Determining computational complexity from characteristic 'phase transitions'

Non-deterministic polynomial time (commonly termed 'NP-complete') problems are relevant to many computational tasks of practical interest-such as the 'travelling salesman problem'-but are difficult to solve: the computing tim e grows exponentially with problem size in the worst case. It has recently been shown that these problems exhibit 'phase boundaries', across which dra matic changes occur in the computational difficulty and solution character- the problems become easier to solve away from the boundary. Here we report an analytic solution and experimental investigation of the phase transition in K-satisfiability, an archetypal NP-complete problem. Depending on the i nput parameters, the computing time may grow exponentially or polynomially with problem size; in the former case, we observe a discontinuous transitio n, whereas in the latter case a continuous (second-order) transition is fou nd. The nature of these transitions may explain the differing computational costs, and suggests directions for improving the efficiency of search algo rithms. Similar types of transition should occur in other combinatorial pro blems and In glassy or granular materials, thereby strengthening the link b etween computational models and properties of physical systems.