What are some algorithms where space complexity tends to be the limiting factor in practice? Time complexity can't be any lower than space complexity (at least one operation is required to use a unit of memory), so what are some algorithms where space actually tends to be the limiting factor? It puts a hard upper bound on what you can do, whereas time is only a factor if you're 'impatient.'
Preferably something practical.
Most computations in algebraic geometry / commutative algebra.
Most involve computing Grobner bases, which are EXPSPACE-hard in general. There are some parameter regimes where this improves and thus some computations can reasonably be done in practice (e.g. using Macaulay2 or SINGULAR), but very often it quickly eats up all the space and crashes.  I think one of the first papers to look at this was Bayer &amp; Mumford &quot;What can be computed in algebraic geometry?&quot;.
(FWIW, my recent experience with these programs has been that there is a trichotomy: either the answer returns in (1) seconds, (2) a few minutes, or (3) so long that you give up / so much memory that it crashes.)