The fuzzy intersection rule in variational analysis with applications

The fuzzy intersection rule for Fréchet normal cones in Asplund spaces was established by Mordukhovich and the author using the extremal principle, which appears more convenient to apply in some applications. In this paper, we present a complete discussion of this rule in various aspects. We show that the fuzzy intersection rule is another characterization of the Asplund property of the space. Various applications are considered as well. In particular, a complete set of fuzzy calculus rules for general lower semicontinuous functions are established.     

Dynamical Systems Gradient Method for Solving Nonlinear Equations with Monotone Operators

A version of the Dynamical Systems Gradient Method for solving ill-posed nonlinear monotone operator equations is studied in this paper. A discrepancy principle is proposed and justified. A numerical experiment was carried out with the new stopping rule. Numerical experiments show that the proposed stopping rule is efficient. Equations with monotone operators are of interest in many applications.      

多选择NTU结构对策下的社会稳定核心

本文研究了多选择情形下NTU结构对策及其社会稳定核心的理论和应用。定义了多选择NTU结构对策的转移率规则和支付依赖平衡性质,给出了K-K-M-S定理在多选择NTU结构对策下的一个扩展形式,并用扩展后的K-K-M-S定理证明了如果转移率规则包含某些力量函数值,且多选择NTU结构对策关于转移率规则是支付依赖平衡的,则多选择NTU结构对策的社会稳定核心是非空的。     

Rule learning: Ordinal prediction based on rough sets and soft-computing

This work promotes a novel point of view in rough set applications: rough sets rule learning for ordinal prediction is based on rough graphical representation of the rules. Our approach tackles two barriers of rule learning. Unlike in typical rule learning, we construct ordinal prediction with a mathematical approach, rough sets, rather than purely rule quality measures. This construction results in few but significant rules. Moreover, the rules are given in terms of ordinal predictions rather than as unique values. This study also focuses on advancing rough sets theory in favor of soft-computing. Both theoretical and a designed architecture are presented. The features of our proposed approach are illustrated using an experiment in survival analysis. A case study has been performed on melanoma data. The results demonstrate that this innovative system provides an improvement of rule learning both in computing performance for finding the rules and the usefulness of the derived rules.     

Optimality of the Expert Rule Under Partial Information

We study the uncertain dichotomous choice model. In this model a group of decision makers is required to select one of two alternatives. The applications of this model are relevant to a wide variety of areas, such as medicine, management and banking. The decision rule may be the simple majority rule; however, it is also possible to assign more weight to the opinion of members known to be more qualified. The extreme example of such a rule is the expert decision rule. We are concerned with the probability of the expert rule to be optimal. Our purpose is to investigate the behaviour of this probability as a function of the group size for several rather general types of distributions. One such family of distributions is that where the density function of the correctness probability is a polynomial (on the interval [1/2,1]). Our main result is an explicit formula for the probability in question. This contains formerly known results as very special cases.     

Volume-preserving schemes and applications

In this paper, we discuss the conditions for the Euler midpoint rule to be volume-preserving and present Euler type explicit volume-preserving schemes. Some numerical applications to the system defining rigid body motion and the ABC flow are also given.     

A Stable and Convergent Algorithm to Evaluate Hilbert Transform

We deal with the numerical evaluation of the Hilbert transform on the real line by a Gauss type quadrature rule. The convergence and the stability of the method are investigated. The goodness of the numerical results for practical applications is examined.     

An interpretability improvement for fuzzy rule bases obtained by the iterative rule learning approach

Interpretability is one of the key concepts in many of the applications using the fuzzy rule-based approach. It is well known that there are many different criteria around this concept, the complexity being one of them. In this paper, we focus our efforts in reducing the complexity of the fuzzy rule sets. One of the most interesting approaches for learning fuzzy rules is the iterative rule learning approach. It is mainly characterized by obtaining rules covering few examples in final stages, being in most cases useless to represent the knowledge. This behavior is due to the specificity of the extracted rules, which eventually creates more complex set of rules. Thus, we propose a modified version of the iterative rule learning algorithm in order to extract simple rules relaxing this natural trend. The main idea is to change the rule extraction process to be able to obtain more general rules, using pruned searching spaces together with a knowledge simplification scheme able to replace learned rules. The experimental results prove that this purpose is achieved. The new proposal reduces the complexity at both, the rule and rule base levels, maintaining the accuracy regarding to previous versions of the algorithm.     

一种古代的中国算法:盈不足术与牛顿迭代算法的比较

详细讨论了大约在公元前二世纪广泛流行的一种中国算法,这种算法在西方被称作为双假设法。强调指出双假设法是中国算法的一种译版。首次给出了中国算法与牛顿迭代算法之间的联系,如果引入了导数的概念,中国算法可以非常方便地转化为牛顿迭代算法。提出了一种改进的中国算法,并给出中国算法在非线性振动方程中的应用。     

Differential evolution with preferential crossover

We study the mutation operation of the differential evolution algorithm. In particular, we study the effect of the scaling parameter of the differential vector in mutation. We derive the probability density function of points generated by mutation and thereby identify some drawbacks of the scaling parameter. We also visualize the drawbacks using simulation. We then propose a crossover rule, called the preferential crossover rule, to reduce the drawbacks. The preferential crossover rule uses points from an auxiliary population set. We also introduce a variable scaling parameter in mutation. Motivations for these changes are provided. A numerical study is carried out using 50 test problems, many of which are inspired by practical applications. Numerical results suggest that the proposed modification reduces the number of function evaluations and cpu time considerably.     

