Uniform Random-3-SAT is a family of SAT problems
distributions obtained by randomly generating 3-CNF formulae
in the following way:
For an instance with n variables and k clauses,
each of the k clauses is constructed from 3 literals
which are randomly drawn from the 2n possible literals
(the n variables and their negations)
such that each possible literal is selected with the same probability
Clauses are not accepted for the construction of the problem
instance if they contain multiple copies of the same literal
or if they are tautological (i.e., they contain a variable
and its negation as a literal).
Each choice of n and k thus induces a distribution
of Random-3-SAT instances. Uniform Random-3-SAT is the
union of these distributions over all n and k.
Forced vs. unforced generation
Random-3-SAT instances can be satisfiable or unsatisfiable.
Since the behaviour of SAT algorithms can be expected to
differ significantly between satisfiable and unsatisfiable instances,
we seperated these into different test-sets using
a complete algorithm. As complete SAT algorithms have usually
considerable higher run-times than (incomplete) SLS algorithms,
this approach for generating benchmark problems is computationally
very expensive, especially when dealing with larger problem sizes.
Therefore, especially when dealing with satisfiable instances,
it would be preferable to generate the instances
in a way such that their satisfiability (or unsatisfiability) is guaranteed
For generating satisfiable instances, one possibility for achieving this
is to randomly determine
a variable assignment B at the beginning of the generation process.
Then, the clauses are generated as before, but only clauses which are
compatible with B (i.e., clauses which have B as a model)
are accepted for the formula F to be constructed. Thus,
F has at least one model (namely B).
The problem distributions thus obtained are called
``forced'' uniform Random-3-SAT problems; earlier literature on SLS
algorithms for SAT used these for evaluating algorithms, until
it was noticed that these problems tend to be much easier
to solve for SLS algorithms then ``unforced'' problems obtained
as described before. It has been speculated for some time that
the reason for this phenomenon might be that in forced formulae,
the clause are somehow guiding the SLS algorithm into the right
One particularly interesting property of uniform Random-3-SAT
is the occurence of a phase transition phenomenon [CKT91], i.e.,
a rapid change in solubility which can be observed when
systematically increasing (or decreasing) the number of k clauses
for fixed problem size n.
More precisely, for small k almost all formulae are
satisfiable; at some critical k=k', the probability
of generating a satisfiable instance drops sharply to almost zero.
Beyond k', almost all instances are unsatisfiable.
Intuitively, k' characterises the transition between
a region of underconstrained instances which are almost
certainly soluble, to overconstrained instanced which are mostly
insoluble. For Random-3-SAT, this phase transition occurs
approximately at k' = 4.26 n for large n;
for smaller n, the critical clauses/variable ratio
k'/n is slightly higher. Furthermore, for growing n
the transition becomes increasingly sharp.
The phase transition would not be very interesting in the context
of evaluating SLS algorithms, didn't empirical analyses show that
problems from the phase transition region of uniform Random-3-SAT
tend to be particularly hard for both systematic SAT solvers
and SLS algorithms .
Striving for testing their algorithms on hard problem instances,
many researchers used test-sets sampled from the phase transition
region of uniform Random-3-SAT (see for some examples).
Although, similar phase transition phenomena have been observed
for other subclasses of SAT, including uniform Random-k-SAT
with k >= 4, but these have never gained the popularity of
uniform Random-3-SAT . Maybe, one of the reasons for this is
the the prominent role of 3-SAT as a prototypical and syntactically
particularly simple NP-complete problem.
The benchmark test-sets
The test-sets provided here are sampled from the phase transition
region of uniform Random-3-SAT. The instances were generated in an
unforced way and were separated into test-sets of satisfiable and
unsatisfiable instances . The characteristics of these
test-sets are shown in Table 1. The smallest
problems (20 variables) are typically used for studying scaling
properties. Due to limited computational resources,
only the test-sets for n=20,50,100 contain
1000 instances, while for larger problem sizes
each test-set contains 100 instances.
| test-set || instances
|| clause-len || vars || clauses
| uf20-91 || 1000
|| 3 || 20 || 91
| uf50-218 / uuf50-218|| 1000
|| 3 || 50 || 218
| uf75-325 / uuf75-325 || 100
|| 3 || 75 || 325
| uf100-430 / uuf100-430 || 1000
|| 3 || 100 || 430
| uf125-538 / uuf125-538 || 100
|| 3 || 125 || 538
| uf150-645 / uuf150-645 || 100
|| 3 || 150 || 645
| uf175-753 / uuf175-753 || 100
|| 3 || 175 || 753
| uf200-860 / uuf200-860 || 100
|| 3 || 200 || 860
| uf225-960 / uuf225-960 || 100
|| 3 || 225 || 960
| uf250-1065 / uuf250-1065 || 100
|| 3 || 250 || 1065
Table 1: Uniform Random-3-SAT test-sets;
test-sets uf* contain only satisfiable instances,
uuf* only unsatisfiable instances.