Quote from: kTimesG on Today at 09:49:37 AM

Quote from: mcdouglasx on October 01, 2024, 10:08:33 PM

You lack long-term vision, I assure you that BSGS with a sufficiently large database in the future, would double your current speed with Kangaroo, and to surpass it you would have to double your computing power, on the contrary BSGS would only have to store more keys, this is the interesting point of this research, which is not limited to how much computing power you have, and I'm not saying that Kangaroo is not efficient, I'm just saying that it depends on the net computing power, which is already beginning to be seen as a stone in the shoe.

You might as well be right. I started this off-topic with <Space x Time> is a constant, so obviously if you use a ton of "space" then you need less "time" to solve the same problem.

This is valid no matter what algorithm you use. So I got it now - you want to store lots and lots of keys, in less space, maybe so more keys fit is fast memory (RAM?).

The fallacy in your logic is however this one: you are thinking only in terms of a system that actually uses memory in a fast way, and comparing two algorithms on this same system. In this case, the one that trades off more memory for less time is BSGS. I wish you good luck with that.

However, if we start comparing BSGS vs Kangaroo on a system that trades off slow/no memory with a lot more computing power, then what you find is that BSGS does not even apply (since memory is either really really slow, or non-existent), and an algorithm that is based on computing power alone, will always outperform it, simply because the amount of extra computing far surpasses any level of storage you optimized for, on a system with a low amount of computing power.

Your theory is biased by your own interests, so please stop spamming here. Your theories without test models are just that: theories based on what you believe.

Sometimes, what seems logical in theory doesn’t always work in practice. This can be due to many factors, such as unconsidered variables, implementation errors, or simply because reality is more complex than anticipated.

Let me tell you a story about the arrogance of thinking you know everything and how it can lead to self-humiliation. When Marilyn vos Savant published her solution to the Monty Hall problem in 1990, she received a lot of criticism, especially from mathematicians and statisticians. More than 1,000 people with doctorates wrote to the magazine where she published her response to tell her she was wrong. Over time, her solution was accepted and became a classic example of how intuition can fail in probabilistic problems.

In short, you are only hindering research with your supposed theoretical intuitions without real scientific basis in practice. Respect the community. Of your five messages, all contain fallacies that do not represent reality because you speak from misinformation. That’s why you were so disliked when you used the name @digaran. Self-analyze, bro. If you find this topic useless, just ignore it.