Universal Alignment Probability Revisited

In this note, we quantify and validate the representativeness of the uniformly sampled set N for the search space and the use of universal alignment probability (UAP) curves.     

Ordinal Optimization with Subset Selection Rule

Ordinal optimization (OO) has enjoyed a great degree of success in addressing stochastic optimization problems characterized by an independent and identically distributed (i.i.d.) noise. The methodology offers a statistically quantifiable avenue to find good enough solutions by means of soft computation. In this paper, we extend the OO methodology to a more general class of stochastic problems by relaxing the i.i.d. assumption on the underlying noise. Theoretical results and their applications to simple examples are presented.     

Subset simulation for unconstrained global optimization

Global optimization problem is known to be challenging, for which it is difficult to have an algorithm that performs uniformly efficient for all problems. Stochastic optimization algorithms are suitable for these problems, which are inspired by natural phenomena, such as metal annealing, social behavior of animals, etc. In this paper, subset simulation, which is originally a reliability analysis method, is modified to solve unconstrained global optimization problems by introducing artificial probabilistic assumptions on design variables. The basic idea is to deal with the global optimization problems in the context of reliability analysis. By randomizing the design variables, the objective function maps the multi-dimensional design variable space into a one-dimensional random variable. Although the objective function itself may have many local optima, its cumulative distribution function has only one maximum at its tail, as it is a monotonic, non-decreasing, right-continuous function. It turns out that the searching process of optimal solution(s) of a global optimization problem is equivalent to exploring the process of the tail distribution in a reliability problem. The proposed algorithm is illustrated by two groups of benchmark test problems. The first group is carried out for parametric study and the second group focuses on the statistical performance.     

Two-Stage Nested Partitions Method for Stochastic Optimization

We address the problem of optimizing over a large but finite set when the objective function does not have an analytical expression and is evaluated using noisy estimation. Building on the recently proposed nested partitions method for stochastic optimization, we develop a new approach that combines this random search method and statistical selection for guiding the search. We prove asymptotic convergence and analyze the finite time behavior of the new approach. We also report extensive numerical results to illustrate the benefits of the new approach.     

Stochastic Nonstationary Optimization for Finding Universal Portfolios

We apply ideas from stochastic optimization for defining universal portfolios. Universal portfolios are that class of portfolios which are constructed directly from the available observations of the stocks behavior without any assumptions about their statistical properties. Cover [7] has shown that one can construct such portfolio using only observations of the past stock prices which generates the same asymptotic wealth growth as the best constant rebalanced portfolio which is constructed with the full knowledge of the future stock market behavior.In this paper we construct universal portfolios using a different set of ideas drawn from nonstationary stochastic optimization. Our portfolios yield the same asymptotic growth of wealth as the best constant rebalanced portfolio constructed with the perfect knowledge of the future and they are less demanding computationally compared to previously known universal portfolios. We also present computational evidence using New York Stock Exchange data which shows, among other things, superior performance of portfolios which explicitly take into account possible nonstationary market behavior.     

Stochastic Optimization of Insurance Portfolios for Managing Exposure to Catastrophic Risks

A catastrophe may affect different locations and produce losses that are rare and highly correlated in space and time. It may ruin many insurers if their risk exposures are not properly diversified among locations. The multidimentional distribution of claims from different locations depends on decision variables such as the insurer's coverage at different locations, on spatial and temporal characteristics of possible catastrophes and the vulnerability of insured values. As this distribution is analytically intractable, the most promising approach for managing the exposure of insurance portfolios to catastrophic risks requires geographically explicit simulations of catastrophes. The straightforward use of so-called catastrophe modeling runs quickly into an extremely large number of what-if evaluations. The aim of this paper is to develop an approach that integrates catastrophe modeling with stochastic optimization techniques to support decision making on coverages of losses, profits, stability, and survival of insurers. We establish connections between ruin probability and the maximization of concave risk functions and we outline numerical experiments.     

Stochastic Optimization for mechanical structures

The Stochastic Optimization Problem is well known in mathematics and operations research. Due to increasing demands for performance improvement together with reduced weight and costs the introduction of uncertainties into optimization problems in mechanical engineering is also necessary. In a number of papers this Stochastic Structural Optimization Problem is formulated and solved only within certain limiting assumptions. This paper presents a general definition of the Stochastic Structural Optimization Problem. Furthermore one solution technique based on reliability calculation is presented. With this technique the use of an augmented optimization loop based on Mathematical Programming is possible. The use of the developed tool for reliability calculations and optimization is demonstrated for complex mechanical structures.     

