A Quasi-Newton Penalty Barrier Method for Convex Minimization Problems

We describe an infeasible interior point algorithm for convex minimization problems. The method uses quasi-Newton techniques for approximating the second derivatives and providing superlinear convergence. We propose a new feasibility control of the iterates by introducing shift variables and by penalizing them in the barrier problem. We prove global convergence under standard conditions on the problem data, without any assumption on the behavior of the algorithm.     

时域有限差分法在光波导分析中的应用及改进

对时域有限差分算法在圆柱坐标下弱导光纤中应用时吸收边界条件的设置及其对计算精度的影响进行了讨论，提出了改善的方法。并且将这种方法应用于一般光纤和光电子晶体光纤传输特性的计算，得到了有用结果。     

Feature selection algorithm in classification learning using support vector machines

An algorithm for selecting features in the classification learning problem is considered. The algorithm is based on a modification of the standard criterion used in the support vector machine method. The new criterion adds to the standard criterion a penalty function that depends on the selected features. The solution of the problem is reduced to finding the minimax of a convex-concave function. As a result, the initial set of features is decomposed into three classes—unconditionally selected, weighted selected, and eliminated features. Original Russian Text Yu.V. Goncharov, I.B. Muchnik, L.V. Shvartser @, 2008, published in Zhurnal Vychislitel’noi Matematiki i Matematicheskoi Fiziki, 2008, Vol. 48, No. 7, pp. 1318–1336.     

非线性极大极小系统全局优化算法的分析

非线性极大极小系统的全局优化可用于柔性制造和智能交通的决策与控制.实现了非线性极大极小系统的全局优化算法的仿真,并进行了计算时间分析.数值实验表明了全局优化算法的可行性.算法的计算时间主要由系统的优化极大射影矩阵数目决定,而优化极大射影矩阵数目与系统解析式中单极大式的系数紧密相关,系数取值越分散,简约极大射影矩阵的效果越好,计算效率越高.     

多阶段M-SV投资组合优化的离散近似迭代法研究

文章提出了离散近似迭代法,用该方法求解具有交易成本和交易量限制的多阶段均值一半方差(M-sV)投资组合模型.离散近似迭代方法的基本思路为:首先,将连续型状态变量离散化,根据网络图的构造方法将上述模型转化多阶段赋权有向图;其次,运用嘉量原理求出起点至终点的最长路程,即获得模型的一个可行解;最后,以该可行解为基础,继续迭代直到前后两个可行解非常接近.文章还证明了该方法的收敛性和复杂性.     

Optimal shape design for the time‐dependent Navier–Stokes flow

This paper is concerned with the problem of shape optimization of two‐dimensional flows governed by the time‐dependent Navier–Stokes equations. We derive the structures of shape gradients for time‐dependent cost functionals by using the state derivative and its associated adjoint state. Finally, we apply a gradient‐type algorithm to our problem, and numerical examples show that our theory is useful for practical purposes and the proposed algorithm is feasible in low Reynolds number flows. Copyright © 2007 John Wiley & Sons, Ltd.     

Dual multilevel optimization

We study the structure of dual optimization problems associated with linear constraints, bounds on the variables, and separable cost. We show how the separability of the dual cost function is related to the sparsity structure of the linear equations. As a result, techniques for ordering sparse matrices based on nested dissection or graph partitioning can be used to decompose a dual optimization problem into independent subproblems that could be solved in parallel. The performance of a multilevel implementation of the Dual Active Set algorithm is compared with CPLEX Simplex and Barrier codes using Netlib linear programming test problems.      

Rainfall forecasting by technological machine learning models

Accurate forecasting of rainfall has been one of the most important issues in hydrological research. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. Recurrent artificial neural networks (RNNS) have played a crucial role in forecasting rainfall data. Meanwhile, support vector machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. This investigation elucidates the feasibility of hybrid model of RNNs and SVMs, namely RSVR, to forecast rainfall depth values. Moreover, chaotic particle swarm optimization algorithm (CPSO) is employed to choose the parameters of a SVR model. Subsequently, example of rainfall values during typhoon periods from Northern Taiwan is used to illustrate the proposed RSVRCPSO model. The empirical results reveal that the proposed model yields well forecasting performance, RSVRCPSO model provides a promising alternative for forecasting rainfall values.     

Automatic optic waveguide chip packaging system based on center-integration algorithm

In order to realize efficient automatic packaging of the waveguide chip and reduce its cost, an integrated optic waveguide chip packaging system based on center-integration algorithms is designed and fabricated, which realizes 11-dimension position adjustment and fewer operation times. The integration center position is obtained through the sampling data. The system can avoid local optimum and reduce packaging time as well as packaging loss. The automated alignment of a single-core fiber with channel waveguide and single-core fiber system is completed in less than 2 min. The packaging experiment of a single-core fiber array with 1 × 8 splitter coupler and 8-core fiber array is completed in less than 5 min with an insert-loss of less than 10.5 dB and homogenization of less than 0.4 dB. The system is validated with good efficiency and practicability.     

A filter-trust-region method for <Emphasis Type="Italic">LC</Emphasis><Superscript>1</Superscript> unconstrained optimization and its global convergence

In this paper we present a filter-trust-region algorithm for solving LC1 unconstrained optimization problems which uses the second Dini upper directional derivative.We establish the global convergence of the algorithm under reasonable assumptions.