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> seems like a boil the ocean kind of approach and I’m not sure if that’s ever going to be optimal

Finding the most optimal traveling salesman problem for 20 cities by brute force will take a few universes lifetimes and you still won’t be halls way. You’ll need to compromise with heuristics but that usually leads to near optimal

My personal experience has been that the simpler undergrad algorithms are the most applicable. That’s probably also why you learn them first.

IMO, it’s much more practical to thoroughly learn and understand the simpler techniques. There is often more depth to it, than you’d think.

Analogy: Many (most?) MMA matches are won with techniques you learn in the first year.

Aside: I’ve gotten much more mileage out of simple statistics in the 16 years since I graduated, than advanced algorithms.

EDIT:

IMO, theories and techniques can be easy enough to learn. Figuring out applications can be much more difficult. It requires some creativity.

Same, too bad that recently (based on my hunch) these gems are getting less attention and LLM BS (or AI) is dominating the scene.

If it makes you feel better, I've known the author personally for over ten years, and he is both a human being and an excellent teacher. I'm about as confident as I could imagine being that he wrote this book himself. (In order for this to completely reassure you, you of course have to trust me, but I thought I'd add my vote of confidence just in case it helps even a little.)

This book aside, though, the fact that writing is no longer definitive evidence of human thought is one of the things I dislike the most about the LLM era. I very much share your sadness about that.

AI tech is hitting our lives harder and faster than any tech before, and this has social and psychological side effects. society at large will struggle to recognize this, much less find healthy andeffective ways to adapt.

the erosion of trust will be socially catastrophic.

remember a time where you might admire a photo not just for its beauty, but for the good luck and skill of the photographer who captured it?

photoshop made us skeptical about truth, but AI will simply make it hopeless to ascertain truth.

note that i use AI extensively. i am hyper aware of the way it has impacted me, my peers, and my career, and im deeply concerned about how it will impact everyone as it is increasingly made "non-optional" by the rich and greedy who stand to benefit most from it.

You know this is easily verifiable right…. I just put 10 random pages into Pangram and they all came back 100% human. And now I feel bad for making an even greater proportion of comments on this thread not about graph theory.

Putting aside whether or not the author used AI to write this - the concern here is that the content is accurate and correct, right? So suppose they did use AI to write it - your end goal is actually the same - you want a trustworthy, accurate, source of information.

In this sense AI does not actually detract from that - so long as there IS an author associated to the book, that author can build a reputation for thoroughly reviewed and high quality textbooks. So in the end, some authors will be known for a reputation for making good books, and the use of AI should give us access to a larger quantity of books, addressing more and more niche topics, for lower costs. So, this should be a good thing for education?

I'm very much just writing my thoughts down, but curious to know if others agree or disagree with this.

I spot-checked a few random chapters, and it looks clearly human-written to me. But I share your general worry.

The author seems to have published math papers (as one can see on DBLP and other websites) and a PhD in maths. It gives me confidence that it's written by a human and also reading chapter 1 and chapter 2 I think there is a human behind it, not an LLM.

I get your sentiment, though the problem is older than AI, the convenience and market penetration has just ramped up the visibility in this arena.

Many books pre-ai were garbage but not at the rate they could be today. If anything people operate on greater discretion now than in the past because even the worst garbage still had a non-trivial barrier to entry, either sitting down to regurgitate your own garbage or paying someone to make some garbage for you.

There's also some historic precedence of this, the rise of Photoshop/CG and digital manipulation certainly led to a "is this photo real" to similar effect despite manipulations going back to the origins of photography. That acute concern was not as prevalent in my mind before editing became common and accessible.

While the author and book here seem trustworthy, the issue is real and likely more upon all of us. Nothing that we output will escape the same scrutiny, at least for a while until we will probably have own dedicated AI to scrutinize everything that gets in front of us

There has been plenty of crap released in the past, and even in the AI age we should be able to look at the same signals to filter through it today.

I have just taken this in stride but it has struck me a few times recently that everything I see now.. I basically have to classify it as to be immediately discarded or worth my time to even begin reading, and even if I do continue reading then I still have to be constantly evaluating if a person bothered to struggle with (write) it or not.

