Best books and papers for classical "hard" analysis tricks

In summary, the conversation discusses the shift towards more abstract perspectives in recent analysis textbooks, but notes that classical or "hard" analysis techniques are still crucial for solving problems. Some suggested books for learning this type of analysis include Whittaker and Watson, "Baby" Rudin, Zygmund's book on trigonometric series, and Stromberg's book on classical real analysis. The best way to learn this kind of analysis is through attempting to solve problems and studying proofs. It is also noted that this type of mathematics used to be a central focus and some notable mathematicians/scientists who excel in hard analysis include Freeman Dyson, Lars Onsager, and George Polya. The conversation ends with a mention of some recommended books
  • #1
Sauvain
It seems that most recently published analysis textbooks are written from a more abstract perspective, e.g., dealing with functions on general metric spaces or with spaces of functions. However, often solving a problem requires some "get your hands dirty" classical or "hard" analysis -- for example involving clever manipulations of inequalities, algebraic identities, substitutions, tricks to rewrite integrals in other forms, and so on. I have noticed that a lot of older analysis textbooks contain a lot of this kind of analysis. Some books that I think fit with what I have in mind are:

* Whittaker and Watson
* "Baby" Rudin
* Zygmund's book on trigonometric series
* Stromberg's book on classical real analysis

My question is, what are some of the best books or papers for learning this kind of analysis? More generally, what is the best way to learn this kind of analysis? A process that has worked fairly well for me so far has involved attempting to solve problems of this type and studying "hard analysis" proofs. I just wonder if anyone has any suggestions or experiences to share of learning this kind of math.

Also, it seems that this kind of mathematics used to be more of a central focus, for example in the younger days of G.H. Hardy. Other mathematicians/scientists who seem to be / hav quite good at hard analysis include Freeman Dyson, Lars Onsager, George Polya. If anyone has any others to add to this list, I would be interested in hearing them also. I guess I'd be interested in seeing a "hard analysis" hall of fame. Of course it would have to include Euler, Gauss, and so on.

Finally, if anyone has any favorite results or papers in this vein, feel free to mention them as well.
 
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  • #2
The books
Boros and Moll, Irresistible Integrals
Nahin, Inside Interesting Integrals

are full of tricks for dealing with integrals.
 

1. What are some good books for learning classical "hard" analysis tricks?

Some popular books for classical "hard" analysis include "Real Analysis" by Royden and Fitzpatrick, "Principles of Mathematical Analysis" by Rudin, and "Calculus" by Spivak.

2. Are there any specific papers that are helpful for learning classical "hard" analysis tricks?

Yes, some notable papers in this area include "A Proof of the Fundamental Theorem of Calculus" by G. H. Hardy, "The Cauchy-Schwarz Master Class" by J. Michael Steele, and "The Fundamental Theorem of Calculus and Related Topics" by Terence Tao.

3. How can I practice applying classical "hard" analysis tricks?

One way to practice is by solving problems from textbooks or online resources, such as MIT OpenCourseWare or Khan Academy. You can also try proving theorems or solving exercises from the above-mentioned books and papers.

4. Are there any online resources for learning classical "hard" analysis?

Yes, there are many online resources available for learning classical "hard" analysis, such as lecture notes, video lectures, and interactive practice problems. Some popular websites include Coursera, edX, and Brilliant.

5. How can I apply classical "hard" analysis tricks to real-world problems?

Classical "hard" analysis is a fundamental tool in many branches of mathematics and physics. By mastering these tricks, you will have a solid foundation for tackling real-world problems in these fields. You can also look for applications in other areas, such as economics, engineering, and computer science, where analytical skills are highly valued.

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