Hey, I've been on your waitlist for quite a while! When can I buy your product? I've been on the verge of building my own EEG due to a lack of quality options.
Interestingly, no Kindle version of The Power Broker is available, although there were reports in 2021 that one was purchasable on Amazon for a very short time [1]. I really dislike handling large books, especially if I'm reading in a casual setting. Because of this, I decided to start reading The Years of Lyndon Johnson series instead, which do have Kindle versions.
I recently started reading the The Years of Lyndon Johnson and desperately want to see the documentary mentioned in the article, Turn Every Page. Alas, it doesn't seem to be playing anywhere near me [1]. Maybe other people reading will have a chance.
Can this idea be turned on its head to engineer adversarial legal language that sounds reasonable but actually has terrible implications for the signee?
To my knowledge, it is an open challenge to find real-world problem instances where existing quantum annealers outperform state-of-the-art classical alternatives. It's even hard to find _extremely contrived families of instances_ where practical computational benefits can be observed with quantum annealing. I recently contributed to one such benchmarking manuscript [1], which may indicate future promise as the number of qubits in quantum annealers continues to increase.
Anyway, as a result of that experience, I'm skeptical of the benchmarking efforts that led these two companies to the conclusion that quantum annealing is more cost-effective than dropping in a classical alternative as the inner optimization solver. D-Wave even has some reasonably efficient, open-source algorithms that can be used as a point of comparison (e.g., [2]). I'd be interested in reading more about the companies' benchmarks, as this article is very light on details.
Here's an excerpt from a comment I previously made on Hacker News:
I'm a Ph.D. student in operations research (OR). My suggestion would be to first build a strong foundation in linear programming. This will introduce you to the notion of duality, which is heavily emphasized in many mathematical programming courses. Here's a good open-source book on linear programming written by Jon Lee, the current editor of Mathematical Programming A: https://github.com/jon77lee/JLee_LinearOptimizationBook
Then I'd suggest studying more general methods for continuous and convex optimization. The book I see mentioned a lot is Convex Optimization by Boyd and Vandenberghe, although we didn't use this in our coursework. Instead, we used a lot of the material presented here: http://mitmgmtfaculty.mit.edu/rfreund/educationalactivities/
If you read the above (or any other two books on linear programming and convex optimization), you'll probably have a better idea of what you want to study next and how you want to go about it. The next natural step would be to study combinatorial (i.e., integer or mixed-integer) optimization. (Jon Lee has another book on this subject; I've also heard good things about the Schrijver book.)
I cannot register with my University of Michigan email. It states, "You are not listed as a PhD candidate." What's the algorithm that determines if I'm a Ph.D. candidate?