1,569 research outputs found
A novel multigrid method for electronic structure calculations
A general real-space multigrid algorithm for the self-consistent solution of
the Kohn-Sham equations appearing in the state-of-the-art electronic-structure
calculations is described. The most important part of the method is the
multigrid solver for the Schroedinger equation. Our choice is the Rayleigh
quotient multigrid method (RQMG), which applies directly to the minimization of
the Rayleigh quotient on the finest level. Very coarse correction grids can be
used, because there is no need to be able to represent the states on the coarse
levels. The RQMG method is generalized for the simultaneous solution of all the
states of the system using a penalty functional to keep the states orthogonal.
The performance of the scheme is demonstrated by applying it in a few molecular
and solid-state systems described by non-local norm-conserving
pseudopotentials.Comment: 9 pages, 3 figure
On solving trust-region and other regularised subproblems in optimization
The solution of trust-region and regularisation subproblems which arise in unconstrained optimization is considered. Building on the pioneering work of Gay, Mor´e and Sorensen, methods which obtain the solution of a sequence of parametrized linear systems by factorization are used. Enhancements using high-order polynomial approximation and inverse iteration ensure that the resulting method is both globally and asymptotically at least superlinearly convergent in all cases, including in the notorious hard case. Numerical experiments validate the effectiveness of our approach. The resulting software is available as packages TRS and RQS as part of the GALAHAD optimization library, and is especially designed for large-scale problems
Pilot Optimization and Channel Estimation for Multiuser Massive MIMO Systems
This paper proposes novel pilot optimization and channel estimation algorithm
for the downlink multiuser massive multiple input multiple output (MIMO) system
with decentralized single antenna mobile stations (MSs), and time division
duplex (TDD) channel estimation which is performed by utilizing pilot
symbols. The proposed algorithm is explained as follows. First, we formulate
the channel estimation problem as a weighted sum mean square error (WSMSE)
minimization problem containing pilot symbols and introduced variables. Second,
for fixed pilot symbols, the introduced variables are optimized using minimum
mean square error (MMSE) and generalized Rayleigh quotient methods. Finally,
for and settings, the pilot symbols of all MSs are optimized using
semi definite programming (SDP) convex optimization approach, and for the other
settings of and , the pilot symbols of all MSs are optimized by applying
simple iterative algorithm. When , it is shown that the latter iterative
algorithm gives the optimal pilot symbols achieved by the SDP method.
Simulation results confirm that the proposed algorithm achieves less WSMSE
compared to that of the conventional semi-orthogonal pilot symbol and MMSE
channel estimation algorithm which creates pilot contamination.Comment: Accepted in CISS 2014 Conferenc
New Structured Matrix Methods for Real and Complex Polynomial Root-finding
We combine the known methods for univariate polynomial root-finding and for
computations in the Frobenius matrix algebra with our novel techniques to
advance numerical solution of a univariate polynomial equation, and in
particular numerical approximation of the real roots of a polynomial. Our
analysis and experiments show efficiency of the resulting algorithms.Comment: 18 page
Diffusion Approximations for Online Principal Component Estimation and Global Convergence
In this paper, we propose to adopt the diffusion approximation tools to study
the dynamics of Oja's iteration which is an online stochastic gradient descent
method for the principal component analysis. Oja's iteration maintains a
running estimate of the true principal component from streaming data and enjoys
less temporal and spatial complexities. We show that the Oja's iteration for
the top eigenvector generates a continuous-state discrete-time Markov chain
over the unit sphere. We characterize the Oja's iteration in three phases using
diffusion approximation and weak convergence tools. Our three-phase analysis
further provides a finite-sample error bound for the running estimate, which
matches the minimax information lower bound for principal component analysis
under the additional assumption of bounded samples.Comment: Appeared in NIPS 201
A robust adaptive algebraic multigrid linear solver for structural mechanics
The numerical simulation of structural mechanics applications via finite
elements usually requires the solution of large-size and ill-conditioned linear
systems, especially when accurate results are sought for derived variables
interpolated with lower order functions, like stress or deformation fields.
Such task represents the most time-consuming kernel in commercial simulators;
thus, it is of significant interest the development of robust and efficient
linear solvers for such applications. In this context, direct solvers, which
are based on LU factorization techniques, are often used due to their
robustness and easy setup; however, they can reach only superlinear complexity,
in the best case, thus, have limited applicability depending on the problem
size. On the other hand, iterative solvers based on algebraic multigrid (AMG)
preconditioners can reach up to linear complexity for sufficiently regular
problems but do not always converge and require more knowledge from the user
for an efficient setup. In this work, we present an adaptive AMG method
specifically designed to improve its usability and efficiency in the solution
of structural problems. We show numerical results for several practical
applications with millions of unknowns and compare our method with two
state-of-the-art linear solvers proving its efficiency and robustness.Comment: 50 pages, 16 figures, submitted to CMAM
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