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klkuttler.com

In college I was taught Linear Algebra from the operator point of view, rather than with matrices. That way theorems are clearer and the student's understanding is deeper, but for applications it's better to study from the matrix point of view and with lots of examples. Kuttler's book was refreshing in that sense. His other books are excellent, too. If you have been studying pure math (or french-style applied math which is just pure math with a concentration in Analysis) they are a light and fun complementary read.

Although I understand that matrix-soup is kind of the entropic endstate of all high school mathematics pedagogy, I think it is a real tragedy: In the UK especially even the best and brightest barely touch any real mathematics until after they leave secondary school so they leave with almost literally no idea of what university mathematics consists of. It's all well and good training people to be engineers, but the universities end up teaching the whole syllabus to them again in about 6 weeks. It's just shit.

The only reason why I am now doing theoretical physics (I was in the dumb group initially and worked my way up largely by myself) is because I read a calculus textbook by accident and got hooked when I was 14. Even when I made it to the top of the pile I still wasn't allowed to do anything more than calculus because the module system means we had to choose as a class whether to do group theory or not.

Good luck to you. I wish you every success. The education in this country has been shit for such a long time. It is and has been pretty devastating for so long.

Apologies for the negative waves.

No negativity measured, actually

I understand, I too had a similar experience in high school. My comment was largely referring to my context: This semester I'll finish a mathematical engineering degree (in my country engs. are 12 semesters long) and the abstract way is rewarding but without applications can only be endured so far. It does not help that a large fraction of mathematical engineers after they graduate end up doing some software work consisting in statistics, optimization or some other advanced mathetical concept. With a dropout rate of ~80%, not many people are willing to put themselves through rigorous analysis classes on top of the engineering requirements for so many years to get a job doing something you could have learned in a more practical way. But we are very well paid and the unemployment rate is 0% since there are maybe 10 graduates in the whole country each year.

The best, by far, book on Linear Algebra that elegantly teaches it from Vector Spaces and Linear Operators point of view is

Paul Halmos "Finite-Dimensional Vector Spaces"

For instance, the way Halmos introduces the determinant of a matrix (or an operator) is the most consistent, elegant and simple way I ever encountered. OTOH, in Kenneth Kuttler's LinAlg books the determinant is pulled out of the thin air like in 1000+ other similar books.

Thanks, I'll look it up. The best textbook from which I studied (operators) was Elon Lima's Algebra Linear. Sadly the only physical copies are sold in Brazil.

If you want to see the matrix point of view done well, there's Linear Algebra Done Wrong: https://www.math.brown.edu/streil/papers/LADW/LADW.html. You can read a bit about the motivation for doing it that way on that website.

The title is a reference to a somewhat well-known book, Linear Algebra Done Right, which avoids using determinants to develop the theory (resulting in a somewhat novel/cleaner presentation). It's unfortunately not freely available online (published by Springer – I would suspect most university students can get it freely through their library's website, however).

LADR was freely available at Springer at least at some point in time. Under their open access program. I couldn't find it again in a few minutes' search, so it may be gone.

Ah yes, a fellow Brazillian. I've alwys found Elon's book on Linear Algebra a masterpiece. Coupled with the exercises book, going through it is an eye opening experience.

While Halmos' book is lovely, I still prefer the geometric definition of determinant to the algebraic one: The determinant of a matrix is the signed volume (or area) of the parallellepiped spanned by its columns. Equivalently, the determinant of a linear map is the volume of the image of a unit cube by that map (or any arbitrary shape of volume one, not necessarily a cube). All the algebraic properties of the determinant follow easily from the geometric definition (multi-linearity, anti-symmetry, etc).

Really, I don't see what you like about Halmos definition of the determinant... I have just read it (page 99 of my copy) and he admits that it is a "somewhat roundabout procedure", just after giving the definition! There's other references that seem much cleaner (e.g. Spivak's calculus on manifolds, using exterior algebra).

> Really, I don't see what you like about Halmos definition of the determinant...

Halmos shows (it is almost trivial) that the space of anti-symmetric n-forms Wn over L_n is 1-dimensional. Wn(Ae1,...,Aen) = const*Wn(e1,...,en). This scalar const is called determinant. It has all the properties you would ascribe to Volume like volume spanned by collinear column-vectors is zero. This is a nice bridge to geometry in Ln. Also, in a space of just one page (p.99) he introduces determinant and proves its main properties like det(A*B) = det(A)*det(B) and therefore det(A^-1) = 1/det(A).

