Hidemaro Suwa

Markov Chain Monte Carlo Method without Detailed Balance

We present a specific algorithm that generally satisfies the balance condition without imposing the detailed balance in the Markov chain Monte Carlo. In our algorithm, the average rejection rate is minimized, and even reduced to zero in many relevant cases. The absence of the detailed balance also introduces a net stochastic flow in a configuration space, which further boosts up the convergence. We demonstrate that the autocorrelation time of the Potts model becomes more than 6 times shorter than that by the conventional Metropolis algorithm. Based on the same concept, a bounce-free worm algorithm for generic quantum spin models is formulated as well.

Velocity of excitations in ordered, disordered, and critical antiferromagnets

We test three different approaches, based on quantum Monte Carlo simulations, for computing the velocity

Geometric allocation approaches in Markov chain Monte Carlo

c

Geometric Allocation Approach in Markov Chain Monte Carlo

of triplet excitations in antiferromagnets. We consider the standard

Semiclassical dynamics of spin density waves

S=1/2

Upper and lower critical decay exponents of Ising ferromagnets with long-range interaction.

one- and two-dimensional Heisenberg models, as well as a bilayer Heisenberg model at its critical point. Computing correlation functions in imaginary time and using their long-time behavior, we extract the lowest excitation energy versus momentum using improved fitting procedures and a generalized moment method. The velocity is then obtained from the dispersion relation. We also exploit winding numbers to define a cubic space-time geometry, where the velocity is obtained as the ratio of the spatial and temporal lengths of the system when all winding number fluctuations are equal. The two methods give consistent results for both ordered and critical systems, but the winding number estimator is more precise. For the Heisenberg chain, we accurately reproduce the exactly known velocity. For the two-dimensional Heisenberg model, our results are consistent with other recent calculations, but with an improved statistical precision,

Magnetization Process of the S = 1/2 Two-Leg Organic Spin-Ladder Compound BIP-BNO

c=1.65847(4)

Stochastic Approximation of Dynamical Exponent at Quantum Critical Point

. We also use the hydrodynamic relation

Multi-Chain Spin-Peierls Systems

{c}^{2}={\ensuremath{\rho}}_{s}/{\ensuremath{\chi}}_{\ensuremath{\perp}}(q\ensuremath{\rightarrow}0)

\(XXZ\) Spin-Peierls Chain

between