Introduction to vector spaces

In summary, the conversation suggests two book recommendations for an introduction to functional analysis, with Kreyszig's "Introductory functional analysis with applications" being considered the easiest and Conway's "A course in functional analysis" being more difficult due to its assumption of prior knowledge in topology. Another option mentioned is Shilov's two books, "Linear Algebra" and "Elementary Functional Analysis," which are said to provide a smooth transition from finite dimensional vector spaces to infinite dimensional vector spaces. However, it is noted that functional analysis is a difficult topic and even the easiest book may still be challenging for self-study.
  • #1
Ahmad Kishki
159
13
i want a book that smoothly takes me from finite dimensional vector spaces to infinite dimensional vector spaces. Edit: I am doing this as self study, so i would prefer the book to be easy going without an instructor

Thanks
 
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  • #2
Kreyszig (Introductory functional analysis with applications) is considered the easiest introduction to functional analysis. I haven't read it though.

Conway (A course in functional analysis) is extremely hard to read (because he skips details and assumes that you're already very good at topology), but I think he does the stuff about orthonormal bases better than anyone else.
 
  • #3
Th
Fredrik said:
Kreyszig (Introductory functional analysis with applications) is considered the easiest introduction to functional analysis. I haven't read it though.

Conway (A course in functional analysis) is extremely hard to read (because he skips details and assumes that you're already very good at topology), but I think he does the stuff about orthonormal bases better than anyone else.

Thank you, but will these recommendations be easy as self study?
 
  • #4
Ahmad Kishki said:
Thank you, but will these recommendations be easy as self study?
Kreyszig: Maybe. This is supposed to be the easiest book, but it's a difficult topic, so even the easiest book may be difficult.

Conway: Definitely not. You would need to spend at least a couple of months studying topology before you give this book a shot. If you know just a little topology however, you can try to take a look at the stuff on orthonormal bases. It's easier than the rest of the book.
 
  • #5
You could try Shilov's two books: Linear Algebra and Elementary Functional Analysis. The transition would be very smooth indeed.
 

Related to Introduction to vector spaces

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of objects, called vectors, which can be added together and multiplied by numbers, called scalars. It follows a set of axioms that govern its operations, making it a powerful tool for solving problems in linear algebra and other areas of mathematics and science.

2. What are the properties of a vector space?

A vector space must satisfy several properties, including closure under vector addition and scalar multiplication, associativity and commutativity of addition, existence of an additive identity element, and existence of additive and multiplicative inverse elements. It must also follow the distributive law and have a scalar multiplication identity element.

3. How do you determine if a set is a vector space?

To determine if a set is a vector space, you must check if it satisfies all of the properties listed above. If it satisfies all of the properties, it can be considered a vector space. If it fails to satisfy even one of the properties, it is not a vector space.

4. What are some examples of vector spaces?

Some common examples of vector spaces include the set of real numbers, the set of complex numbers, and the set of n-dimensional vectors. Other examples include the space of polynomials, the space of continuous functions, and the space of matrices.

5. How are vector spaces used in science?

Vector spaces have a wide range of applications in science, including physics, engineering, computer science, and economics. They are used to model physical quantities such as velocity, force, and electric fields. In computer science, they are used for data storage and manipulation, while in economics, they are used to model supply and demand. Vector spaces also play a crucial role in machine learning and data analysis.

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