Decision making with interval probabilities

Handling uncertainty by interval probabilities is recently receiving considerable attention by researchers. Interval probabilities are used when it is difficult to characterize the uncertainty by point-valued probabilities due to partially known information. Most of researches related to interval probabilities, such as combination, marginalization, condition, Bayesian inferences and decision, assume that interval probabilities are known. How to elicit interval probabilities from subjective judgment is a basic and important problem for the applications of interval probability theory and till now a computational challenge. In this work, the models for estimating and combining interval probabilities are proposed as linear and quadratic programming problems, which can be easily solved. The concepts including interval probabilities, interval entropy, interval expectation, interval variance, interval moment, and the decision criteria with interval probabilities are addressed. A numerical example of newsvendor problem is employed to illustrate our approach. The analysis results show that the proposed methods provide a novel and effective alternative for decision making when point-valued subjective probabilities are inapplicable due to partially known information.     

A new nonlinear interval programming method for uncertain problems with dependent interval variables

This paper proposes a new nonlinear interval programming method that can be used to handle uncertain optimization problems when there are dependencies among the interval variables. The uncertain domain is modeled using a multidimensional parallelepiped interval model. The model depicts single-variable uncertainty using a marginal interval and depicts the degree of dependencies among the interval variables using correlation angles and correlation coefficients. Based on the order relation of interval and the possibility degree of interval, the uncertain optimization problem is converted to a deterministic two-layer nesting optimization problem. The affine coordinate is then introduced to convert the uncertain domain of a multidimensional parallelepiped interval model to a standard interval uncertain domain. A highly efficient iterative algorithm is formulated to generate an efficient solution for the multi-layer nesting optimization problem after the conversion. Three computational examples are given to verify the effectiveness of the proposed method.     

A goal programming method for obtaining interval weights from an interval comparison matrix

Crisp comparison matrices lead to crisp weight vectors being generated. Accordingly, an interval comparison matrix should give an interval weight estimate. In this paper, a goal programming (GP) method is proposed to obtain interval weights from an interval comparison matrix, which can be either consistent or inconsistent. The interval weights are assumed to be normalized and can be derived from a GP model at a time. The proposed GP method is also applicable to crisp comparison matrices. Comparisons with an interval regression analysis method are also made. Three numerical examples including a multiple criteria decision-making (MCDM) problem with a hierarchical structure are examined to show the potential applications of the proposed GP method.     

Stability for several type of interval matrices

The robust stability for some types of tlme-varying interval raatrices and nonlineartime-varying interval matrices is considered and some sufficient conditions for robust stability of such interval matrices are given, The main results of this paper are only related to the verticesset of a interval matrices, and therefore, can be easily applied to test robust stability of interval matrices. Finally, some examples are given to illustrate the results.     

具有区间支付的宗派对策

在区间不确定环境下,针对具有否决权的成员与其他成员之间的合作,建立了具有区间支付的宗派对策。在区间核心中,非宗派成员得到的区间分配不能超过他对大联盟的边际贡献。给出了完全区间宗派对策的等价条件。当相应的区间减法可行时,完全区间宗派对策的区间核心中的分配可以通过两种单调区间分配方案扩张得到。算例验证了模型的有效性。     

基于误差理论的区间主成分分析及其应用

针对区间数样本,传统的主成分分析需进行拓展。首先讨论了区间样本数据的两种主要来源,即观测误差和符号数据分析。然后将区间数看作一个由中点和半径构成的具有一定误差的数,从误差理论出发,研究基于误差传递公式的区间主成分分析方法,并获得以区间数为表达形式的主成分。最后,结合我国2005年第四季度股票市场的数据进行了实证分析。结果表明,面对海量数据,区间PCA较传统PCA更容易从总体上把握样本的属性。     

区间合作对策核心存在性的进一步讨论

区间合作对策,是研究当联盟收益值为区间数情形时如何进行合理收益分配的数学模型。近年来,其解的存在性与合理性等问题引起了国内外专家的广泛关注。区间核心,是区间合作对策中一个非常稳定的集值解概念。本文首先针对区间核心的存在性进行深入的讨论,通过引入强非均衡,极小强均衡,模单调等概念,从不同角度给出判别区间核心存在性的充分条件。其次,通过引入相关参数,定义了广义区间核心,并给出定理讨论了区间核心与广义区间核心的存在关系。本文的结论将为进一步推动区间合作对策的发展,为解决区间不确定情形下的收益分配问题奠定理论基础。     

Dynamic load identification for interval structures under a presupposition of ‘being included prior to being measured’