A Littlewood–Richardson rule for two-step flag varieties

This paper studies the geometry of one-parameter specializations of subvarieties of Grassmannians and two-step flag varieties. As a consequence, we obtain a positive, geometric rule for expressing the structure constants of the cohomology of two-step flag varieties in terms of their Schubert basis. A corollary is a positive, geometric rule for computing the structure constants of the small quantum cohomology of Grassmannians. We also obtain a positive, geometric rule for computing the classes of subvarieties of Grassmannians that arise as the projection of the intersection of two Schubert varieties in a partial flag variety. These rules have numerous applications to geometry, representation theory and the theory of symmetric functions. Mathematics Subject Classification (2000)  Primary 14M15, 14N35, 32M10     

On solving composite power polynomial equations

It is well known that a system of power polynomial equations can be reduced to a single-variable polynomial equation by exploiting the so-called Newton's identities. In this work, by further exploring Newton's identities, we discover a binomial decomposition rule for composite elementary symmetric polynomials. Utilizing this decomposition rule, we solve three types of systems of composite power polynomial equations by converting each type to single-variable polynomial equations that can be solved easily. For each type of system, we discuss potential applications and characterize the number of nontrivial solutions (up to permutations) and the complexity of our proposed algorithmic solution.

     

Fractals control in particle’s velocity

Julia set, a fractal set of the literature of nonlinear physics, has significance for the engineering applications. For example, the fractal structure characteristics of the generalized M–J set could visually reflect the change rule of particle’s velocity. According to the real world requirement, the system need show various particle’s velocity in some cases. Thus, the control of the nonlinear behavior, i.e., Julia set, has attracted broad attention. In this work, an auxiliary feedback control is introduced to effectively control the Julia set that visually reflects the change rule of particle’s velocity. It satisfies the performance requirement of the real world problems.     

Comprehensible credit scoring models using rule extraction from support vector machines

In recent years, support vector machines (SVMs) were successfully applied to a wide range of applications. However, since the classifier is described as a complex mathematical function, it is rather incomprehensible for humans. This opacity property prevents them from being used in many real-life applications where both accuracy and comprehensibility are required, such as medical diagnosis and credit risk evaluation. To overcome this limitation, rules can be extracted from the trained SVM that are interpretable by humans and keep as much of the accuracy of the SVM as possible. In this paper, we will provide an overview of the recently proposed rule extraction techniques for SVMs and introduce two others taken from the artificial neural networks domain, being Trepan and G-REX. The described techniques are compared using publicly available datasets, such as Ripley’s synthetic dataset and the multi-class iris dataset. We will also look at medical diagnosis and credit scoring where comprehensibility is a key requirement and even a regulatory recommendation. Our experiments show that the SVM rule extraction techniques lose only a small percentage in performance compared to SVMs and therefore rank at the top of comprehensible classification techniques.     

Stationary probability distributions for multiresponse linear learning models

A necessary and sufficient condition is given for the existence of stationary probability distributions of a non-Markovian model with linear transition rule. Similar to the Markovian case, stationary probability distributions are characterized as eigenvectors of nonnegative matrices. The model studied includes as special cases the Markovian model as well as the linear learning model and has applications in psychological and biological research, in control theory, and in adaption theory.     

A note on knowledge discovery using neural networks and its application to credit card screening

We address an important issue in knowledge discovery using neural networks that has been left out in a recent article “Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem” by Sexton et al. [R.S. Sexton, S. McMurtrey, D.J. Cleavenger, Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem, European Journal of Operational Research 168 (2006) 1009–1018]. This important issue is the generation of comprehensible rule sets from trained neural networks. In this note, we present our neural network rule extraction algorithm that is very effective in discovering knowledge embedded in a neural network. This algorithm is particularly appropriate in applications where comprehensibility as well as accuracy are required. For the same data sets used by Sexton et al. our algorithm produces accurate rule sets that are concise and comprehensible, and hence helps validate the claim that neural networks could be viable alternatives to other data mining tools for knowledge discovery.     

Optimality conditions for vector optimization problems with variable ordering structures

Our main concern in this article are concepts of nondominatedness w.r.t. a variable ordering structure introduced by Yu [P.L. Yu, Cone convexity, cone extreme points, and nondominated solutions in decision problems with multiobjectives, J. Optim. Theory Appl. 14 (1974), pp. 319–377]. Our studies are motivated by some recent applications e.g. in medical image registration. Restricting ourselves to the case when the values of a cone-valued map defining the ordering structure are Bishop–Phelps cones, we obtain for the first time scalarizing functionals for nondominated elements, Fermat rule, Lagrange multiplier rule and duality results for a single- or set-valued vector optimization problem with a variable ordering structure.     

Applications of the theory of modular forms to number theory

The survey is devoted to arithmetic questions in the theory of modular forms and, in particular, to arithmetic applications of modular functions; mainly only elementary and analytic aspects of this topic, as a rule, for the case of a single variable are presented.Translated from Itogi Nauki i Tekhniki, Algebra, Topologiya, Geometriya, Vol. 15, pp. 5–91, 1977.     

关于两个非线性算子的一个分裂方法(英文)

<正>This paper generalizes a class of projection type methods for monotone variational inequalities to general monotone inclusion.It is shown that when the normal cone operator in projection is replaced by any maximal monotone operator,the resulting method inherits all attractive convergence properties of projection type methods,and allows an adjusting step size rule.Weaker convergence assumption entails an extra projection at each iteration.Moreover,this paper also addresses applications of the resulting method to convex programs and monotone variational inequalities.