We study a non-monotonic behavior of the Binder parameter, which appears in the systems with the Potts symmetry. Using the Fortuin-Kasteleyn graph representation, we find that the improved estimator of the Binder parameter consists of two terms with values only in high- and low-temperature regions. The non-monotonic behavior is found to originate from the low-temperature term. With the appropriately defined order parameter, we can reduce the influence of the low-temperature term, and as a result, the non-monotonic behavior can also be reduced. We propose new definitions of ordered parameter which reduces or eliminates the non-monotonic behavior of the Binder parameter in a system whose Fortuin-Kasteleyn representation is non-trivial.
We study the magnetization process of the $S=1$ Heisenberg model on a two-leg ladder with further neighbor spin-exchange interaction. We consider the interaction that couples up to the next-nearest neighbor rungs and find an exactly solvable regime where the ground states become product states. The next-nearest neighbor interaction tends to stabilize magnetization plateaus at multiples of 1/6. In most of the exactly solvable regime, a single magnetization curve shows two series of plateaus with different periodicities.
We investigate a quantum phase transition between a Neel phase and a valence bond solid (VBS) phase, in each of which a different Z2 symmetry is broken, in a spin-1/2 two-leg XXZ ladder with a four-spin interaction. The model can be viewed as a one-dimensional variant of the celebrated J-Q model on a square lattice. By means of variational uniform matrix product state calculations and an effective field theory, we determine the phase diagram of the model, and present evidences that the Neel-VBS transition is continuous and belongs to the Gaussian universality class with the central charge c=1. In particular, the critical exponents $beta, eta,$ and, $ u$ are found to satisfy the constraints expected for a Gaussian transition within numerical accuracy. These exponents do not detectably change along the phase boundary while they are in general allowed to do so for the Gaussian class.
A tensor network renormalization algorithm with global optimization based on the corner transfer matrix is proposed. Since the environment is updated by the corner transfer matrix renormalization group method, the forward-backward iteration is unnecessary, which is a time-consuming part of other methods with global optimization. In addition, a further approximation reducing the order of the computational cost of contraction for the calculation of the coarse-grained tensor is proposed. The computational time of our algorithm in two dimensions scales as the sixth power of the bond dimension while the higher-order tensor renormalization group and the higher-order second renormalization group methods have the seventh power. We perform benchmark calculations in the Ising model on the square lattice and show that the time-to-solution of the proposed algorithm is faster than that of other methods.
A utility-based Bayesian population finding (BaPoFi) method was proposed by Morita and Muller (2017, Biometrics, 1355-1365) to analyze data from a randomized clinical trial with the aim of identifying good predictive baseline covariates for optimizing the target population for a future study. The approach casts the population finding process as a formal decision problem together with a flexible probability model using a random forest to define a regression mean function. BaPoFi is constructed to handle a single continuous or binary outcome variable. In this paper, we develop BaPoFi-TTE as an extension of the earlier approach for clinically important cases of time-to-event (TTE) data with censoring, and also accounting for a toxicity outcome. We model the association of TTE data with baseline covariates using a semi-parametric failure time model with a Polya tree prior for an unknown error term and a random forest for a flexible regression mean function. We define a utility function that addresses a trade-off between efficacy and toxicity as one of the important clinical considerations for population finding. We examine the operating characteristics of the proposed method in extensive simulation studies. For illustration, we apply the proposed method to data from a randomized oncology clinical trial. Concerns in a preliminary analysis of the same data based on a parametric model motivated the proposed more general approach.
We present efficient algorithms to generate a bit string in which each bit is set with arbitrary probability. By adopting a hybrid algorithm, i.e., a finite-bit density approximation with correction techniques, we achieve 3.8 times faster random bit generation than the simple algorithm for the 32-bit case and 6.8 times faster for the 64-bit case. Employing the developed algorithm, we apply the multispin coding technique to one-dimensional bond-directed percolation. The simulations are accelerated by up to a factor of 14 compared with an optimized scalar implementation. The random bit string generation algorithm proposed here is applicable to general Monte Carlo methods.
A calculation method for higher-order moments of physical quantities, including magnetization and energy, based on the higher-order tensor renormalization group is proposed. The physical observables are represented by impurity tensors. A systematic summation scheme provides coarse-grained tensors including multiple impurities. Our method is compared with the Monte Carlo method on the two-dimensional Potts model. While the nature of the transition of the $q$-state Potts model has been known for a long time owing to the analytical arguments, a clear numerical confirmation has been difficult due to extremely long correlation length in the weakly first-order transitions, e.g., for $q=5$. A jump of the Binder ratio precisely determines the transition temperature. The finite-size scaling analysis provides critical exponents and distinguishes the weakly first-order and the continuous transitions.
An algorithm of the tensor renormalization group is proposed based on a randomized algorithm for singular value decomposition. Our algorithm is applicable to a broad range of two-dimensional classical models. In the case of a square lattice, its computational complexity and memory usage are proportional to the fifth and the third power of the bond dimension, respectively, whereas those of the conventional implementation are of the sixth and the fourth power. The oversampling parameter larger than the bond dimension is sufficient to reproduce the same result as full singular value decomposition even at the critical point of the two-dimensional Ising model.
The conventional tensor-network states employ real-space product states as reference wave functions. Here, we propose a many-variable variational Monte Carlo (mVMC) method combined with tensor networks by taking advantages of both to study fermionic models. The variational wave function is composed of a pair product wave function operated by real space correlation factors and tensor networks. Moreover, we can apply quantum number projections, such as spin, momentum and lattice symmetry projections, to recover the symmetry of the wave function to further improve the accuracy. We benchmark our method for one- and two-dimensional Hubbard models, which show significant improvement over the results obtained individually either by mVMC or by tensor network. We have applied the present method to hole doped Hubbard model on the square lattice, which indicates the stripe charge/spin order coexisting with a weak $d$-wave superconducting order in the ground state for the doping concentration less than 0.3, where the stripe oscillation period gets longer with increasing hole concentration. The charge homogeneous and highly superconducting state also exists as a metastable excited state for the doping concentration less than 0.25.
Two-dimensional Ising models on the honeycomb lattice and the square lattice with striped random impurities are studied to obtain their phase diagrams. Assuming bimodal distributions of the random impurities where all the non-zero couplings have the same magnitude, exact critical values for the fraction p of ferromagnetic bonds at the zero-temperature (T=0) are obtained. The critical lines in the p-T plane are drawn by numerically evaluating the Lyapunov exponent of random matrix products.
