Numerical Analysis - Matrix Norm equalities.

In summary, we can determine constants k and K for the infinity and one-norms by using the result from the first part of the problem and the definitions of these norms.
  • #1
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Homework Statement



Determine constants c and C that do not depend on vector x. For this to be true:

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Use this result and the definition of matrix norms to determine constants k and K so that:

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Homework Equations



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The Attempt at a Solution




I've done half of this problem. For c and C it was not hard. I found that if c = 1/n (n being the number of terms in the vector) and C = 1 the statement holds true.

The second half on the other hand has me stumped. The infinity norm and the one-norm only seem to share one term, due to one being the max row sum and the other being the max column sum. Can anyone give me any direction?

Thanks.
 
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  • #2


To determine the constants k and K, we can use the definition of matrix norms. The infinity norm (also known as the maximum norm) of a matrix is the maximum absolute row sum, while the one-norm (also known as the column sum norm) is the maximum absolute column sum. Therefore, we can use the result from the first part of the problem to determine the constants k and K as follows:

For the infinity norm, we have k = c = 1/n and K = C = 1. This is because the maximum row sum of a matrix is equal to the maximum absolute value of any row, and since c = 1/n, the maximum absolute value of any row will be less than or equal to 1. Similarly, the maximum absolute column sum will also be less than or equal to 1, so K = 1.

For the one-norm, we have k = c = 1 and K = C = n. This is because the maximum column sum of a matrix is equal to the maximum absolute value of any column, and since c = 1, the maximum absolute value of any column will be less than or equal to n. Similarly, the maximum absolute row sum will also be less than or equal to n, so K = n.

Therefore, the constants k and K are k = 1/n, K = 1 for the infinity norm, and k = 1, K = n for the one-norm. These constants do not depend on the vector x, as required by the problem.
 

FAQ: Numerical Analysis - Matrix Norm equalities.

What is numerical analysis?

Numerical analysis is a branch of mathematics that deals with the development and use of numerical methods for solving mathematical problems. It involves the use of algorithms and computational techniques to obtain approximate solutions to complex mathematical equations.

What is a matrix norm?

A matrix norm is a mathematical concept used to measure the size or magnitude of a matrix. It is analogous to the concept of absolute value in elementary algebra and is defined as the largest magnitude of the corresponding vector when the matrix is multiplied by a unit vector.

What are the different types of matrix norm equalities?

The most commonly used matrix norm equalities are: submultiplicative property, triangle inequality, absolute homogeneity, and symmetry. These properties help in understanding the behavior of matrix norms and their relationships with other mathematical concepts.

Why is matrix norm important in numerical analysis?

Matrix norm is an essential tool in numerical analysis as it helps in measuring the error involved in approximating a solution to a mathematical problem. It also plays a crucial role in analyzing the stability and accuracy of numerical algorithms.

What are some applications of matrix norm in real-world problems?

Matrix norm has numerous applications in various fields such as engineering, physics, economics, and computer science. It is used in solving systems of linear equations, optimization problems, data analysis, and image processing, among others. It also has applications in quantum mechanics and graph theory.

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