The determinant of n vectors {vi} relative to a particular basis {ei} in an n-dimensional vector space is the scalar-valued ratio:

( v1 ∧ v2 ∧ ··· ∧ vn ) / ( e1 ∧ e2 ∧ ··· ∧ en )

The signed volume per se is just the n-vector: v1 ∧ v2 ∧ ··· ∧ vn

Generally working with the wedge product is more pleasant and conceptually clearer than working with determinants. Among other things we don't need to make an arbitrary choice of basis or unit n-vector. There's also no reason to limit ourselves to n terms. v1 ∧ v2 is also a reasonable quantity to use, etc.

The beauty of Halmos' derivation, which is similar but not identical to exterior algebra (wedge product), is that his approach is basis independent. A determinant by his definition is scalar invariant over all bases. It is very geometrical in nature.

The determinant inherently involves a basis (or at the very least a choice of unit n-vector). Or if you like you can think of the determinant as a function of a square matrix (grid of numbers), rather than a function of a collection of vectors.

When you take the basis out, that's the wedge product, which inherently includes the orientation. Conveniently, there is only one degree of freedom for n-vectors in n-dimensional space. When we take the quotient of two n-vectors in n-dimensional space we therefore get a scalar.

Let me sketch a way to get the determinant basis-free:

Say we live in an n-dimensional vector space V and have an endomorphism f : V -> V. Now, we consider the pullback [1] f* : Λⁿ(V) -> Λⁿ(V) induced by f on the vector space of n-linear alternating forms Λⁿ(V) on V.

This is just an endomorphism on Λⁿ(V). However, Λⁿ(V) is one-dimensional, hence necessarily invariant under f*. This means f* has an eigenvalue (!). This eigenvalue is what we usually call the determinant of f.

This is completely independent of any choice of basis, orientation, or an inner product.

[1] That is, given an element w ∈ Λⁿ(V) and an arbitrary n-tuple v₁, ..., vₙ of vectors from V, we have (f*w)(v₁, ..., vₙ) = w(f(v₁), ..., f(vₙ))

> and have an endomorphism f

And the "outermorphism" f̱ of your linear transformation, when limited to considering its application to an arbitrary pseudoscalar, returns another pseudoscalar which necessarily has the same orientation, making that a scaling operation.

So what we could say in that case is that f̱(p) / p = d (some scalar, the "determinant" of f), where p is any pseudoscalar p = v1 ∧ v2 ∧ ··· ∧ vn.

This turns out to be about the same as what I wrote a few comments upthread. We are just dealing with

f̱( v1 ∧ v2 ∧ ··· ∧ vn ) / ( v1 ∧ v2 ∧ ··· ∧ vn ) = d

= ( f(v1) ∧ f(v2) ∧ ··· ∧ f(vn) ) / ( v1 ∧ v2 ∧ ··· ∧ vn )

instead of ( v1 ∧ v2 ∧ ··· ∧ vn ) / ( e1 ∧ e2 ∧ ··· ∧ en ) = d

And now we are talking about a property of a linear transformation instead of a property of a collection of n vectors.

In many practical situations, an oriented quantity like v1 ∧ v2 ∧ ··· ∧ vn is more useful than a scalar ratio d though.

If you define determinant as volume, how do you define volume? I agree that it's pedagogically sound to motivate the notion of determinant by the volume of a parallelepiped, but using volume as the definition of determinant just doesn't sound right.

And strictly speaking, determinant is not volume because the former is dimensionless. It is the scaling factor of the volume when a geometric entity is transformed by a linear map.

> If you define determinant as volume, how do you define volume?

How do you define "length" and "area"? I guess that if you don't have already a very firm grasp of these basic concepts, then there's no business for you (yet) in studying determinants. Much later, once you master thoroughly lengths, areas, volumes and hypervolumes; and also linear algebra and determinants (however they are defined), then you can embark in the elegant definitions using exterior algebra and the like. Notice that Halmos itself says that his treatment is appropriate for a *second* course in linear algebra, preparing the field for the later study of infinite-dimensional spaces.

> And strictly speaking, determinant is not volume because the former is dimensionless.

This really depends on the context. If you are working on euclidean space, you already have "units" and the determinant makes sense in itself, as the volume spanned by sets of vectors.

I find both the geometric and algebraic definitions quoted here unsatisfying. What is a “volume” spanned by a vector space of polynomials or co-tangent functionals?

*A* determinate function (not the) is simply a skew symmetric n-linear map into the underlying field.

Done. Now we get the volume interpretation when it’s appropriate, the wedge product interpretation, and the generalization to finitely generated projective modules (if a determinate function exists, there are additional conditions needed for the existence.)

Could you please talk about alternative ways of learning about linear algebra?

Did the same person write all these books, which altogether amount to upwards of 10k pages? From his website, he is quite an active research mathematician too. While just writing so many textbooks is a huge accomplishment in itself, the author has also made them available for free; I am lost for words at how prolific as well as generous the author is. I really want to know how this was possible, how long it took, and more about their motivation.

