Extended Regularized Dual Averaging Methods for Stochastic Optimization

We introduce a new algorithm, extended regularized dual averaging (XRDA), for solving regularized stochastic optimization problems, which generalizes the regularized dual averaging (RDA) method. The main novelty of the method is that it allows a flexible control of the backward step size. For instance, the backward step size used in RDA grows without bound, while for XRDA the backward step size can be kept bounded. We demonstrate experimentally that additional control over the backward step size can speed up the convergence of the algorithm while preserving desired properties of the iterates, such as sparsity. Theoretically, we show that the XRDA method achieves the same convergence rate as RDA for general convex objectives.     

用模糊综合评价值作为单纯形优化指标

本文介绍了一个模糊综合评价方法,讨论了模糊运算方法对运算结果即综合评价值的影响,并将模糊综合评价值用作单纯形优化指标。     

基于免疫算法的组合预测方法

利用免疫算法搜索全局最优解能力,提出了一种其于免疫算法的组合预测权系数确定的新方法,并给出了具体算法.仿真实验结果表明了免疫算法在组合预测方面具有很好的可行性和有效性.     

城市功能区声环境质量监测时间代表性研究

城市功能区声环境质量监测数据是评估我国城市声环境质量水平的重要依据,为科学准确地反映城市功能区声环境质量水平,应选取合理的、具有代表性的监测时间开展监测。本文以某地4个典型的功能区声环境质量监测点位1年的连续监测数据作为样本,分析了不同功能区点位长期噪声波动特征,并采用Kruskal-Wallis 检验（K-W检验）方法,分别分析了一年中各月之间及一周中各日之间噪声水平的差异性。数据处理及分析结果表明：（1）4个监测点位一年中各月之间噪声水平,特别是夜间等效连续A声级之间具有显著差异,一年监测一次显然难以代表全年声环境质量水平;相比较下,每季度监测一次能大幅提高监测结果代表性,季度内的3个月份各月之间无显著差异的比例占80%以上;（2）对于这4个监测点位,一周中在周二至周五之间监测其结果更具代表性。     

Necessary and sufficient optimality conditions for a class of nonsmooth minimization problems

The purpose of this paper is to derive, in a unified way, second order necessary and sufficient optimality criteria, for four types of nonsmooth minimization problems: thediscrete minimax problem, thediscrete l ₁-approximation, the minimization of theexact penalty function and the minimization of theclassical exterior penalty function. Our results correct and supplement conditions obtained by various authors in recent papers.     

THE ＇HYBRID＇ TECHNIQUE FOR RISK ANALYSIS OF SOME DISEASES

Based on the data obtained from a survey recently made in Shanghai, this paper presents the hybrid technique for risk analysis and evaluation of some diseases. After determination of main risk factors of these diseases by analysis of variance, the authors introduce a new concept ‘Illness Fuzzy Set‘ and use fuzzy comprehensive evaluation to evaluate the risk of suffering from a disease for residents. Optimal technique is used to determinethe weights wi in fuzzy comprehensive evaluation, and a new method ‘Improved Information Distribution‘ is also introduced for the treatment of small sample problem. It is shown that the results obtained by using the hybrid technique are better than by using single fuzzy technique or single statistical method.     

An artificial hummingbird algorithm based localization with reduced number of reference nodes for wireless sensor networks

Wireless Sensor Networks (WSN) are widely used in recent years due to the advancements in wireless and sensor technologies. Many of these applications require to know the location information of nodes. This information is useful to understand the collected data and to act on them. Existing localization algorithms make use of a few reference nodes for estimating the locations of sensor nodes. But, the positioning and utilization of reference nodes increase the cost and complexity of the network. To reduce the dependency on reference nodes, in this paper, we have developed a novel optimization based localization method using only two reference nodes for the localization of the entire network. This is achieved by reference nodes identifying a few more nodes as reference nodes by the analysis of the connectivity information. The sensor nodes then use the reference nodes to identify their locations in a distributive manner using Artificial Hummingbird Algorithm (AHA). We have observed that the localization performance of the reported algorithm at a lower reference node ratio is comparable with other algorithms at higher reference node ratios.     

A bilinear approach to the pooling problem†

In this paper we present an algorithm for the pooling problem in refinery optimization based on a bilinear programming approach. The pooling problem occurs frequently in process optimization problems, especially refinery planning models. The main difficulty is that pooling causes an inherent nonlinearity in the otherwise linear models. We shall define the problem by formulating an aggregate mathematical model of a refinery, comment on solution methods for pooling problems that have been presented in the literature, and develop a new method based on convex approximations of the bilinear terms. The method is illustrated on numerical examples     

A proximal gradient descent method for the extended second-order cone linear complementarity problem

We consider an extended second-order cone linear complementarity problem (SOCLCP), including the generalized SOCLCP, the horizontal SOCLCP, the vertical SOCLCP, and the mixed SOCLCP as special cases. In this paper, we present some simple second-order cone constrained and unconstrained reformulation problems, and under mild conditions prove the equivalence between the stationary points of these optimization problems and the solutions of the extended SOCLCP. Particularly, we develop a proximal gradient descent method for solving the second-order cone constrained problems. This method is very simple and at each iteration makes only one Euclidean projection onto second-order cones. We establish global convergence and, under a local Lipschitzian error bound assumption, linear rate of convergence. Numerical comparisons are made with the limited-memory BFGS method for the unconstrained reformulations, which verify the effectiveness of the proposed method.     

Newton's method for constrained optimization

We derive a quadratically convergent algorithm for minimizing a nonlinear function subject to nonlinear equality constraints. We show, following Kaufman [4], how to compute efficiently the derivative of a basis of the subspace tangent to the feasible surface. The derivation minimizes the use of Lagrange multipliers, producing multiplier estimates as a by-product of other calculations. An extension of Kantorovich's theorem shows that the algorithm maintains quadratic convergence even if the basis of the tangent space changes abruptly from iteration to iteration. The algorithm and its quadratic convergence are known but the drivation is new, simple, and suggests several new modifications of the algorithm.