On Extending the LP Computable Risk Measures to Account Downside Risk

A mathematical model of portfolio optimization is usually quantified with mean-risk models offering a lucid form of two criteria with possible trade-off analysis. In the classical Markowitz model the risk is measured by a variance, thus resulting in a quadratic programming model. Following Sharpe’s work on linear approximation to the mean-variance model, many attempts have been made to linearize the portfolio optimization problem. There were introduced several alternative risk measures which are computationally attractive as (for discrete random variables) they result in solving linear programming (LP) problems. Typical LP computable risk measures, like the mean absolute deviation (MAD) or the Gini’s mean absolute difference (GMD) are symmetric with respect to the below-mean and over-mean performances. The paper shows how the measures can be further combined to extend their modeling capabilities with respect to enhancement of the below-mean downside risk aversion. The relations of the below-mean downside stochastic dominance are formally introduced and the corresponding techniques to enhance risk measures are derived.The resulting mean-risk models generate efficient solutions with respect to second degree stochastic dominance, while at the same time preserving simplicity and LP computability of the original models. The models are tested on real-life historical data.The research was supported by the grant PBZ-KBN-016/P03/99 from The State Committee for Scientific Research.     

Image Space Analysis and Scalarization of Multivalued Optimization

Using a new method based on generalized sections of feasible sets, we obtain optimality conditions for vector optimization of objective multifunctions with multivalued constraints. The authors express their sincere gratitude to Professor F. Giannessi and the referees for comments and valuable suggestions. The second author was partially supported by the Center of Excellence for Mathematics (University of Isfahan).     

利用遗传算法由电阻抗计算四阶带通箱的低频响应

提出了一种基于电阻抗得到四阶带通扬声器系统低频频率响应的方法。该方法无需消声室,首先建立扬声器系统的电阻抗集中参数电路模型并测量得到电阻抗曲线,然后运用遗传算法优化模型中的元件参数值,使得由模型计算得到的阻抗曲线与测得的阻抗曲线相吻合,再根据模型计算得到低频响应曲线。测量结果表明理论曲线与实测曲线相吻合,说明基于电阻抗得到低频响应的方法是可行的和有效的。     

多源单汇网络资源调度路径优化探讨

在考虑队列长度的前提下 ,论文在分析多源单汇资源传输问题的基础上 ,以两源单汇网络为例 ,在资源到达终点所需时间最短约束下 ,研究、计算了最短传输时间的上下界 ,并提出了相应的算法 .论文的研究内容为进一步研究多源多汇问题建立了基础 ,提供了思路 .     

Duality in robust optimization: Primal worst equals dual best

We study the dual problems associated with the robust counterparts of uncertain convex programs. We show that while the primal robust problem corresponds to a decision maker operating under the worst possible data, the dual problem corresponds to a decision maker operating under the best possible data.     

On (0, 1)-matrices with prescribed row and column sum vectors

Given partitions R and S with the same weight, the Robinson-Schensted-Knuth correspondence establishes a bijection between the class A(R,S) of (0, 1)-matrices with row sum R and column sum S and pairs of Young tableaux of conjugate shapes λ and λ^∗, with S?λ?R^∗. An algorithm for constructing a matrix in A(R,S) whose insertion tableau has a prescribed shape λ, with S?λ?R^∗, is provided. We generalize some recent constructions due to R. Brualdi for the extremal cases λ=S and λ=R^∗.     

Orthogonal similarity transformation into block‐semiseparable matrices of semiseparability rank k

Very recently, an algorithm, which reduces any symmetric matrix into a semiseparable one of semi‐ separability rank 1 by similar orthogonality transformations, has been proposed by Vandebril, Van Barel and Mastronardi. Partial execution of this algorithm computes a semiseparable matrix whose eigenvalues are the Ritz‐values obtained by the Lanczos' process applied to the original matrix. Also a kind of nested subspace iteration is performed at each step. In this paper, we generalize the above results and propose an algorithm to reduce any symmetric matrix into a similar block‐semiseparable one of semiseparability rank k, with k ∈ ?, by orthogonal similarity transformations. Also in this case partial execution of the algorithm computes a block‐semiseparable matrix whose eigenvalues are the Ritz‐values obtained by the block‐Lanczos' process with k starting vectors, applied to the original matrix. Subspace iteration is performed at each step as well. Copyright © 2005 John Wiley & Sons, Ltd.     

投影寻踪模型在国民经济综合评价中的应用

文章针对区域国民经济发展的多属性,采用投影寻踪评价模型,用加速遗传算法寻找最佳投影方向,将多维属性指标转换为一维投影值.在此基础上,通过建立单属性指标的分类等级区间,给出区域国民经济发展水平分类的投影值区间,从而实现对区域国民经济发展水平的分类.应用表明,它是国民经济评价的一种计算过程简单、直观的新方法.     

On the complexity of the exchange algorithm for minimax optimization problems

We present an exchange algorithm for the solution of minimax optimization problems involving convex functions. For a certain class of functions, the complexity of this algorithm is shown to be either linear in the number of functions, or at least squared in that number.     

Existence of weakly efficient solutions in vector optimization

In this paper, we present an existence result for weak efficient solution for the vector optimization problem. The result is stated for invex strongly compactly Lipschitz functions.