The zero-field six-vertex (ice-type) model on a 4-regular graph assigns weights to the six ice-rule-satisfying vertex types and asks for the partition function over Eulerian orientations. Cai, Liu, and Lu (SODA 2019) gave a fully polynomial randomized approximation scheme (FPRAS) for all nonnegative parameters satisfying , , (the blue region) and proved NP-hardness of approximation in the ferroelectric/antiferroelectric phases (the grey region), leaving a gap: the white region of the disordered phase, together with the finer classification on planar 4-regular graphs and, most sharply, the point (equivalently ), where computing is equivalent to evaluating the Tutte polynomial on planar graphs — the critical, self-dual point of the 4-state ferromagnetic Potts model. The approximation status of the white region and of on planar graphs remains open.
Background
The six-vertex model (Pauling's ice model) is defined on a 4-regular graph (every vertex has degree 4): an arrow is placed on each edge so that at every vertex exactly two arrows point in and two point out (the ice rule); such an orientation is called an Eulerian orientation. Six local configurations are allowed at each vertex, grouped into three types with weights , , (two configurations per type), and the partition function is over all Eulerian orientations , where counts vertices of type . Computing is #P-hard for most parameters (#P is the counting analogue of NP, so #P-hardness means at least as hard as any counting problem), so the central question is approximability: an FPRAS (fully polynomial randomized approximation scheme) outputs a multiplicative estimate of in time polynomial in the input size and with high probability. In statistical mechanics the model on the square lattice has three phases: a ferroelectric phase when (or symmetrically ), an antiferroelectric phase when , and a disordered phase when the three triangle inequalities , , hold. On the curve (the free-fermion curve) the model is exactly computable by Pfaffians (determinantal signed sums, the Kasteleyn/FKT method) on planar graphs. Cai, Liu, and Lu (SODA 2019) proved: (1) an FPRAS exists on all 4-regular graphs whenever , , and (the blue region, which contains the ice point and the free-fermion curve), via a rapidly mixing Markov chain for windable constraint functions (windability, due to McQuillan, is a property of the weight function that yields canonical-path mixing proofs); and (2) approximating is NP-hard unless RPNP (RP being randomized polynomial time) in the grey region or or , coinciding with the ferroelectric/antiferroelectric phases. This leaves the white region: disordered-phase parameters with that violate at least one of the three quadratic inequalities — three lens-shaped regions between the cones and (and their symmetric images), including the phase-transition boundary . The problem has two further sharpenings. First, on planar graphs the model connects to the Tutte polynomial , the universal deletion–contraction invariant of graphs: Las Vergnas (1988) proved whenever is the medial graph of a plane graph (the medial graph of a plane graph has one vertex for each edge of , with two such vertices adjacent when the corresponding edges are consecutive around a vertex or face of ; every 4-regular plane graph arises this way), and every plane graph is available, so approximating the six-vertex model at the red point (normalized , on the boundary ) is exactly the problem of approximating on planar graphs. Via the Fortuin–Kasteleyn correspondence between the Tutte polynomial and the random-cluster/Potts model, is, up to an efficiently computable prefactor, the partition function of the ferromagnetic 4-state Potts model at inverse temperature (the random-cluster model at edge probability , which is precisely the planar self-dual, i.e. critical, point for ). Approximating is known to be #BIS-hard on general graphs (Goldberg–Jerrum 2012; #BIS is the benchmark problem of counting independent sets in bipartite graphs, believed intractable but resistant to NP-hardness proofs), and the planar inapproximability regions charted by Goldberg–Jerrum do not cover . Second, the NP-hardness in the grey region uses non-planar gadgets, so even the grey region's status on planar 4-regular graphs — the finer planar classification — is open. On the exact side the picture is complete: Cai–Fu–Shao (SODA 2021) classified exact computation of the six-vertex partition function for all complex parameters, discovering new planar polynomial-time cases beyond Kasteleyn's algorithm, and Cai–Fan–Shao–Tang (2026) completed the planar eight-vertex exact classification; but exact tractability does not decide approximation hardness or availability in the white region.
Problem Statement
Determine the approximation complexity of the zero-field six-vertex model partition function for parameters in the white region — all satisfying the triangle inequalities , , that violate at least one of the quadratic inequalities , , (including the phase-transition boundary ) — on 4-regular graphs: for each such parameter setting, does an FPRAS exist, is approximating NP-hard (or #BIS-hard), or does it have intermediate status? In particular, resolve the red point (equivalently ): is there an FPRAS for on planar graphs — the partition function of the ferromagnetic 4-state Potts model at its planar self-dual point — or is it hard to approximate? Finally, establish the finer approximation-complexity classification of restricted to planar 4-regular graphs, where the known NP-hardness reductions (which use non-planar gadgets) do not apply. The common objective is to complete the approximation-complexity classification of the six-vertex model over its nonnegative parameter space, in direct analogy with the already complete exact-computation classification. The three requested results are (i) the white-region classification on general 4-regular graphs, (ii) the finer planar classification of over the full parameter space on planar 4-regular graphs (covering the planar status of the grey region as well as of the white region), and (iii) the red point , equivalently on planar graphs; (i) and (ii) each require an independent answer, whereas (iii) is a single-point special case of (ii) called out separately because it is the sharpest instance.
References
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