A new augmented penalty function technique for optimal control problems

This note presents an extension of the Miele—Cragg-Iyer-Levy augmented function method for finite-dimensional optimization problems to optimal control problems. A numerical study is provided.     

Pseudo-duality and saddle points

Results associated with saddle-type stationary points are described. It is shown that barrier-type functions are pseudo-duals of generalized Lagrangian functions, while augmented Lagrangians are pseudo-duals of the regular Lagrangian function. An application of pseudo-duality to a min-max problem is illustrated, together with several other examples.     

Effect of local atomic disorder on the half-metallicity of full-Heusler Co2FeSi alloy: a first-principles study

This paper investigates the effect of atomic disorder on the electronic structure, magnetism, and half-metallicity of full-Heusler Co2FeSi alloy by using the full-potential linearized augmented plane wave method within the generalized gradient approximation (GGA) and GGA+U schemes. It considers three types of atomic disorders in Co2FeSi alloy: the Co-Fe, Co-Si, and Fe-Si disorders. Total energy calculations show that of the three types of disorders, the Fe-Si disorder is more likely to occur. It finds that for the Co-Si disorder, additional states appear in the minority band-gap at the EF and the half-metallcity is substantially destroyed, regardless of the disorder level. On the other hand, the Co-Fe and Fe-Si disorders have little effect on the half-metallicity at a low disorder level. When increasing the disorder levels, the half-metallcity is destroyed at about 9 % of the Co-Fe disorder level, while that stays at 25 % of the Fe-Si disorder level.     

医学虚拟仿真仪器的开发研究

通过对日本Sysmex XE-2100型血细胞分析仪的三维数字化重建和仿真功能开发研究,分析了虚拟现实软件EON Studio的技术特点和软件优势及其在虚拟医学仪器开发中的技术关键。该项研究工作为虚拟现实技术运用于医学教育和培训提供了可借鉴的经验。     

Using a factored dual in augmented Lagrangian methods for semidefinite programming

In the context of augmented Lagrangian approaches for solving semidefinite programming problems, we investigate the possibility of eliminating the positive semidefinite constraint on the dual matrix by employing a factorization. Hints on how to deal with the resulting unconstrained maximization of the augmented Lagrangian are given. We further use the approximate maximum of the augmented Lagrangian with the aim of improving the convergence rate of alternating direction augmented Lagrangian frameworks. Numerical results are reported, showing the benefits of the approach.     

Accelerated method for optimization over density matrices in quantum state estimation

In this paper, a class of minimization problems over density matrices arising in the quantum state estimation is investigated. By making use of the Nesterov’s accelerated strategies, we introduce a modified augmented Lagrangian method to solve it, where the subproblem is tackled by the projected Barzilai–Borwein method with nonmonotone line search. Compared with the existing projected gradient method, several numerical examples are tested to show that the proposed method is efficient and promising.     

基于VRML的三维虚拟场景的构建与优化

本文对基于VRML的虚拟场景的设计与实现进行了研究,并从减少文件长度,提高渲染速度两方面提出了优化场景的方法。     

Strong Reality Properties of Normalizers of Parabolic Subgroups in Finite Coxeter Groups

Let W be a finite Coxeter group, P a parabolic subgroup of W, and N _W (P) the normalizer of P in W. We prove that every element in N _W (P) is strongly real in N _W (P), and that every irreducible complex character of N _W (P) has Frobenius-Schur indicator 1.     

Efficient linear solvers for incompressible flow simulations using Scott‐Vogelius finite elements

Recent research has shown that in some practically relevant situations like multiphysics flows (Galvin et al., Comput Methods Appl Mech Eng, to appear) divergence‐free mixed finite elements may have a significantly smaller discretization error than standard nondivergence‐free mixed finite elements. To judge the overall performance of divergence‐free mixed finite elements, we investigate linear solvers for the saddle point linear systems arising in ((P_k)^d,P _k‐1^disc) Scott‐Vogelius finite element implementations of the incompressible Navier–Stokes equations. We investigate both direct and iterative solver methods. Due to discontinuous pressure elements in the case of Scott‐Vogelius (SV) elements, considerably more solver strategies seem to deliver promising results than in the case of standard mixed finite elements such as Taylor‐Hood elements. For direct methods, we extend recent preliminary work using sparse banded solvers on the penalty method formulation to finer meshes and discuss extensions. For iterative methods, we test augmented Lagrangian and \begin{align*}\mathcal{H}\end{align*} ‐LU preconditioners with GMRES, on both full and statically condensed systems. Several numerical experiments are provided that show these classes of solvers are well suited for use with SV elements and could deliver an interesting overall performance in several applications.© 2012 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2013     

Portfolio selection with a minimax measure in safety constraint

In this paper, we attempt to design a portfolio optimization model for investors who desire to minimize the variation around the mean return and at the same time wish to achieve better return than the worst possible return realization at every time point in a single period portfolio investment. The portfolio is to be selected from the risky assets in the equity market. Since the minimax portfolio optimization model provides us with the portfolio that maximizes (minimizes) the worst return (worst loss) realization in the investment horizon period, in order to safeguard the interest of investors, the optimal value of the minimax optimization model is used to design a constraint in the mean-absolute semideviation model. This constraint can be viewed as a safety strategy adopted by an investor. Thus, our proposed bi-objective linear programming model involves mean return as a reward and mean-absolute semideviation as a risk in the objective function and minimax as a safety constraint, which enables a trade off between return and risk with a fixed safety value. The efficient frontier of the model is generated using the augmented -constraint method on the GAMS software. We simultaneously solve the ratio optimization problem which maximizes the ratio of mean return over mean-absolute semideviation with same minimax value in the safety constraint. Subsequently, we choose two portfolios on the above generated efficient frontier such that the risk from one of them is less and the mean return from other portfolio is more than the respective quantities of the optimal portfolio from the ratio optimization model. Extensive computational results and in-sample and out-of-sample analysis are provided to compare the financial performance of the optimal portfolios selected by our proposed model with that of the optimal portfolios from the existing minimax and mean-absolute semideviation portfolio optimization models on real data from S&P CNX Nifty index.