Variational estimates using a discrete variable representation
نویسندگان
چکیده
منابع مشابه
Using Discrete Variable Representation and Toeplitz Matrices
A direct and exact method for calculating the density of states for systems with localized potentials is presented. The method is based on explicit inversion of the operator E−H. The operator is written in the discrete variable representation of the Hamiltonian, and the Toeplitz property of the asymptotic part of the obtained infinite matrix is used. Thus, the problem is reduced to the inversio...
متن کاملBessel-zernike discrete variable representation basis.
The connection between the Bessel discrete variable basis expansion and a specific form of an orthogonal set of Jacobi polynomials is demonstrated. These so-called Zernike polynomials provide alternative series expansions of suitable functions over the unit interval. Expressing a Bessel function in a Zernike expansion provides a straightforward method of generating series identities. Furthermor...
متن کاملContinuous Discrete Variable Optimization of Structures Using Approximation Methods
Optimum design of structures is achieved while the design variables are continuous and discrete. To reduce the computational work involved in the optimization process, all the functions that are expensive to evaluate, are approximated. To approximate these functions, a semi quadratic function is employed. Only the diagonal terms of the Hessian matrix are used and these elements are estimated fr...
متن کاملVery highly excited vibrational states of LiCN using a discrete variable representation
Calculations are presented for the lowest 900 vibrational (J = 0) states of the LiCN floppy system for a two dimensional potential energy surface (rcN frozen). Most of these states lie well above the barrier separating the two linear isomers of the molecule and the point where the classical dynamics of the system becomes chaotic. Analysis of the wavefunctions of individual states in the high en...
متن کاملDiscrete-valued Neural Networks Using Variational Inference
The increasing demand for neural networks (NNs) being employed on embedded devices has led to plenty of research investigating methods for training low precision NNs. While most methods involve a quantization step, we propose a principled Bayesian approach where we first infer a distribution over a discrete weight space from which we subsequently derive hardware-friendly low precision NNs. To t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Physical Review A
سال: 2004
ISSN: 1050-2947,1094-1622
DOI: 10.1103/physreva.70.032503