New Subinterval Selection Criteria for Interval Global Optimization

The theoretical convergence properties of interval global optimization algorithms that select the next subinterval to be subdivided according to a new class of interval selection criteria are investigated. The latter are based on variants of the RejectIndex: , a recently thoroughly studied indicator, that can quite reliably show which subinterval is close to a global minimizer point. Extensive numerical tests on 40 problems confirm that substantial improvements can be achieved both on simple and sophisticated algorithms by the new method (utilizing the known minimum value), and that these improvements are larger when hard problems are to be solved.     

A Stochastic Minimum Cross-Entropy Method for Combinatorial Optimization and Rare-event Estimation*

We present a new method, called the minimum cross-entropy (MCE) method for approximating the optimal solution of NP-hard combinatorial optimization problems and rare-event probability estimation, which can be viewed as an alternative to the standard cross entropy (CE) method. The MCE method presents a generic adaptive stochastic version of Kull-backs classic MinxEnt method. We discuss its similarities and differences with the standard cross-entropy (CE) method and prove its convergence. We show numerically that MCE is a little more accurate than CE, but at the same time a little slower than CE. We also present a new method for trajectory generation for TSP and some related problems. We finally give some numerical results using MCE for rare-events probability estimation for simple static models, the maximal cut problem and the TSP, and point out some new areas of possible applications.AMS 2000 Subject Classification: 65C05, 60C05, 68W20, 90C59*This reseach was supported by the Israel Science Foundation (grant no 191-565).     

基于次梯度选取的非光滑优化强次可行方向法

本文结合次梯度选取技术及割平面法和强次可行方向法的思想,提出了一个求解目标函数非光滑约束优化问题的强次可行方向算法.通过设计一个新的寻找搜索方向子问题和构造新型线搜索,算法不仅能接受不可行的初始点,而且能保持迭代点的强次可行性,同时避免在可行域外目标函数值的不适度增加.算法具备全局收敛性,且初步的数值试验表明算法是稳定有效的.     

命中次数随机时毁伤时间分布与格斗获胜概率的研究

文章研究了一对一随机格斗中一类最具有一般性的模型——格斗双方带有搜索系统并且毁伤对方所需命中次数随机的格斗模型 .文章从研究条件随机过程入手 ,导出了格斗方毁伤对方所需时间的分布与相应的特征函数表达形式 ,也求出了计算获胜概率的公式     

基于信用额度和外生违约概率的出口海陆仓融资质押率优化

质押率优化是出口海陆仓融资决策的核心内容。针对出口商信用、出口商与进口商外生违约以及质押物价值波动三重叠加风险下的出口海陆仓融资决策问题,在设定出口商信用额度,且给定出口商和进口商外生违约概率的前提下,依据双重Stackelberg博弈原理,以供应链协同均衡下的出口商、进口商和船公司的期望利润最大化为目标,建立了质押率与订货量及货物价格联动优化模型,设计了微分法和逆向归纳法求解模型。算例验证了模型和方法的适用性和有效性。敏感性分析结果表明质押率与出口商信用额度呈负相关关系,对出口商和进口商外生违约概率不敏感。研究结论可为出口海陆仓融资优化决策提供科学参考。     

多式联运分运人选择的模型和算法

在对多式联运分运人选择问题分析的基础上,运用图论技术构建了基于联运分运人选择的多式联运网络。综合考虑了运输的规模经济性、时效性、风险性和联运联盟的稳定性,建立了基于综合运输成本最小、运输风险最小和合作强度最大的多目标优化选择模型。通过主要目标法,将模型转化为单目标模型。借鉴生物免疫原理,设计了基于克隆增扩的人工免疫算法对问题进行求解。最后通过算例对方法进行了验证。     

Optimization via simulation for logistic systems planning and control

This text summarizes the author’s PhD thesis, presented in February 2007 at the University of Calabria and supervised by Roberto Musmanno and Pasquale Legato. The work deals with the field of optimization via simulation. A special emphasis is put on problems with discrete decision variables because they are especially relevant in engineering applications (e.g., in the design of logistic systems). In particular, the thesis focuses on a critical decision problem at marine container terminals. For tackling the problem, an optimization via simulation procedure is presented. Computational results are obtained by using high performance computing systems. The thesis is written in Italian and is available from the author upon request.      

