Complete Norm on M_n(R): Questions & Hints

In summary: So ||X|| is the sum of the absolute values of all the entries in X. In summary, we discussed the norm || || and its relation to the norm || ||*. We also looked at several problems involving these norms, including showing completeness, proving that I-A is nonsingular when ||A||<1, and finding the norm of a specific 2x2 matrix. We also considered ways to approach these problems, such as using linearity and identifying R² with C. Additionally, we discussed the potential usefulness of showing ||AB||*<=||A||*||B||* and the definition of ||B||* as the supremum of ||B'(x)|| when ||x||=1.
  • #1
nubmathie
5
0
Let M_n(R) be the n x n matrices over the reals R. Define a norm || || on M_n(R) by ||A||= sum of absolute values of all the entries of A. Further define a new norm || ||* by ||A||* = sup{||AX||/||X||, ||X||!=0}.
Show that

1. M_n(R) under || ||* is complete.
2. If ||A||<1, then I-A is nonsingular, where I is the identity matrix.
3. The set of nonsingular matrices in M_n(R) is open.
4. Find ||B||*, where B is 2x2 and b_11=1, b_12=2, b_21=3, b_22=4.

There is a series of over 10 questions on the norm || ||. I've solved most of them but I've been stuck on (have no clue for) these ones above for a week.

I'd appreciate any hints.
 
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  • #2
Problem 1. Would completeness be easier in the other norm? Is there a relation between the norms?

Problem 2. Once you prove completeness, then you can show that the series for [tex](I-A)^{-1}[/tex] converges.

Problem 3. Use Problem 2.
 
  • #3
For 4., notice that by linearity, if B' denotes the linear map associated to the matrix B, then ||B||*=sup{||B'(x)||: ||x||=1}. And R² can be identified (i.e. is isomorphic) as a normed linear space to C (via the correspondance (x,y)<-->x+iy). Under this correspondence, then, the map B' becomes the map C(x+iy)=(x + 2y) + i(3x + 4y) and so ||B||*==sup{||C(z)||: ||z||=1}. I have transported the problem from R² to C simply because in C, the unit vector are easily parametrized: they are just the e^it for t in [0,2pi]. And so the problem of finding ||B||* has been reduced to the simple calculus problem of finding max{||C(cos(t)+isin(t))||: t in [0,2pi]}.
 
  • #4
Would you know how to show that ||AB||*<=||A||*||B||*? This would be helpful for Problem 2.

Also, how is ||B||* equal to sup{||B'(x)||: ||x||=1}? Notice that in my definition of || ||*, X refers to an nxn matrix.
 
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1. What is the definition of a complete norm on M_n(R)?

A complete norm on M_n(R) is a function that assigns a non-negative real number to each matrix in M_n(R) and satisfies three properties: submultiplicativity, absolute homogeneity, and completeness.

2. How is a complete norm different from a regular norm on M_n(R)?

A complete norm on M_n(R) is a stronger concept than a regular norm because it also requires the property of completeness, which means that every Cauchy sequence in M_n(R) converges to a limit in M_n(R).

3. Can you give an example of a complete norm on M_n(R)?

Yes, the Frobenius norm is a complete norm on M_n(R). It is defined as the square root of the sum of the squares of the entries of a matrix, and it satisfies all three properties of a complete norm.

4. How are complete norms used in mathematical analysis?

Complete norms on M_n(R) are used in functional analysis to study the properties of matrices and linear transformations. They are also used to define important concepts such as orthogonal matrices and unitary matrices.

5. Are complete norms unique for M_n(R)?

No, there can be multiple complete norms on M_n(R) that satisfy the three properties. However, the Frobenius norm is a commonly used and well-studied complete norm on M_n(R).

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