Monday, 6 January 2020

Vector p-norm for square matrices is submultiplicative for 1leple2




I'm trying to prove that the vector p-norm for square matrices is submultiplicative for values of p between 1 and 2. The vector p-norm for a square matrix A is defined as





For p=1 and p=2 the result follows easily from the Cauchy-Schwarz inequality. Now for $1

My approach is as follows:



Let A=(a_{ij}) and B=(b_{ij}) be two square n \times n matrices and let q=\frac{p}{p-1}. Then




\displaystyle \left(\|AB\|_p\right)^p=\sum_{i=1}^n \sum_{j=1}^n \left| \sum_{k=1}^n a_{ik}b_{kj} \right|^p \le \sum_{i=1}^n \sum_{j=1}^n \left( \sum_{k=1}^n \left| a_{ik}\right|^p\right) \left( \sum_{k=1}^n \left| b_{ik}\right|^q\right)^\frac{p}{q}



Now, since p<2, we have that q>2 and as such \frac{p}{q}<1. Here comes my doubt:
Can I assure that \left( \sum_{k=1}^n \left| b_{ik}\right|^q\right)^\frac{p}{q} \le \sum_{k=1}^n \left| b_{ik}\right|^p ? Because if so, then the result would follow immediately.



Any help would be greatly appreciated!


Answer



The answer to your question follows from this: let \|x\|_p = \left(\sum_{i=1}^n |x_i|^p \right)^{1/p}. Then is \|x\|_q \le \|x\|_p if q \ge p?



This is a very well known result, But here is a proof.




Answer: yes. Let M = \|x\|_p. So \|\frac1Mx\|_p = 1. Therefore |\frac{x_i}M|^p \le 1 \Rightarrow |\frac{x_i}M| \le 1 \Rightarrow |\frac{x_i}M|^q \le |\frac{x_i}M|^p. Hence
\|\frac1M x\|_q^q = \sum_{i=1}^n |\frac{x_i}M|^q \le \sum_{i=1}^n |\frac{x_i}M|^p = \|\frac1Mx\|_p^p = 1.
Therefore \|x\|_q \le M = \|x\|_p.


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