Another solid source of free math resources: https://realnotcomplex.com/

Does anybody know of an awesome list that has more of these kinds of "pdf textbooks" on math/phys/engineering?

> Does anybody know of an awesome list that has more of these kinds of "pdf textbooks" on math/phys/engineering?

Go to John Baez's awesome list "How to Learn Math and Physics":

https://math.ucr.edu/home/baez/books.html

And then to Library Genesis.

Thank you so much!

I have been adding a number of these free books at https://learnawesome.org/

Do you really care about the format being PDF or is it about the books being FREE? I'd like to make common queries like yours easier. LearnAwesome is open-source, so of course you're free to contribute: https://github.com/learn-awesome/learn

Oooh this is like a dmoz.org list from yesteryear when the web was useful. Bookmarked, Cheers!

Nerds love to bike shed crap like this and bite the hand that feeds.

To paraphrase a typical rant: “Making knowledge free and accessible is useless unless a libre format is used like Markdown.” Lol

The PDF beef is funnier in that people usually don’t know why they are morally opposed to PDF, it usually boils down to not liking Adobe Acrobat a decade ago, not accepting that page format preservation is a thing, or some beef with zooming or something. End of the day, it seeks to be digital paper.

End of the day, it’s an open standard with multiple implementations. Just by virtue of the US Federal Courts using it for millions of documents it will be usable for the foreseeable future, well beyond our lifetimes. (And there are many similar or even bigger examples)

Not just that, but if you actually look at the implementation its pretty understandable. 90% of use cases are covered by the easiest parts to parse too. PDF might not be perfect, but honestly not much comes close.

https://github.com/EbookFoundation/free-science-books/blob/m...

Kenneth Kuttler's books doesn't seem to be there though.

Check out the Open Textbook Library:

https://open.umn.edu/opentextbooks/

Thank you.

openstax.org

https://www.reddit.com/r/mathbooks/ is a good place to find free titles.

Not sure if you needed PDF specifically, but this site has a lot of good OER textbooks, digitized nicely as HTML + MathJax: https://math.libretexts.org/Bookshelves

There is this good old goldmine:

http://www.theassayer.org/cgi-bin/asbrowsesubject.cgi?class=...

Are the answers to the exercises to be found somewhere?

Probably not. That’s why most of these books are useless for people that self-study. There isn’t an external feedback mechanism to check your work. I’d recommend hiring a tutor. You can post online too, but the answers to your questions may vary.

I never understood why they do that - especially for something like this that's offered for free. What's the point of even including the exercises if there's no way to check the answers?

I wondered the same. I have no idea, other than to speculate. In this case, it might be out of habit (since this is usually how textbooks are written) or laziness. I can’t recommend any of these books for self-studies unless the person studying is fine with posting every exercise they do online for correctness checks or can hire a tutor.

It's really hard to do (at all, let alone well) but the point is to encode more information in the problems such that, by solving them oneself, the student gains direct understanding of some technique or application or extension that wasn't covered in the main text. In a (well-written) textbook there might be five or tens times as much information latent in the (solving of the) exercises than in the entire preceding chapter. Writing these kinds of sets of exercises is very challenging, much harder than just writing a textbook.

Wow, these look great! Does anyone know of any similar resources for mechanics, specifically the Hamiltonian & Lagrangian formulations? I've had a bit of trouble finding good resources online to supplement my mechanics modules at uni.

Based on "uni" I'm guessing you are at a UK institution (as am I).

The notes are utter dogshit because there's no culture of publishing them online for the public.

Kibble's classical mechanics book is deep, easy to read and cheap, I'd start there. Most physics books past a certain level end up with an increasingly hand-wavy derivation of the Euler-lagrange equations in some context

Look for Dover books, particularly from Russian authors. Cheap and good!

For linear algebra: Shilov.

I own four copies of that book :-)

Two hard copies since I moved countries, one on kindle and one on Apple Books.

Great book :D

I found these lecture notes by David Tong to be really good at a glance [1]. A free introductory physics book [2]. I don't have much physics stuff and I know almost nothing about it, I mostly focus on Math/CS, this is some stuff that I had bookmarked. Maybe someone else here can share some good Mechanics resources.

[1] http://www.damtp.cam.ac.uk/user/tong/dynamics.html [2] http://www.lightandmatter.com

Tong is good, most appropriate if you already know a little about the subject. However, it is a "fast-track" kind of resource, it is good to read some standard textbooks on the subject, i.e. Landau & Lifshitz (the most efficient book on theoretical mechanics ever) and Goldstein (broad and interesting).

