Solving Matrix Norms & Finding Valid Expressions: Help Needed!

In summary, the conversation is discussing the computation of the condition number for a given matrix using different norms and how to determine the values of a that result in a large condition number. It is also exploring whether a given expression is a valid norm for a vector in R^n.
  • #1
timmy1
1
0
Hi all, I think I have part of the answer to my question but I need some help if you would be so kind.

Q: a)Compute the condition number of the following matrix using matrix norms ||A||_1 and ||A||_infinity.
[a 1]
[1 1]

And also: What values of a give a large condition number?

So ||A||_1= either 2 or (|a|+1), whichever is bigger. So it would be written as max(2,|a|+1).
And ||A||_infinity is the same as ||A||_1.

How do I bring this together to get the answer I need?b) Is the following expression a valid norm for x in R^n?
max{|x_2|,|x_3|,|x_4|,...,|x_n|}

Got nothing for this one.

Thanks for any help guys!

Timmy
 
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  • #2
For the first question, you have to compute the norm of $A^{-1}$, and for the second question, $x_1$ may be not equal to $0$, even if the "norm" of $x$ is $0$.
 
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Related to Solving Matrix Norms & Finding Valid Expressions: Help Needed!

1. What is a matrix norm and why is it important?

A matrix norm is a measure of the size or magnitude of a matrix. It is important because it allows us to compare and quantify the differences between matrices, and it is also used in various mathematical and scientific applications, such as optimization problems and error analysis.

2. How do you calculate the matrix norm?

The calculation of a matrix norm depends on the type of norm being used. Some common methods include the Frobenius norm, the maximum norm, and the induced matrix norm. Each type has a specific formula for calculating the norm, which involves taking the absolute values of the elements of the matrix and performing certain operations, such as summing or finding the maximum value.

3. What is a valid expression for a matrix norm?

A valid expression for a matrix norm is any mathematical expression that follows the rules and properties of the specific norm being used. For example, the Frobenius norm must be a non-negative value, and it is only equal to zero if the matrix is a zero matrix. In general, a valid expression for a matrix norm should accurately measure the size or magnitude of the matrix.

4. How can matrix norms be used in solving problems?

Matrix norms can be used in various problems, such as finding the distance between two matrices, determining the convergence of iterative methods, and estimating the error in numerical solutions. They can also be used in optimization problems, where the goal is to minimize or maximize a function that involves matrices.

5. Can matrix norms be used for non-square matrices?

Yes, matrix norms can be used for non-square matrices. In fact, most types of matrix norms can be applied to matrices of any size. However, some properties and calculations may differ for non-square matrices compared to square matrices. For example, the Frobenius norm for a non-square matrix involves taking the square root of the sum of squared elements, while for a square matrix it involves taking the sum of the squared elements.

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