Riesz Bases Generated by Contractions and Translations of a Function on an Interval

Conditions for a system of contractions and translations of a function to be a Riesz basis are given.     

Symbolic Interval Inference Approach for Subdivision Direction Selection in Interval Partitioning Algorithms

In bound constrained global optimization problems, partitioning methods utilizing Interval Arithmetic are powerful techniques that produce reliable results. Subdivision direction selection is a major component of partitioning algorithms and it plays an important role in convergence speed. Here, we propose a new subdivision direction selection scheme that uses symbolic computing in interpreting interval arithmetic operations. We call this approach symbolic interval inference approach (SIIA). SIIA targets the reduction of interval bounds of pending boxes directly by identifying the major impact variables and re-partitioning them in the next iteration. This approach speeds up the interval partitioning algorithm (IPA) because it targets the pending status of sibling boxes produced. The proposed SIIA enables multi-section of two major impact variables at a time. The efficiency of SIIA is illustrated on well-known bound constrained test functions and compared with established subdivision direction selection methods from the literature.     

Interval Branch and Bound with Local Sampling for Constrained Global Optimization

In this article, we introduce a global optimization algorithm that integrates the basic idea of interval branch and bound, and new local sampling strategies along with an efficient data structure. Also included in the algorithm are procedures that handle constraints. The algorithm is shown to be able to find all the global optimal solutions under mild conditions. It can be used to solve various optimization problems. The local sampling (even if done stochastically) is used only to speed up the convergence and does not affect the fact that a complete search is done. Results on several examples of various dimensions ranging from 1 to 100 are also presented to illustrate numerical performance of the algorithm along with comparison with another interval method without the new local sampling and several noninterval methods. The new algorithm is seen as the best performer among those tested for solving multi-dimensional problems.     

The Maximum Box Problem and its Application to Data Analysis

Given two finite sets of points X ⁺ and X ^– in ⁿ , the maximum box problem consists of finding an interval (box) B = {x : l x u} such that B X ^– = , and the cardinality of B X ⁺ is maximized. A simple generalization can be obtained by instead maximizing a weighted sum of the elements of B X ⁺. While polynomial for any fixed n, the maximum box problem is -hard in general. We construct an efficient branch-and-bound algorithm for this problem and apply it to a standard problem in data analysis. We test this method on nine data sets, seven of which are drawn from the UCI standard machine learning repository.     

泛灰数在区间分析中的应用

在概述泛灰数的概念及其运算规则的基础上 ,介绍了泛灰数与区间数的转化 ,利用泛灰数的可扩展性对区间进行分析 ,它不仅具有区间分析的功能 ,而且能解决区间分析所不能解决的问题 .并给出了算例作了对比分析     

Tail Bounds for the Suprema of Empirical Processes over Unbounded Classes of Functions

So far the study of exponential bounds of an empirical process has been restricted to a bounded index class of functions. The case of an unbounded index class of functions is now studied on the basis of a new symmetrization idea and a new method of truncating the original probability space; the exponential bounds of the tail probabilities for the supremum of the empirical process over an unbounded class of functions are obtained. The exponential bounds can be used to establish laws of the logarithm for the empirical processes over unbounded classes of functions. This work is supported partially by the National Natural Science Foundation of China (Grant No. 10471061) and the Social Science Foundation of Ministry of Education of China (Grant No. 01JD910001)     

Tail Bounds for the Supremums of Empirical Processes over Unbounded Classes of Functions

So far the study of exponential bounds of an empirical process has been restricted to a bounded index class of functions. The case of an unbounded index class of functions is now studied on the basis of a new symmetrization idea and a new method of truncating the original probability space; the exponential bounds of the tail probabilities for the supremum of the empirical process over an unbounded class of functions are obtained. The exponential bounds can be used to establish laws of the logarithm for the empirical processes over unbounded classes of functions.     

On the Inclusion Properties of Interval Multiplication: A Diagrammatic Study

Certain algebraic properties of familiar (set-theoretic) interval multiplication are studied diagrammatically. The centred interval multiplication operations (the long known outward multiplication and the newly proposed inward one) are defined and studied diagrammatically in some detail, especially with respect to inclusion isotonicity.This revised version was published online in October 2005 with corrections to the Cover Date.     

基于区间分析的投资组合VaR计算新方法

基于区间分析估计变量的累计概率分布是进行风险价值分析的一种新方法。本文将区间分析运用到股票投资组合的VaR计算中,研究区间分析在VaR计算方法中的应用。首先给出了基于区间分析估计分布函数的计算步骤,然后将区间分析运用到VaR的计算中,以两只股票的投资组合为例得出收益率的累计概率分布,从中得到某一置信度下的VaR值,最后与蒙特卡洛模拟方法做了比较研究,结果表明,基于区间分析的VaR计算方法的运算精度和计算速度明显优于蒙特卡洛模拟方法。     

排序的贪婪算法的参数上界

本文研究平行机排序中最著名的贪婪算法─LPT算法的性质．经典排序中机器随时可以开始加工．本文研究机器不都是从开始就可以加工,而是需要一个准备时间,也就是说本文研究各台机器最早可以开工的时间可以不同的同型号平行机（ideaticalParallel）的排序问题,分析LPT算法得到的近似解的参数上界．     

Error Estimation for Nonlinear Complementarity Problems via Linear Systems with Interval Data

For the nonlinear complementarity problem, we derive norm bounds for the error of an approximate solution, generalizing the known results for the linear case. Furthermore, we present a linear system with interval data, whose solution set contains the error of an approximate solution. We perform extensive numerical tests and compare the different approaches.     

