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Determining computational complexity from characteristic ‘phase transitions’

Nature volume 400, pages 133–137 (1999)Cite this article

Abstract

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 time grows exponentially with problem size in the worst case. It has recently been shown that these problems exhibit ‘phase boundaries’, across which dramatic 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 input parameters, the computing time may grow exponentially or polynomially with problem size; in the former case, we observe a discontinuous transition, whereas in the latter case a continuous (second-order) transition is found. The nature of these transitions may explain the differing computational costs, and suggests directions for improving the efficiency of search algorithms. Similar types of transition should occur in other combinatorial problems and in glassy or granular materials, thereby strengthening the link between computational models and properties of physical systems.

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Figure 1: The SAT/UNSAT phase transition for the (2 + p) SAT problems for a range of values of p and N.
Figure 2: Theoretical and experimental results for the SAT/UNSAT transitions in the (2 + p)-SAT model.
Figure 3: The median computation cost (number of backtracks) of proving a formula SAT or UNSAT in the (2 + p)-SAT model for a range of values of p.
Figure 4: Backbone fractions as a function of α for 2-SAT and 3-SAT.

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Acknowledgements

S.K. thanks the Laboratories of Theoretical and Statistical Physics at Ecole Normale Superieure for a visiting professorship in 1998, during which this work was completed. B.S. is an Alfred P. Sloan research fellow and is supported by an NSF Faculty Early Career Development Award. During the early phase of this research, B.S. was at AT&T Bell Laboratories.

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Authors and Affiliations

  1. CNRS-Laboratoire de Physique Théorique, 24, Rue Lhomond, 75231, Paris Cedex, France

    Rémi Monasson

  2. The Abdus Salam International Centre for Theoretical Physics, Strada Costieri 11, Trieste, 34100, Italy

    Riccardo Zecchina

  3. IBM, Thomas J. Watson Research Center, Yorktown Heights, 10598, New York, USA

    Scott Kirkpatrick

  4. Computer Science Department, Cornell University, Ithaca, 14853, New York, USA

    Bart Selman

  5. Institute of Computer Science, The Hebrew University, Jerusalem, 91904, Israel

    Lidror Troyansky

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  1. Rémi Monasson
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  4. Bart Selman
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  5. Lidror Troyansky
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Correspondence to Scott Kirkpatrick.

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Monasson, R., Zecchina, R., Kirkpatrick, S. et al. Determining computational complexity from characteristic ‘phase transitions’. Nature 400, 133–137 (1999). https://doi.org/10.1038/22055

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