Andreas Galanis, L. A. Goldberg, Andrés Herrera-Poyatos
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引用次数: 4
Abstract
We study the complexity of approximating the partition function of the q-state Potts model and the closely related Tutte polynomial for complex values of the underlying parameters. Apart from the classical connections with quantum computing and phase transitions in statistical physics, recent work in approximate counting has shown that the behaviour in the complex plane, and more precisely the location of zeros, is strongly connected with the complexity of the approximation problem, even for positive real-valued parameters. Previous work in the complex plane by Goldberg and Guo focused on q = 2, which corresponds to the case of the Ising model; for q > 2, the behaviour in the complex plane is not as well understood and most work applies only to the real-valued Tutte plane. Our main result is a complete classification of the complexity of the approximation problems for all non-real values of the parameters, by establishing #P-hardness results that apply even when restricted to planar graphs. Our techniques apply to all q $$\geq$$ ≥ 2 and further complement/refine previous results both for the Ising model and the Tutte plane, answering in particular a question raised by Bordewich, Freedman, Lovász and Welsh in the context of quantum computations.
期刊介绍:
computational complexity presents outstanding research in computational complexity. Its subject is at the interface between mathematics and theoretical computer science, with a clear mathematical profile and strictly mathematical format.
The central topics are:
Models of computation, complexity bounds (with particular emphasis on lower bounds), complexity classes, trade-off results
for sequential and parallel computation
for "general" (Boolean) and "structured" computation (e.g. decision trees, arithmetic circuits)
for deterministic, probabilistic, and nondeterministic computation
worst case and average case
Specific areas of concentration include:
Structure of complexity classes (reductions, relativization questions, degrees, derandomization)
Algebraic complexity (bilinear complexity, computations for polynomials, groups, algebras, and representations)
Interactive proofs, pseudorandom generation, and randomness extraction
Complexity issues in:
crytography
learning theory
number theory
logic (complexity of logical theories, cost of decision procedures)
combinatorial optimization and approximate Solutions
distributed computing
property testing.