Sometimes it feels like the more I learn, the less I know.
(It is OK to feel that way; so did great thinkers like MrSocrates.) I'm yearning for a little bit of certainty right now, so I was hoping to track some of the problems that have been solved. I mean,
really solved. Understood completely. We know what algorithms solve the problem best in each given situation, and we have proven that no better algorithms exist in each situation than those we have found.
I'll try to start (though I'm not certain that these problems really
are completely solved):
These problems are really vicious in chip design. The problem of addition/subtraction carry propagation is literally the limiting factor on the clock cycle of a CPU. I am unaware of a proof that the best currently known methods of doing fast carry lookahead/propagation are in fact theoretically best; if anyone knows otherwise, enlighten me.  dm
"we have proven that no better algorithms exist " 
oops, sorry, no, the opposite is true...the SpeedupTheorem? proved that there is no fastest algorithm; there's always a faster one.
That doesn't apply to O(n) analysis, where we do know that e.g. sorting based on compare and swap cannot be faster than O(nlogn)  but in practice multiplicative and additive constants can in fact matter. Also obviously we don't know whether P = NP; everyone doubts it, but if it turned out to be the case, then a vast number of important algorithms might have much faster solutions than currently known.  dm
The problem of addition/subtraction carry propagation is literally the limiting factor on the clock cycle of a CPU.
True at one time. But I've been told that the first level of cache is now the limiting factor  make the cache bigger, and it slows down the cycle time of the entire CPU. (Make the cache smaller, and you get more misses, which makes the CPU take more cycles to execute a given bit of code). 
DavidCary
You are absolutely right, but that is a separate issue, one that would disappear if external DRAM were getting faster fast enough. The problem you're talking about is due to DRAM speeds not keeping pace with CPU demand; their Moore's Law curves are different. This put increasing pressure on the SRAM caches between the two, so this is a side effect rather than being fundamental  although it certainly does matter.
I'm sorry to say I must complain that all of your examples below address only the page title, but not his further characterization of "what algorithms solve the problem best". You're listing problems where we've found some solution; the full list of such must be in the tens of millions.
OK, I deleted a few of my nonbest solutions.
Is it OK to list algorithms when it's been shown that no other algorithm can do better?
How about proofs that a particular problem cannot be solved?
Problem: All
hard drives fail sooner or later.
Solution: Write everything to an array of at least 2 drives (preferably more). When one fails, replace it with a fresh drive, then synchronize before the next one fails. (RAID).
(If we assume that hard drives are the only costeffective online storage media, this is the only solution. Of course this begs the question of whether hard drives are really the best.)
Cryptography
It is impossible to decrypt messages encoded using a "onetime pad" of random numbers, unless you have that pad.
There are no other algorithms that can possibly make encrypted messages *more* secure.
"Password handling is simultaneously one of the few Solved Problems of Cryptography *and* one of the most misunderstood. Simply store a MD5 or SHA1 oneway hash of the password."
Except that's still vulnerable to RainbowTables?  use salting please. And even good salts are vulnerable to brute force searching for bad passwords, so really... ad infinitum.

http://www.doxpara.com/read.php/security/password_rejected.html
Is this one safe from my critique? It's claiming I may misunderstand it. :)
Q: Is it possible for one program, in a finite amount of time, to decide whether some arbitrary other program gets stuck in an infinite loop, or eventually finishes?
A: No.
HaltingProblem.
Compression
If we have a set of messages with known probability, and we want to indicate 1 of them with a sequence of bits, canonical Huffman compression gives the minimum expected number of bits.
(Other types of encoding might give the *same* expected number of bits, but no other algorithm can possibly give *fewer* bits).
 Actually, no. Your statement is closer to true of arithmetic encoding, which generalizes Huffman encoding; it's not true of Huffman encoding itself. You're making a pretty strong claim, actually; I think you need to tone down the wording and also add more specificity to make it more defensible even of arithmetic coding.
 To elaborate, huffman encoding rounds the size of each symbol to an integral bit size. Entropy encoding, in general, does not. Standard arithmetic encoding rounds to a particular precision, usually a rational number in the form n/2^32 or so, so it is still suboptimal. Ideal (unrounded) entropy encoding is optimal in the case that the ONLY possible a priori knowledge of the sequence of symbols is the probabilities.  AdamBerger
(puzzled look) I don't quite see why it is incorrect. If I want to indicate *more* than 1 message, then arithmetic encoding may be able to transmit the whole sequence of messages with fewer total bits than Huffman. If the receiver *doesn't* know the probabilities, some other algorithm may be able to transmit messages with fewer total bits. (Especially if there is some sort of correlation between messages). But in the case of 1 message (and known probabilities), it is impossible to beat Huffman.
(I would be very surprised and delighted to find even a single exception.)