Influences of structural uncertainties in the dynamic load identification are always significant and need to be quantified. In case of insufficient information available, intervals are favorable for modelling uncertainties. To perform the interval propagation in an inverse problem, this paper develops a sequential dual-stage interval identification method under a presupposition that each noisy response, which is an accomplished measurement for reconstructing unknown loads, should be included in the corresponding interval response of the structure exerted by interval loads to be identified. The proposed method transforms the interval identification problem into a classical one at the midpoint of interval parameters and an optimization model for minimizing the radius of each interval load. The effectiveness of the proposed method is validated by a spatial truss subjected to multiple forces due to the inclusion of each unknown load in the corresponding load. Besides, regularized solutions without exact knowledge of the accuracy loss are recommended to be used as few as possible in the interval identification of unknown loads.     

Zeros of univariate interval polynomials

Polynomials with perturbed coefficients, which can be regarded as interval polynomials, are very common in the area of scientific computing due to floating point operations in a computer environment. In this paper, the zeros of interval polynomials are investigated. We show that, for a degree n interval polynomial, the number of interval zeros is at most n and the number of complex block zeros is exactly n if multiplicities are counted. The boundaries of complex block zeros on a complex plane are analyzed. Numeric algorithms to bound interval zeros and complex block zeros are presented.     

Interval fuzzy preferences in the graph model for conflict resolution

A new analysis technique, appropriate to situations of high preference uncertainty, is added to the graph model for conflict resolution methodology. Interval fuzzy stabilities are now formulated, based on decision makers’ (DMs’) interval fuzzy preferences over feasible scenarios or states in a conflict. Interval fuzzy stability notions enhance the applicability of the graph model, and generalize its crisp and fuzzy preference-based stability ideas. A graph model is both a formal representation and an analysis procedure for multiple participant-multiple objective decisions that employs stability concepts representing various forms of human behavior under conflict. Defined based on a type-2 fuzzy logic, an interval fuzzy preference for one state over another is represented by a subinterval of [0, 1] indicating an interval-valued preference degree for the first state over the second. The interval fuzzy stabilities put forward in this research are interval fuzzy Nash stability, interval fuzzy general metarational stability, interval fuzzy symmetric metarational stability, and interval fuzzy sequential stability. A state is interval fuzzy stable for a DM if moving to any other state is not adequately desirable to the DM; where adequacy is measured by the interval fuzzy satisficing threshold of the DM and farsightedness, involving possible moves and countermoves by DMs, is determined by the interval fuzzy stability notion selected. Note that infinitely many degrees in an interval-valued preference are preserved in characterizing the desirability of a move. A state from which no DM can move to any sufficiently desirable scenario is an interval fuzzy equilibrium, and is interpreted as a possible resolution of the strategic conflict under study. The new stability concept is illustrated through its application to an environmental conflict that took place in Elmira, Ontario, Canada. Insightful results are identified and discussed.     

Intrinsic Compiler Support for Interval Arithmetic

Interval arithmetic provides an efficient method for monitoring errors in numerical computations and for solving problems that cannot be efficiently solved with floating-point arithmetic. To support interval arithmetic, several software tools have been developed including interval arithmetic libraries, extended scientific programming languages, and interval-enhanced compilers. The main disadvantage of these software tools is their speed, since interval operations are implemented using function calls. In this paper, compiler support for interval arithmetic is investigated. In particular, the performance benefits of having the compiler inline interval operations to eliminate function call overhead is researched. Interval operations are inlined with the GNU gcc compiler and the performance of interval arithmetic is evaluated on a superscalar architecture. To implement interval operations with compiler support, the compiler produces sequences of instructions that use existing floating point hardware. Simulation results show that the compiler implementation of interval arithmetic is approximately 4 to 5 times faster than a functionally equivalent interval arithmetic software implementation with function call overhead and approximately 1.2 to 1.5 times slower than a dedicated interval arithmetic hardware implementation.     

Efficient solution of interval optimization problem

In this paper the interval valued function is defined in the parametric form and its properties are studied. A methodology is developed to study the existence of the solution of a general interval optimization problem, which is expressed in terms of the interval valued functions. The methodology is applied to the interval valued convex quadratic programming problem.     

