[molf]

It's not possible to learn anything about other elements when performing binary search, _except_ the only thing there is to learn: if the target is before or after the recently compared element.

If we would guess that there is a bias in the distribution based on recently seen elements, the guess is at least as likely to be wrong as it is to be right. And if we guess incorrectly, in the worst case, the algorithm degrades to a linear scan.

Unless we have prior knowledge. For example: if there is a particular distribution, or if we know we're dealing with integers without any repetition (i.e. each element is strictly greater than the previous one), etc.

[kryptiskt]

> It's not possible to learn anything about other elements when performing binary search, _except_ the only thing there is to learn: if the target is before or after the recently compared element.

You have another piece of information, you don't only know if the element was before or after the compared element. You can also know the delta between what you looked at and what you're looking for. And you also have the delta from the previous item you looked at.

[LorenPechtel]

Assuming your key space is anything like randomly distributed.

Thinking about it--yeah, if you can anticipate anything like a random distribution it's a few extra instructions to reduce the number of values looked up. In the old days that would have been very unlikely to be a good deal, but with so many algorithms dominated by the cache (I've seen more than one case where a clearly less efficient algorithm that reduced memory reads turned out better) I suspect there's a lot of such things that don't go the way we learned them in the stone age.

[wtallis]

And you always start off knowing the total length of the array, and the width of the datatype.

Actually deciding what to do with that information without incurring a bunch more cache misses in the process may be tricky.

[xenadu02]

Is the disconnect here that in many datasets there is some implicit distribution? For example if we are searching for english words we can assume that the number of words or sentences starting with "Q" or "Z" is very small while the ones starting with "T" are many. Or if the first three lookups in a binary search all start with "T" we are probably being asked to search just the "T" section of a dictionary.

Depending on the problem space such assumptions can prove right enough to be worth using despite sometimes being wrong. Of course if you've got the compute to throw at it (and the problem is large) take the Contact approach: why do one when you can do two in parallel for twice the price (cycles)?

[travisjungroth]

> If we would guess that there is a bias in the distribution based on recently seen elements, the guess is at least as likely to be wrong as it is to be right.

This is true for abstract and random data. I don't think it's true for real world data.

For example, python's sort function "knows nothing" about the data you're passing in. But, it does look for some shortcuts and these end up saving time, on average.