  We analyze to what extent the random SAT and Max-SAT problems differ in their
properties. Our findings suggest that for random $k$-CNF with ratio in a
certain range, Max-SAT can be solved by any SAT algorithm with subexponential
slowdown, while for formulae with ratios greater than some constant, algorithms
under the random walk framework require substantially different heuristics. In
light of these results, we propose a novel probabilistic approach for random
Max-SAT called ProMS. Experimental results illustrate that ProMS outperforms
many state-of-the-art local search solvers on random Max-SAT benchmarks.
