Understanding Weak Convergence in Little l1 and Little l∞ Spaces

In summary, the conversation discusses a question about weak convergence and the problem involving L1 and Linf spaces. It is mentioned that a sequence in L1 that weakly converges also strongly converges. The speaker is struggling to prove this and is seeking advice. The solution is found in Schur's lemma, which states that every weakly Cauchy sequence converges.
  • #1
homology
306
1
hello folks,

I've got a question about weak convergence. I'm sure I'm missing something but can't see what it is (<--standard "I'm dumb apology")

The problem concerns little l 1 and little l infinity (which is dual to little l 1) To make notation easier I'm going to denote these spaces by L1 and Linf.

If a sequence in L1 weakly converges then it strongly converges.

So we take a sequence in L1 {x_n} where each x_n is a inf-tuple (a_1,...) of real numbers such that Sum|a_k| is bounded. Take an element of Linf, call it g. We given that limg(x_n)=g(x) for an x in L1. Now we need to show that limx_n=x.

So my dilemma is that I essentially keep proving that strong convergence implies weak convergence. I keep trying to work expressions like |g(x_m)-g(x_n)|<e and |g(x)-g(x_n)|<e into the analogs for the x_n (using of course the appropriate norm).

As per usual i can't get my inequalities going in the right direction. I need only a tiny push, I'm sure. So advise sparingly.

As always, your help is much appreciated,

Kevin
 
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  • #2
By Schur's lemma every weakly Cauchy sequence converges. So your answer lies in the proof of Schur's lemma.
 

FAQ: Understanding Weak Convergence in Little l1 and Little l∞ Spaces

Question 1: What is the definition of weak convergence?

Weak convergence is a concept in mathematics that describes the convergence of a sequence of random variables to a specific distribution. It is a weaker form of convergence compared to strong or almost sure convergence, as it only requires the convergence of the expected values of the random variables.

Question 2: How is weak convergence different from strong convergence?

Weak convergence focuses on the convergence of expected values, while strong convergence requires the convergence of entire probability distributions. This means that weak convergence is a weaker form of convergence, as it allows for more variability in the behavior of the random variables.

Question 3: What is the role of weak convergence in probability theory?

Weak convergence is an important concept in probability theory as it allows for the study of the behavior of random variables without requiring strong assumptions about their convergence. It is useful in various fields, such as statistics, econometrics, and mathematical finance.

Question 4: What are the main theorems related to weak convergence?

The two main theorems related to weak convergence are the Portmanteau theorem and the Helly-Bray theorem. The Portmanteau theorem provides a set of equivalent conditions for weak convergence, while the Helly-Bray theorem states that weak convergence is equivalent to convergence in distribution.

Question 5: How is weak convergence used in practical applications?

Weak convergence has many practical applications, such as in the study of time series data, where it is used to test for stationarity. It is also used in statistical inference, where it allows for the construction of confidence intervals and hypothesis testing without requiring strong assumptions about the underlying distributions of the data.

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