Sublinear Computational Time Modeling by Momentum-Space Renormalization Group Theory in Statistical Machine Learning Procedures

Time-Dependent Real-Space Renormalization Group Method

In this paper, using the tight-binding model, we extend the real-space renormalization group method to time-dependent Hamiltonians. We drive the time-dependent recursion relations for the renormalized tight-binding Hamiltonian by decimating selective sites of lattice iteratively. The formalism is then used for the calculation of the local density of electronic states for a one dimensional quant...

time-dependent real-space renormalization group method

in this paper, using the tight-binding model, we extend the real-space renormalization group method to time-dependent hamiltonians. we drive the time-dependent recursion relations for the renormalized tight-binding hamiltonian by decimating selective sites of lattice iteratively. the formalism is then used for the calculation of the local density of electronic states for a one dimensional quant...

Momentum-Space Renormalization Group Transformation in Bayesian Image Modeling by Gaussian Graphical Model

Probabilistic graphical models based on Bayesian statistics are powerful tools for carrying out statistical inferences Probabilistic graphical models are regarded as an application of classical spin systems from statistical-mechanical viewpoint . Moreover, these provide many useful applications, not only for image processing but also for other inference systems in high-dimensional data driven s...

Density-matrix renormalization-group method in momentum space.

A momentum-space approach of the density-matrix renormalization-group ~DMRG! method is developed. Ground-state energies of the Hubbard model are evaluated using this method and compared with exact diagonalization as well as quantum Monte Carlo results. It is shown that the momentum-space DMRG is a very useful numerical tool for studying the Hubbard model and other fundamental models of interact...

Computational and Statistical Learning Theory