Quite frankly this is.. fucking terrible? Even worse, the end of something for humanity..?

Perhaps we’ll reach a point where humans might have to record themselves writing the things and publish this next to the work itself to prove it was human made.

I've asked myself this question as well.

Where I've landed -- once I get away from the distaste for load-bearing or a comment mentioning "spine" -- is that if the idea pushes me forward, challenges me towards improving, or otherwise informs, I'm okay with AI assisted writing.

I'm not okay with formulaic garbled nonsense, that annoys me!

IMO if i can learn from/enjoy the content then it makes no difference who/what wrote it. I'll even pay for it if I feel the content is equal in value to the price.

I see a perfectly good, non-AI submission. Open the comments, and the top comment is about AI.

Can we not do this? I understand your sentiment, but ... can we not do this?

Reading through the first chapter, I got the feeling that it is more human than a lot of the books you might find on O'Reilly or wherever. I totally support the use of AI to help with organizing material in better ways or creating different modalities for representation. I really hate when the book is very clearly just copy pasting the output of the LLM.

Also, saw your comment response, I do feel this very same guarding and unfortunately my instinct is to assume the content is AI, which is a disservice to all the people who are putting their hard effort into putting out quality things.

There was not a single real tangable way of determining this before AI Slop.

For sure it was less to review, less options but still.

So what did you use? I used things like Reviews, skimming through, getting a referal through a blog or hn and buying from reputational sources.

HN is a filter. Same as all the other things we have.

Let's say the book was written by AI. Can you you explain what makes you less excited about this stuff?

Like is there a concrete issue with the book you have?

This is what we are supposed to have book and movie reviewers for. Just to check for slop regardless if the slop is AI or human.

But who reviews the reviewers?

What is the problem if it is AI written? Properly guided AI writes better than most human expositors.

math is arguably in a worse place than software

Just use hashmap for everything.

I have studied algorithms in college. I believe the goals of leetcode vs the goals of graph theory may be against each other.

A good graph theory course abstracts away the details of code and pursues the goal of understanding how many problems can be expressed concisely and clearly through graphs.

A good leetcode practice ... honestly just hones how patient you are in debugging and memorising various code patterns.

If you want to pursue money, it's almost always better to pursue leetcode exclusively

Thanks for that, i was initials skeptical i would understand but this very clearly resembles the candidate paths for walkers in variatonal monte carlo simulation of spin systems. The edge set can be different, requiring "half filled" and flipping both a 1 and a 0 simultaneously. I'd not thought of these processes in terms of a graph, much less graphs which denote very high dimensional cubes with strange edges.

*Zotero

AHH. Definition 1.4 and the questions after did it. It went from "Tracking all" to "I don't understand how this follows at all, and this makes no sense to me" 1.3b is a graph, not a union of two graphs?. What are the "two graphs"? What is this operator-like algebra, and what does it have to do with the rest of the definition or the text before and after it? A question about the complement which was never defined? Why are we probably agreeing that {1, 2} contain 3?

Update: Better phrasing I can understand:

"Definition 1.4. The union of two graphs G and H, denoted <>, is a single graph containing all vertices and edges from both. If G and H share no vertices in common, <> is a disjoint union."

For 1.5, I had no idea what the written definition meant, but this does: ``` Definition 1.5. The complement of graph G is the one that has the same vertices, two vertices are connected in G_complement if and only if they are not connected in G.

The book you want in this case is Gross, Yellen & Anderson, Graph Theory and its Applications, 3rd Edition.

There are plenty of ways to get a copy.

You can ask Claude for the solutions?

agreed, even as a graph background :(

For fun. All knowledge is beneficial, including what may not directly help with your job, but nonetheless will make you smarter and better at solving problems. This over-technical approach to knowledge is actually sad...

Graphs theory, like in general? Just as one example, an awful lot of numerical linear algebra research is devoted to the handling of sparse matrices (ones where most of the entries are zeroes), which can also be looked at as graphs. Likely any CAD program with a finite element analysis tool uses some graph theory under the hood.

How so?