LPs can prove optimality of a result but sometimes this is to expensive and you stop as soon as you reach near optimality based on known bounds.
For several problems you can prove lower and/or upper bounds for the optimal results. For example you can prove that a route between two points may not be shorter than their distance.
If you use LP in production you often don't care about optimality and can work with suboptimal results very well. Worst case you stop after a timeout and just pick the best result.
Edit: I forgot to mention that during solving of a LP you usually get dynamically computed bounds. The branch and bounds algorithm works by computing bounds on relaxed problems that give upper bounds for the original problem.
The biggest problem is heating in winter, which requires way more energy than cooling in summer. So for colder areas doubling Panels will not be enough.
You are right in that there is a class of algorithms for which it is possible to do that. It is just not possible to decide if any algorithm is in that specific class of algorithms or not. There is an even smaller class of algorithms that can be constructed bottom up from halting sub algorithms. But these are not as powerful.
You cannot approximate NP-complete functions. If you could approximate them with a practically useful limited error and at most P effort you would have solved P=NP. (disclaimer my computer science classes have been a long time ago)
Nobody is arguing that the usecases are the same. In the end you can't even chat with gzip (although you could with it's predictor).
The thing is, that building the predictor is almost the same thing for compression and LLM. Of course the goals and taken tradeoffs are different. The paper shows this analogy.
ChatGPT et al use structured prediction to simulate intelligence. Building the predictor is fancy lossy compression.
Questions arise if lossy compression of things without copyright is legal or not. If I mp3 a lossless recording we currently think it is not legal. With LLMs this is not entirely clear yet.
I have been able to fix these random lags by doing multiple full disk reads. The first one will take very long, because it will trigger these lags. Subsequent ones will be much better.
The leading theory I have read is that maintenance/refreshing on the ssd is not done preventative/correctly by the firmware and you need to trigger it by accessing the data.
Maybe I'm biased, but in my opinion crime risk is higher in the US, where you have "no-go-zones" than ok most parts of Western Europe, where this concept does not really exist.
Why is there no optimization in place for this? Converting a=x or a=y or a=z to a in(x,y,z) should be trivial and the db should have heuristics to calculate the expected query cost to decide when to apply this transformation.
For several problems you can prove lower and/or upper bounds for the optimal results. For example you can prove that a route between two points may not be shorter than their distance.
If you use LP in production you often don't care about optimality and can work with suboptimal results very well. Worst case you stop after a timeout and just pick the best result.
Edit: I forgot to mention that during solving of a LP you usually get dynamically computed bounds. The branch and bounds algorithm works by computing bounds on relaxed problems that give upper bounds for the original problem.