Solving integer programs with a few important binary gub constraints

During a branch and bound search of an integer program, decisions have to be taken about which subproblem to solve next and which variable or special ordered set to branch on. Both these decisions are usually based on some sort of estimated change in the objective caused by different branching. When the next subproblem is chosen, the estimated change in the objective is often found by summing the change caused by changing all integer variables with non-integer values, as if they were independent. For special ordered sets the estimation is done for each set as a whole. The purpose of this paper is to report some results from trying to do a simultaneous estimation for all the variables in a binary gub constraint. By this, the analysed problems contain one or a few constraints saying that the sum ofn binary variables should be equal tom (<n).I am grateful to Scicon Ltd. for giving me access to the SCICONIC source code.     

Interval Methods for Semi-Infinite Programs

A new approach for the numerical solution of smooth, nonlinear semi-infinite programs whose feasible set contains a nonempty interior is presented. Interval analysis methods are used to construct finite nonlinear, or mixed-integer nonlinear, reformulations of the original semi-infinite program under relatively mild assumptions on the problem structure. In certain cases the finite reformulation is exact and can be solved directly for the global minimum of the semi-infinite program (SIP). In the general case, this reformulation is over-constrained relative to the SIP, such that solving it yields a guaranteed feasible upper bound to the SIP solution. This upper bound can then be refined using a subdivision procedure which is shown to converge to the true SIP solution with finite -optimality. In particular, the method is shown to converge for SIPs which do not satisfy regularity assumptions required by reduction-based methods, and for which certain points in the feasible set are subject to an infinite number of active constraints. Numerical results are presented for a number of problems in the SIP literature. The solutions obtained are compared to those identified by reduction-based methods, the relative performances of the nonlinear and mixed-integer nonlinear formulations are studied, and the use of different inclusion functions in the finite reformulation is investigated.     

Parallel methods for verified global optimization practice and theory

We present a new parallel method for verified global optimization, using a centralized mediator for the dynamic load balancing. The new approach combines the advantages of two previous models, the master slave model and the processor farm. Numerical results show the efficiency of this new method. For a large number of problems at least linear speedup is reached. The efficiency of this new method is also confirmed by a comparison with other parallel methods for verified global optimization. A theoretical study proves that using the best-first strategy to choose the next box for subdivision, no real superlinear speedup may be expected concerning the number of iterations. Moreover, the potential of parallelization of methods of verified global optimization is discussed in general.     

关于实二次域类数的上下界

设Ｄ是无平方因子正整数，Δ，ｈ，ε分别是实二次的判别式、类数和基本单位数，本文运用初等方法证明了：（ｉ）在假定广义Ｒｉｅｍａｎｎ猜想成立的条件下，当Δ＞１０￣９时，（ｉｉ）当Ｄ＝ｐ，ｐ是奇素数时，     

Improving probability bounds by optimization over subsets

The simple device of maximization over subsets of events can provide substantial improvement over the Dawson-Sankoff degree two lower bound on the probability of a union of events and can also exceed a sharper bound that uses individual and pairwise joint event probabilities developed by Kuai, Alajaji, and Takahara. In each of their examples, the maximized bound achieves the exact probability of the union using a subset of events containing no redundant events of the original set of events.     

Optimal Multisections in Interval Branch-and-Bound Methods of Global Optimization

In this paper we define multisections of intervals that yield sharp lower bounds in branch-and-bound type methods for interval global optimization. A so called 'generalized kite', defined for differentiable univariate functions, is built simultaneously with linear boundary forms and suitably chosen centered forms. Proofs for existence and uniqueness of optimal cuts are given. The method described may be used either as an accelerating device or in a global optimization algorithm with an efficient pruning effect. A more general principle for decomposition of boxes is suggested.     

区间数排序的包含度度量及构造方法

本引用包含度和偏序包含度概念，指出[1]、[2]、[3]的可能度是一种区间数的包含度。在此基础上，利用三角模构造了一类偏序包含度，建立了区间数比较的包含度构造方法，并用各种包含度对[1]的算例进行排序，取得满意效果。     

聚类分析在分枝定界法中的应用

针对界约束二次规划的分枝定界法中出现的紧、松弛策略,结合聚类分析方法,给出了新的剖分边的选取原则,把球约束二次规划作为子问题,使得原问题整体最优值的上、下界能较快的达到.     

建设项目区间数属性评价的集对分析方法

针对建设项目承包商优选涉及到定量和定性两类指标属性评价问题,将集对分析理论应用于建设项目区间数指标属性评价决策.通过属性区间数值的同一、差异、对立的集对分析概念延伸,完成建设项目承包商优选评价.方法计算简单,通过案例分析表明,模型是可行和合理的,取得较为满意的结果.