The Cross-Entropy Method for Combinatorial and Continuous Optimization

We present a new and fast method, called the cross-entropy method, for finding the optimal solution of combinatorial and continuous nonconvex optimization problems with convex bounded domains. To find the optimal solution we solve a sequence of simple auxiliary smooth optimization problems based on Kullback-Leibler cross-entropy, importance sampling, Markov chain and Boltzmann distribution. We use importance sampling as an important ingredient for adaptive adjustment of the temperature in the Boltzmann distribution and use Kullback-Leibler cross-entropy to find the optimal solution. In fact, we use the mode of a unimodal importance sampling distribution, like the mode of beta distribution, as an estimate of the optimal solution for continuous optimization and Markov chains approach for combinatorial optimization. In the later case we show almost surely convergence of our algorithm to the optimal solution. Supporting numerical results for both continuous and combinatorial optimization problems are given as well. Our empirical studies suggest that the cross-entropy method has polynomial in the size of the problem running time complexity.     

证券投资组合一种多目标优化模型及其算法

针对Young(1998)提出的证券投资组合极小极大(Minimax)模型,给出了一种有效算法;并在此基础上建立了一个多目标优化模型以及求解该问题的一个中心算法.最后通过算例分析,对两种模型及其算法进行了比较.     

面向远海岛屿的救助基地选址优化模型及其算法

为解决在远海海域选择岛屿建设救助基地的方案优化问题,建立了基于GIS和智能算法的双目标优化模型,采用自适应拉伸的拥挤距离计算公式,设计了自适应精英保留策略对算法进行改进,通过剖析决策者选择最优方案的基本原则,得到了性价比最高的优化方案。最后,以我国南海南沙群岛选择岛屿建设救助基地的方案优化为例进行分析,得到了较好结果。为验证文中改进算法的有效性,选取多个不同规模的方案进行分析比较,结果显示本文提出的算法在优化结果及解的分布性等方面均更优。本文研究为我国海上岛屿救助基地选址和在资源有限的情况下如何科学配置救助船队提供了分析方法。     

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Let $X_1,X_2,\ldots,X_n$ be a sequence of extended negatively dependent random variables with distributions $F_1,F_2,\ldots,F_n$,respectively. Denote by $S_n=X_1+X_2+\cdots+X_n$. This paper establishes the asymptotic relationship for the quantities $\pr(S_n>x)$, $\pr(\max\{X_1,X_2, \ldots,X_n\}>x)$, $\pr(\max\{S_1,S_2$, $\ldots,S_n\}>x)$ and $\tsm_{k=1}^n\pr(X_k>x)$ in the three heavy-tailed cases. Based on this, this paper also investigates the asymptotics for the tail probability of the maximum of randomly weighted sums, and checks its accuracy via Monte Carlo simulations. Finally, as an application to the discrete-time risk model with insurance and financial risks, the asymptotic estimate for the finite-time ruin probability is derived.     

基于风险-均值分析的供应商选择与订单分配集成优化模型与求解

针对不确定环境下具有不同供应合约的供应商选择与订单分配问题,本文构建了基于风险-均值分析的模糊两阶段多周期集成优化模型。与传统的该问题研究并未充分考虑供应商选择与订单分配两阶段决策的交互影响不同,在该模型中,第一阶段供应商选择的评价目标依赖于后期实际运营中的订单分配决策;并考虑未来需求和实际运营成本的不确定性,引入在险价值和期望值两种决策准则对供应商选择方案的绩效进行评价。提出了该模型的分析求解方法,在险价值得以精确评估,期望值被控制在确定的误差范围内,并可以达到足够的精度要求。     

非凸极小极大问题的优化算法与复杂度分析

非凸极小极大问题是近期国际上优化与机器学习、信号处理等交叉领域的一个重要研究前沿和热点,包括对抗学习、强化学习、分布式非凸优化等前沿研究方向的一些关键科学问题都归结为该类问题。国际上凸-凹极小极大问题的研究已取得很好的成果,但非凸极小极大问题不同于凸-凹极小极大问题,是有其自身结构的非凸非光滑优化问题,理论研究和求解难度都更具挑战性,一般都是NP-难的。重点介绍非凸极小极大问题的优化算法和复杂度分析方面的最新进展。