Although I have immense respect for them I really don't like L&L all that much. The first one is pretty beautiful but it could use some material on the "deeper" side of things, particularly the connection with geometry and Noether's theorem. As for the others, they're just too "russian" - I like how oddly practical they are, but they're just not structured properly - StatPhys in particular just doesn't flow very well.

The typesetting is also a crime against humanity - they should be done similarly to the Feynman lectures, so typed up in LaTeX rather than whatever they are now

Regarding geometry and Noether's theorem, I disagree. That stuff is way overblown in general, it gives you very little ( in classical mechanics) for the time and page count the subject requires. I had a lecture on this topic from enthusiast well-respected physicist, and I kept thinking, this stuff is interesting math but completely useless to a physicist.

Classical mechanics is ultimately just masturbation compared to the "real thing", it's all a warm-up for the mind rather than teaching you how to calculate stresses on a beam - Full on symplectic geometry is niche but the basics of Noether's theorem and symmetries are central to classical and quantum field theories which are ultimately the language in which the cutting edge of fundamental physics speaks.

So I sort of agree, but take the two Oxford books on QFT and Statistical physics - they pack in enormous amounts of detail in a 3 or 4 hundred pages. There's room to spare if the book is the right shape.

What are the titles of the two Oxford books you mentioned? I'd like to look at them.

Quantum Field Theory for the gifted amateur (well not quite...)

Concepts in thermal physics

Not at the same level as L&L but they are absolute masterpieces in pedagogy and the formula should be extended to a 800page tome to cover more material. It's the way MTW should be written...

> could use some material on the "deeper" side of things

The first few chapters are precisely about the extraction of concrete conserved quantities from symmetries. A sufficiently "russian" reader will be able to see immediately the general case from these very complete examples. Noether's theorem is only missing in name, not in content.

> The typesetting is also a crime against humanity - they should be done similarly to the Feynman lectures, so typed up in LaTeX rather than whatever they are now

The typeseting of the french edition of L&L is extraordinarily beautiful. I'm not sure that is is possible to reach this typographical elegance with freely available LaTeX packages. On the other hand, the re-typed Feynman lectures are a disgrace, and an objectively worsening in quality from the first editions.

I liked that section a lot but I want a jumping off point. I don't think symmetries of the action or groups are discussed, IIRC.

I may have been a little vague when I said typesetting - the actual typesetting is a bit dull but the thing that is a disgrace, I should clarify, is that the fonts are terrible, and the text has that "Cheap Dover book" print quality (not far off a good typewriter).

To amplify this point, all the negative Amazon reviews praise the book but excoriate the print quality.

This must concern some paperback English print? My copy in French from "Editions MIR Moscou, 1969", hardcover, masterfully typeset and in bible paper is one of the finest books that I have.

Yes, I think it's specific to the English version. You can see examples if you use the Amazon.com "look inside" feature. For instance in volume 3, in the first pages already some of the footnotes are hardly readable.

For one of the volumes, a reviewer even posted a picture of his edition with subscripts printed so faint/partially that they were basically missing, and he had to Google the proper formulas!

Yes, I literally have RF engineering books (interesting reads actually) that are as old as my grandfather that have clean equations and beautiful text, in a lovely binding, but my L&L copies are both like an n-th order photocopy.

Yeah, I liked the mechanics and quantum volumes, but the choice of material in the stat mech books (both volumes) just seemed weird compared to what's usually done in a modern graduate course.

Do you have any recommendations for good stat mech books? I guess the two volume set by Kardar is trendy right now, but I'm always curious about other options.

I'm remiss to recommend as I'm still learning, but I absolutely love "Concepts in Thermal physics". It's embarrassingly well written and structured.

Beyond that the usual suspects are good enough for me, I just don't like L&L. I'm probably forgetting something.

It's not exactly statmech but I really like McComb's introductory renormalization methods book.

Thank you for the recommendations!

While I have not seen either of those books before, I have another book by the author of the first one, Stephen Blundell. It's Quantum Field Theory for the Gifted Amateur, and I think you might like it. Not as a standalone quantum book, but perhaps as a supplement to a more "serious" treatment.

edit: I just saw you recommended this below!

QFTFTGA is really great. Surprisingly hard as well.

I got it when I was 17 and it was an absolute godsend as its really cheap.

What an amazing amount of work to give the world for free. Thank you.

Under which license are these books published?

At the end of the One Variable Advanced Calculus table of contents is this text:

> Copyright © 2018, You are welcome to use this, including copying it for use in classes or referring to it on line but not to publish it for money. I do constantly upgrade it when I find things which could be improved.

I like this. The more free information the better.

Off topic: nice PDF rendering I wish normal PDF would be as fluent.

Indeed, LaTeX makes for really awesome results if you can put in the time to learn it (which is not a lot for basic usage). Plus it helps enforce a common style in all docs, and works well with Git.

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