基于标准区间灰数的发展带离散DDGM预测模型

由于区间灰数运算体系尚不完善,灰数间的代数运算将导致结果灰度增加,难以有效构建基于"区间灰数"的灰色发展带预测模型.对此,通过将区间灰数进行标准化处理,分解成基于实数形式的"白部"和"灰部"两个部分;然后分别对"白部"和"灰部"建立发展带预测模型,再推导并还原得到区间灰数的发展带预测模型;最后,将模型用于摆动幅度大且整体趋势增长的区间灰数在未来时刻的预测,预测效果验证了所提出模型的有效性.     

区间规划问题的最优性条件

区间规划是带有区间参数的规划问题,是一种更易于求解实际问题的柔性规划。它是确定性优化问题的延伸,有区间线性规划和区间非线性规划两种形式。本文讨论了目标函数是区间函数的区间非线性问题。给出了区间规划问题最优性必要条件的较简单证明方法,并利用LU最优解的概念,在一类广义凸函数－(p,r)－ρ－(η,θ)－不变凸函数定义下讨论了最优性充分条件。     

A characterization of uniquely representable interval graphs

An alternative to Hanlon's buried-subgraph characterization of interval graphs that have unique (up to duality) agreeing interval orders is proveded. The alternative is based on a relation L between ordered pairs of points that are adjacent in the interval graph. The interpretation of abLxy is that if the interval for a precedes (follows) the interval for b in a representation of the interval graph, then the interval for x must precede (follow) the interval for y.     

Fast algorithms for floating-point interval matrix multiplication

We discuss several methods for real interval matrix multiplication. First, earlier studies of fast algorithms for interval matrix multiplication are introduced: naive interval arithmetic, interval arithmetic by midpoint-radius form by Oishi-Rump and its fast variant by Ogita-Oishi. Next, three new and fast algorithms are developed. The proposed algorithms require one, two or three matrix products, respectively. The point is that our algorithms quickly predict which terms become dominant radii in interval computations. We propose a hybrid method to predict which algorithm is suitable for optimizing performance and width of the result. Numerical examples are presented to show the efficiency of the proposed algorithms.     

R0-代数（NM-代数）的区间值模糊子代数

将区间值模糊集的概念应用于R0-代数,引入区间值模糊R0-子代数的概念并研究它的性质。给出了区间值模糊集成为区间值模糊R0-子代数的一个充要条件;讨论了区间值模糊R0-子代数和R0-子代数之间的关系;定义了区间值模糊集的象和原象,获得了区间值模糊R0-子代数的象和原象成为区间值模糊R0-子代数的条件。     

Interval oriented multi-section techniques for global optimization

This paper deals with two different optimization techniques to solve the bound-constrained nonlinear optimization problems based on division criteria of a prescribed search region, finite interval arithmetic and interval ranking in the context of a decision maker’s point of view. In the proposed techniques, two different division criteria are introduced where the accepted region is divided into several distinct subregions and in each subregion, the objective function is computed in the form of an interval using interval arithmetic and the subregion containing the best objective value is found by interval ranking. The process is continued until the interval width for each variable in the accepted subregion is negligible. In this way, the global optimal or close to global optimal values of decision variables and the objective function can easily be obtained in the form of an interval with negligible widths. Both the techniques are applied on several benchmark functions and are compared with the existing analytical and heuristic methods.     

Time response of structure with interval and random parameters using a new hybrid uncertain analysis method

Practical structures often operate with some degree of uncertainties, and the uncertainties are often modelled as random parameters or interval parameters. For realistic predictions of the structures behaviour and performance, structure models should account for these uncertainties. This paper deals with time responses of engineering structures in the presence of random and/or interval uncertainties. Three uncertain structure models are introduced. The first one is random uncertain structure model with only random variables. The generalized polynomial chaos (PC) theory is applied to solve the random uncertain structure model. The second one is interval uncertain structure model with only interval variables. The Legendre metamodel (LM) method is presented to solve the interval uncertain structure model. The LM is based on Legendre polynomial expansion. The third one is hybrid uncertain structure model with both random and interval variables. The polynomial-chaos-Legendre-metamodel (PCLM) method is presented to solve the hybrid uncertain structure model. The PCLM is a combination of PC and LM. Three engineering examples are employed to demonstrate the effectiveness of the proposed methods. The uncertainties resulting from geometrical size, material properties or external loads are studied.     

区间合成模糊对策

给出了一种新的合成模糊对策模型——区间合成模糊对策,研究了区间合成模糊对策的区间稳定集、区间核心、区间Shapley值、区间Banzhaf-Coleman势指标以及与子区间模糊对策的关系。区间合成模糊对策作为一种特殊的模糊数合成模糊对策,对于研究其它具有模糊数的模糊合成对策有一定的参考价值。