Question: Super Convergence and Convergence: Is There a Relationship?

In summary, if a sequence (## a_n ##) super converges to l, then it also converges to l in the usual sense. However, the converse is not true as there are sequences that converge in the usual sense but do not meet the conditions for super convergence.
  • #1
neemer
22
0

Homework Statement



Suppose (## a_n ##) is a sequence and let [itex]l\in\mathbb R[/itex]. Let us say that (## a_n ##) is "super convergent" to ##l## if there exists [itex]N\in\mathbb N[/itex] such that for every ε>0 we have ##n \geq N## ⇒ |(## a_n - l##|<ε . Show that if (## a_n ##) super converges to l then (## a_n ##) converges to ##l## in the usual sense. What about the converse?

Homework Equations


The usual definition of convergence as given in my textbook is:

Let (## a_n ##) be a sequence and let [itex]l\in\mathbb R[/itex]. Then (## a_n ##) converges to ##l## if for every ε>0 there exists [itex]N(ε)\in\mathbb N[/itex] such that ##n \geq N(ε)## ⇒ |(## a_n - l##|<ε

The Attempt at a Solution



The only difference I see between the super convergent definition and usual definition is that for super convergence N does not depend on ε as it does in the usual definition. Therefore no matter how small an ε is chosen, any ##n \geq N## will imply (## a_n - l##|<ε . So even if ##N## is dependant on ε as it is in the usual definition, any N will work so the dependence does not matter. Therefore super convergence implies convergence. The second part about the converse is what I am confused about. I'm pretty sure that for converse I'm supposed to show that if (## a_n ##) converges in the usual sense, that it also must "super converge" which is obviously not true because if N is dependent on ε for regular convergence, a random N cannot be selected to have the super convergence statement hold. I'm pretty sure I'm on the right track with this question I just don't know how i would go about showing this last part.
 
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  • #2
neemer said:

Homework Statement



Suppose (## a_n ##) is a sequence and let [itex]l\in\mathbb R[/itex]. Let us say that (## a_n ##) is "super convergent" to ##l## if there exists [itex]N\in\mathbb N[/itex] such that for every ε>0 we have ##n \geq N## ⇒ |(## a_n - l##|<ε . Show that if (## a_n ##) super converges to l then (## a_n ##) converges to ##l## in the usual sense. What about the converse?

Homework Equations


The usual definition of convergence as given in my textbook is:

Let (## a_n ##) be a sequence and let [itex]l\in\mathbb R[/itex]. Then (## a_n ##) converges to ##l## if for every ε>0 there exists [itex]N(ε)\in\mathbb N[/itex] such that ##n \geq N(ε)## ⇒ |(## a_n - l##|<ε

The Attempt at a Solution



The only difference I see between the super convergent definition and usual definition is that for super convergence N does not depend on ε as it does in the usual definition. Therefore no matter how small an ε is chosen, any ##n \geq N## will imply (## a_n - l##|<ε . So even if ##N## is dependant on ε as it is in the usual definition, any N will work so the dependence does not matter. Therefore super convergence implies convergence.The second part about the converse is what I am confused about. I'm pretty sure that for converse I'm supposed to show that if (## a_n ##) converges in the usual sense, that it also must "super converge" which is obviously not true because if N is dependent on ε for regular convergence, a random N cannot be selected to have the super convergence statement hold. I'm pretty sure I'm on the right track with this question I just don't know how i would go about showing this last part.

Yes, the converse is obviously not true. If you think about what 'super convergence' means, it means the sequence must be constant for n>N. There are sequences that converge that are not eventually constant. Use that for a counterexample. That proves the converse is not true.
 
  • #3
Makes sense thanks.
 
  • #4
neemer said:

Homework Statement



Suppose (## a_n ##) is a sequence and let [itex]l\in\mathbb R[/itex]. Let us say that (## a_n ##) is "super convergent" to ##l## if there exists [itex]N\in\mathbb N[/itex] such that for every ε>0 we have ##n \geq N## ⇒ |(## a_n - l##|<ε . Show that if (## a_n ##) super converges to l then (## a_n ##) converges to ##l## in the usual sense. What about the converse?

You should note and try to prove the following:

If ##\forall \ \epsilon > 0, \ \ |x| < \epsilon## then ##x = 0##
 

1. What is "super convergence" proof?

"Super convergence" proof is a mathematical technique used to prove the convergence of a sequence or series at a faster rate than traditional methods. It involves showing that the error between the sequence or series and its limit decreases at a much faster rate than expected.

2. How is "super convergence" proof used in science?

"Super convergence" proof is commonly used in physics, engineering, and other scientific fields to analyze and prove the convergence of numerical methods and algorithms. It allows for more accurate and efficient calculations, leading to better predictions and results.

3. What are the benefits of "super convergence" proof?

The main benefit of "super convergence" proof is that it allows for faster and more accurate convergence of numerical methods and algorithms. This can save time and resources, as well as provide more reliable and precise results in scientific research and applications.

4. Are there any limitations to "super convergence" proof?

Like any mathematical technique, "super convergence" proof has its limitations. It may not be applicable to all types of sequences or series, and the proof itself can be complex and difficult to understand. Additionally, it may require advanced mathematical knowledge and skills to apply effectively.

5. Can "super convergence" proof be used in real-world applications?

Yes, "super convergence" proof has numerous real-world applications in fields such as physics, engineering, and computer science. It can be used to improve the convergence of numerical methods used in simulations, optimization problems, and other scientific calculations. It has also been applied in machine learning and data analysis processes.

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