73 research outputs found
A Regularized Newton Method for Computing Ground States of Bose-Einstein condensates
In this paper, we propose a regularized Newton method for computing ground
states of Bose-Einstein condensates (BECs), which can be formulated as an
energy minimization problem with a spherical constraint. The energy functional
and constraint are discretized by either the finite difference, or sine or
Fourier pseudospectral discretization schemes and thus the original infinite
dimensional nonconvex minimization problem is approximated by a finite
dimensional constrained nonconvex minimization problem. Then an initial
solution is first constructed by using a feasible gradient type method, which
is an explicit scheme and maintains the spherical constraint automatically. To
accelerate the convergence of the gradient type method, we approximate the
energy functional by its second-order Taylor expansion with a regularized term
at each Newton iteration and adopt a cascadic multigrid technique for selecting
initial data. It leads to a standard trust-region subproblem and we solve it
again by the feasible gradient type method. The convergence of the regularized
Newton method is established by adjusting the regularization parameter as the
standard trust-region strategy. Extensive numerical experiments on challenging
examples, including a BEC in three dimensions with an optical lattice potential
and rotating BECs in two dimensions with rapid rotation and strongly repulsive
interaction, show that our method is efficient, accurate and robust.Comment: 25 pages, 6 figure
On the Convergence of the Self-Consistent Field Iteration in Kohn-Sham Density Functional Theory
It is well known that the self-consistent field (SCF) iteration for solving
the Kohn-Sham (KS) equation often fails to converge, yet there is no clear
explanation. In this paper, we investigate the SCF iteration from the
perspective of minimizing the corresponding KS total energy functional. By
analyzing the second-order Taylor expansion of the KS total energy functional
and estimating the relationship between the Hamiltonian and the part of the
Hessian which is not used in the SCF iteration, we are able to prove global
convergence from an arbitrary initial point and local linear convergence from
an initial point sufficiently close to the solution of the KS equation under
assumptions that the gap between the occupied states and unoccupied states is
sufficiently large and the second-order derivatives of the exchange correlation
functional are uniformly bounded from above. Although these conditions are very
stringent and are almost never satisfied in reality, our analysis is
interesting in the sense that it provides a qualitative prediction of the
behavior of the SCF iteration
Gradient type optimization methods for electronic structure calculations
The density functional theory (DFT) in electronic structure calculations can
be formulated as either a nonlinear eigenvalue or direct minimization problem.
The most widely used approach for solving the former is the so-called
self-consistent field (SCF) iteration. A common observation is that the
convergence of SCF is not clear theoretically while approaches with convergence
guarantee for solving the latter are often not competitive to SCF numerically.
In this paper, we study gradient type methods for solving the direct
minimization problem by constructing new iterations along the gradient on the
Stiefel manifold. Global convergence (i.e., convergence to a stationary point
from any initial solution) as well as local convergence rate follows from the
standard theory for optimization on manifold directly. A major computational
advantage is that the computation of linear eigenvalue problems is no longer
needed. The main costs of our approaches arise from the assembling of the total
energy functional and its gradient and the projection onto the manifold. These
tasks are cheaper than eigenvalue computation and they are often more suitable
for parallelization as long as the evaluation of the total energy functional
and its gradient is efficient. Numerical results show that they can outperform
SCF consistently on many practically large systems.Comment: 24 pages, 11 figures, 59 references, and 1 acknowledgement
An Alternating Direction Algorithm for Matrix Completion with Nonnegative Factors
This paper introduces an algorithm for the nonnegative matrix
factorization-and-completion problem, which aims to find nonnegative low-rank
matrices X and Y so that the product XY approximates a nonnegative data matrix
M whose elements are partially known (to a certain accuracy). This problem
aggregates two existing problems: (i) nonnegative matrix factorization where
all entries of M are given, and (ii) low-rank matrix completion where
nonnegativity is not required. By taking the advantages of both nonnegativity
and low-rankness, one can generally obtain superior results than those of just
using one of the two properties. We propose to solve the non-convex constrained
least-squares problem using an algorithm based on the classic alternating
direction augmented Lagrangian method. Preliminary convergence properties of
the algorithm and numerical simulation results are presented. Compared to a
recent algorithm for nonnegative matrix factorization, the proposed algorithm
produces factorizations of similar quality using only about half of the matrix
entries. On tasks of recovering incomplete grayscale and hyperspectral images,
the proposed algorithm yields overall better qualities than those produced by
two recent matrix-completion algorithms that do not exploit nonnegativity
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