DavidCary
I see. This makes me uncomfortable, because I think there's always a way to examine some algorithm, and then after the fact come up with a very specific problem for which that algorithm is the best solution, which loses discriminatory value for "best".
In this case the general problem seems to me to be compression, and you are pointing out that there is some specific problem within that domain that Huffman encoding is best for, if we are careful enough about our phrasing of the problem.
Maybe it's reasonable to allow Huffman encoding here, because maybe in some sense it fits the spirit of the page, but if we do, what's to stop me from coming up with a carefully stated problem for which e.g. BogoSort is the best solution? How do we keep the dam from crumbling?
If I said "1 symbol" instead of "1 message", would that be less misleading?
 Thanks for the succinct elaboration. I should add that there is no single perfect compression algorithm for all data. One can optimize compression based on whatever information is known about possible inputs, but there are potentially an infinite number of types of statistical characterization of data, and what is optimal for one kind of distribution will not be for other distributions. And of course, perfectly random data is not compressible (RandomNumber). Then again, finite sequences are not truly random. It's a hairy subject.
Yes, that's true. I was just trying to point out that for a few distributions, there *is* a perfect compression algorithm. It's a Solved Problem In Computer Science.
Unfortunately, most files don't have that sort of distribution :(.

DavidCary
SortingAlgorithms
Q: Is there a sorting algorithm known to be the best?
A:
Yes and no. No, in the sense that you can custom design a RadixSort (not just use an off the shelf radix sort) to be optimal if you have enough information about potential input data. Yes, in that we know that it can be done by some radix sort in time proportional to O(n) if do that, and yes, in the sense that we know we can't do better than O(n log n) if we don't know anything about the input data and aren't allowed to use anything but compare and swap.
(Note that there is no magic bullet. Yes, clever data structures can make sorting appear O(n) but Ocomplexity is really measured in the space*time domain. People often simplify it to just time since the space is usually constant. An Order(N) sort may use O(N^2) space to guarantee the performance; you may use some slick data structure to reduce that space (hashing, tries, etc) but on closer examination you'll find degenerate cases where the time complexity is once again increased.)
But it's not a solved problem, exactly, because there's still the question of decreasing the multiplicative constant even for the O(n log n) ideal. A new refinement of QuickSort (Bentley and Sedgewick, IIRC) has appeared in recent years that does better in that regard.
There also is still no known algorithm for guaranteeing
O(n log n) performance on any old input data. Some of the best algorithms perform very badly on almostsorted data, or on reversesorted data, etc. In regard to QuickSort, there is no proof of the best way to pick pivots, yet picking good ones is critical to its performance.
Usually, to know the "best" would mean that further research is pointless, and this is not at all the case for sorting.

 Careful... HeapSort for example is guaranteed to be O(n log n) on any old input data where comparisons are of constant cost. The interesting issue is really taking good advantage of ideal cases while guaranteeing O(n log n) in the worst case, while maintaining a reasonable constant: one could conceivably write an algorithm which gives a whole list of algs exactly the amount of time that a guaranteed O(n log n) alg would take before timing them out, and falling back onto the O(n log n) only after expiring the rest. Such an algorithm would give O(a n log n), where 'a' is a constant factor in the worst case, and therefore has no bearing on the final O; as such, it would still be O(n log n), but in most practical applications, the overhead would kill you.
 Thanks for the correction; MergeSort is also guaranteed O(n log n). QuickSort has often been nonetheless the industrial sort of choice because it tends to be twice as fast as HeapSort in the average case.
 Even if heapsort is properly implemented?
 Yes, because each heap percolation is exactly log n (average case = worst case). Other algorithms do less work if the data is in "good form" (average case < worst case).
 The tryallalgorithms approach would be fun on the right kind of parallel system.

 Of course, this is not to imply that further research is pointless, because the constant factors/overhead can still be improved, and those gains are still useful. Indeed, some of the constant factors may not actually be constant; comparison of two arbitrary numbers, for instance, is really of the same complexity as comparing two strings, on commonly available architectures and sufficiently large numbers. To a sufficiently anal observer, bigO notation must take these things into account.
 If you mean bignums, it's not anal, it's just the right way to go. We can neglect these things on 32 bit ints only because we currently favor hardware that makes 32 bit operations all constant cost (either approximately or literally). On bit serial CPUs, they are not.

  cwillu