9 research outputs found
Computing Optimal Experimental Designs via Interior Point Method
In this paper, we study optimal experimental design problems with a broad
class of smooth convex optimality criteria, including the classical A-, D- and
p th mean criterion. In particular, we propose an interior point (IP) method
for them and establish its global convergence. Furthermore, by exploiting the
structure of the Hessian matrix of the aforementioned optimality criteria, we
derive an explicit formula for computing its rank. Using this result, we then
show that the Newton direction arising in the IP method can be computed
efficiently via Sherman-Morrison-Woodbury formula when the size of the moment
matrix is small relative to the sample size. Finally, we compare our IP method
with the widely used multiplicative algorithm introduced by Silvey et al. [29].
The computational results show that the IP method generally outperforms the
multiplicative algorithm both in speed and solution quality
On the Analysis of the Discretized Kohn-Sham Density Functional Theory
In this paper, we study a few theoretical issues in the discretized Kohn-Sham
(KS) density functional theory (DFT). The equivalence between either a local or
global minimizer of the KS total energy minimization problem and the solution
to the KS equation is established under certain assumptions. The nonzero charge
densities of a strong local minimizer are shown to be bounded below by a
positive constant uniformly. We analyze the self-consistent field (SCF)
iteration by formulating the KS equation as a fixed point map with respect to
the potential. The Jacobian of these fixed point maps is derived explicitly.
Both global and local convergence of the simple mixing scheme can be
established if the gap between the occupied states and unoccupied states is
sufficiently large. This assumption can be relaxed if the charge density is
computed using the Fermi-Dirac distribution and it is not required if there is
no exchange correlation functional in the total energy functional. Although our
assumption on the gap is very stringent and is almost never satisfied in
reality, our analysis is still valuable for a better understanding of the KS
minimization problem, the KS equation and the SCF iteration.Comment: 29 page
A semismooth newton method for the nearest Euclidean distance matrix problem
The Nearest Euclidean distance matrix problem (NEDM) is a fundamentalcomputational problem in applications such asmultidimensional scaling and molecularconformation from nuclear magnetic resonance data in computational chemistry.Especially in the latter application, the problem is often large scale with the number ofatoms ranging from a few hundreds to a few thousands.In this paper, we introduce asemismooth Newton method that solves the dual problem of (NEDM). We prove that themethod is quadratically convergent.We then present an application of the Newton method to NEDM with -weights.We demonstrate the superior performance of the Newton method over existing methodsincluding the latest quadratic semi-definite programming solver.This research also opens a new avenue towards efficient solution methods for the molecularembedding problem
Forward-backward truncated Newton methods for convex composite optimization
This paper proposes two proximal Newton-CG methods for convex nonsmooth
optimization problems in composite form. The algorithms are based on a a
reformulation of the original nonsmooth problem as the unconstrained
minimization of a continuously differentiable function, namely the
forward-backward envelope (FBE). The first algorithm is based on a standard
line search strategy, whereas the second one combines the global efficiency
estimates of the corresponding first-order methods, while achieving fast
asymptotic convergence rates. Furthermore, they are computationally attractive
since each Newton iteration requires the approximate solution of a linear
system of usually small dimension
Analysis of Nonsmooth Symmetric-Matrix-Valued Functions with Applications to Semidefinite Complementarity Problems
For any function f from R to R, one can define a corresponding function on the space of n × n (block-diagonal) real symmetric matrices by applying f to the eigenvalues of the spectral decomposition. We show that this matrix-valued function inherits from f the properties of continuity, (local) Lipschitz continuity, directional differentiability, Fréchet differentiability, continuous differentiability, as well as ( -order) semismoothness. Our analysis uses results from nonsmooth analysis as well as perturbation theory for the spectral decomposition of symmetric matrices. We also apply our results to the semidefinite complementarity problem, addressing some basic issues in the analysis of smoothing/semismooth Newton methods for solving this problem
A dual optimization approach to inverse quadratic eigenvalue problems with partial eigenstructure
The inverse quadratic eigenvalue problem (IQEP) arises in the field of structural dynamics. It aims to find three symmetric matrices, known as the mass, the damping, and the stiffness matrices, such that they are closest to the given analytical matrices and satisfy the measured data. The difficulty of this problem lies in the fact that in applications the mass matrix should be positive definite and the stiffness matrix positive semidefinite. Based on an equivalent dual optimization version of the IQEP, we present a quadratically convergent Newton-type method. Our numerical experiments confirm the high efficiency of the proposed method