. The only way the rank could be changed is if you have non-zero values in the new column on a row that is all zeroes except for that, so that the augmented rank has greater rank than the original matrix. That tells you that one of your matrices has reduced to 0x+ 0y+ 0z+ ...= a where a is non-zero and that is impossible. If there is no such case, you have at least one solution to each equation. Yes, the system is consistent if and only if the rank of the coefficient matrix is the same as the rank of the augmented matrix.
 
  • #3
Well my textbook says that the rank of a matrix is the dimension of the largest square sub matrix whose determinant is non-zero. we just started the chapter so I'm not sure what you mean by row reduction, could you please elaborate? I'm sorry, I konw that these are really simple questions but I'd like to know the answers nevertheless... thanx.
 
  • #4

Related to What are the rank conditions for consistency of a linear algebraic system?

1. What are the rank conditions for consistency of a linear algebraic system?

The rank conditions for consistency of a linear algebraic system refer to the necessary conditions that must be satisfied for a system of linear equations to have a solution. These conditions are known as the rank condition and the nullity condition. The rank condition states that the rank of the coefficient matrix must be equal to the rank of the augmented matrix, while the nullity condition states that the nullity of the coefficient matrix must be equal to the nullity of the augmented matrix.

2. How do the rank conditions affect the solvability of a linear algebraic system?

The rank conditions play a crucial role in determining the solvability of a linear algebraic system. If the rank condition is not satisfied, then the system is overdetermined and has no solution. If the rank condition is satisfied, but the nullity condition is not, then the system is underdetermined and has infinitely many solutions. If both conditions are satisfied, then the system is consistent and has a unique solution.

3. What is the significance of the rank conditions in linear algebra?

The rank conditions are important because they provide a way to determine if a linear algebraic system has a solution or not. They also help to determine the number of solutions a system may have. In addition, the rank conditions are used in various applications of linear algebra, such as finding the best fit line in regression analysis and solving systems of differential equations.

4. How can the rank conditions be used to solve a linear algebraic system?

The rank conditions can be used to solve a linear algebraic system by first checking if they are satisfied. If both conditions are satisfied, then the system is consistent and can be solved using methods such as Gaussian elimination or Cramer's rule. If the nullity condition is not satisfied, then the system is underdetermined and can be solved using methods such as least squares. If the rank condition is not satisfied, then the system is overdetermined and has no solution.

5. Are there any other conditions that need to be satisfied for a linear algebraic system to be consistent?

In addition to the rank conditions, there is one more condition that must be satisfied for a linear algebraic system to be consistent. This is known as the existence condition, which states that the number of equations in the system must be equal to the number of unknowns. If this condition is not satisfied, then the system is inconsistent and has no solution.

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