Inversely Symmetric Interpolatory Quadrature Rules

We consider interpolatory quadrature rules with nodes and weights satisfying symmetric properties in terms of the division operator. Information concerning these quadrature rules is obtained using a transformation that exists between these rules and classical symmetric interpolatory quadrature rules. In particular, we study those interpolatory quadrature rules with two fixed nodes. We obtain specific examples of such quadrature rules.     

Szegő–Lobatto quadrature rules

Gauss-type quadrature rules with one or two prescribed nodes are well known and are commonly referred to as Gauss–Radau and Gauss–Lobatto quadrature rules, respectively. Efficient algorithms are available for their computation. Szeg? quadrature rules are analogs of Gauss quadrature rules for the integration of periodic functions; they integrate exactly trigonometric polynomials of as high degree as possible. Szeg? quadrature rules have a free parameter, which can be used to prescribe one node. This paper discusses an analog of Gauss–Lobatto rules, i.e., Szeg? quadrature rules with two prescribed nodes. We refer to these rules as Szeg?–Lobatto rules. Their properties as well as numerical methods for their computation are discussed.     

Symmetric Gauss–Lobatto and Modified Anti-Gauss Rules

The present paper is concerned with symmetric Gauss–Lobatto quadrature rules, i.e., with Gauss–Lobatto rules associated with a nonnegative symmetric measure on the real axis. We propose a modification of the anti-Gauss quadrature rules recently introduced by Laurie, and show that the symmetric Gauss–Lobatto rules are modified anti-Gauss rules. It follows that for many integrands, symmetric Gauss quadrature rules and symmetric Gauss–Lobatto rules give quadrature errors of opposite sign.     

Recursive and explicit rules: How can we build student algebraic understanding?

Differing perspectives have been offered about student use of recursive and explicit rules. These include: (a) promoting the use of explicit rules over the use of recursive rules, and (b) encouraging student use of both recursive and explicit rules. This study sought to explore students’ use of recursive and explicit rules by examining the reasoning of 25 sixth-grade students, including a focus on four target students, as they approached tasks in which they were required to develop generalizations while using computer spreadsheets as an instructional tool. The results demonstrate the difficulty that students had moving from the successful use of recursive rules toward explicit rules. In particular, two students abandoned general reasoning, instead focusing on particular values in an attempt to construct explicit rules. It is recommended that students be encouraged to connect recursive and explicit rules as a potential means for constructing successful generalizations.     

Higher-order Newton–Cotes rules with end corrections

We present higher-order quadrature rules with end corrections for general Newton–Cotes quadrature rules. The construction is based on the Euler–Maclaurin formula for the trapezoidal rule. We present examples with 6 well-known Newton–Cotes quadrature rules. We analyze modified end corrected quadrature rules, which consist on a simple modification of the Newton–Cotes quadratures with end corrections. Numerical tests and stability estimates show the superiority of the corrected rules based on the trapezoidal and the midpoint rules.     

A learning process for fuzzy control rules using genetic algorithms

The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the first one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy rules, and the thrid one is a tuning process for adjusting the membership functions of the fuzzy rules. The three components of the learning process are developed formulating suitable genetic algorithms.     

Computational aspects of Boolean cubature

Boolean methods of interpolation [1,4] have been applied to construct multivariate quadrature rules for periodic functions of Korobov classes which are comparable with lattice rules of numerical integration [6,7]. In particular, we introducedd-variate Boolean trapezoidal rules [3,4] andd-variate Boolean midpoint rules [2,4]. The basic tools for constructing Boolean midpoint rules are Boolean midpoint sums. It is the purpose of this paper to use a modification of these Boolean midpoint sums to compute Boolean trapezoidal rules in an efficient way.     

Evolution of fuzzy classifiers using genetic programming

In this paper, we propose a genetic programming (GP) based approach to evolve fuzzy rule based classifiers. For a c-class problem, a classifier consists of c trees. Each tree, T _i , of the multi-tree classifier represents a set of rules for class i. During the evolutionary process, the inaccurate/inactive rules of the initial set of rules are removed by a cleaning scheme. This allows good rules to sustain and that eventually determines the number of rules. In the beginning, our GP scheme uses a randomly selected subset of features and then evolves the features to be used in each rule. The initial rules are constructed using prototypes, which are generated randomly as well as by the fuzzy k-means (FKM) algorithm. Besides, experiments are conducted in three different ways: Using only randomly generated rules, using a mixture of randomly generated rules and FKM prototype based rules, and with exclusively FKM prototype based rules. The performance of the classifiers is comparable irrespective of the type of initial rules. This emphasizes the novelty of the proposed evolutionary scheme. In this context, we propose a new mutation operation to alter the rule parameters. The GP scheme optimizes the structure of rules as well as the parameters involved. The method is validated on six benchmark data sets and the performance of the proposed scheme is found to be satisfactory.     

A linear proportional effort allocation rule

This paper proposes a new class of allocation rules in network games. Like the solution theory in cooperative games of how the Harsanyi dividend of each coalition is distributed among a set of players, this new class of allocation rules focuses on the distribution of the dividend of each network. The dividend of each network is allocated in proportion to some measure of each player’s effort, which is called an effort function. With linearity of the allocation rules, an allocation rule is specified by the effort functions. These types of allocation rules are called linear proportional effort allocation rules. Two famous allocation rules, the Myerson value and the position value, belong to this class of allocation rules. In this study, we provide a unifying approach to define the two aforementioned values. Moreover, we provide an axiomatic analysis of this class of allocation rules, and axiomatize the Myerson value, the position value, and their non-symmetric generalizations in terms of effort functions. We propose a new allocation rule in network games that also belongs to this class of allocation rules.     

An evaluation of value based dispatching rules in a flow shop

Although investment in inventory has been of primary concern in job shops, little attention has been paid to using value-based dispatching rules in an effort to attain satisfactory on-time performance while reducing inventory investment. This paper compares performance based on both time and value measures of three usual time-based rules with six rules which directly incorporate value information in setting priorities. The results indicate that the value-based rules perform their intended function quite well with only slight sacrifice in on-time performance in light to moderately loaded shops. In addition, some of these values rules outperform the best time-based rules on both dimensions in heavily loaded shops.     

Seventh degree integration rules for the cube

A family of integration rules for the cube is discussed and two such rules are compared with other seventh degree rules in the literature.     

Strategy-proofness and public good provision using referenda based on unequal cost sharing

The paper studies strategy-proof cost sharing rules for public good provision based on referenda with different threshold quotas. By appropriately relaxing the assumptions of individual rationality and anonymity we provide a complete characterization of the family of quota rules with (possibly) unequal pricing. We prove that these quota rules are the only cost sharing rules satisfying four conditions: strategy-proofness, non-bossiness, weak continuity and weak anonymity. In addition, the specification of the degree to which individual rationality may be violated results in the selection of a specific “quota” for the referendum. While all these rules are “almost” always efficient when providing the public good and they are also almost everywhere coalitionally strategy-proof, only one family of rules from this class satisfies these two properties everywhere. The rules satisfying these two properties are Moulin’s Conservative Equal Costs Rule and unequal cost sharing variants of Moulin’s rule.     

Blending product-type quadrature rules

The idea of blending which was originally used for bivariate approximation is utilized for the numerical integration of the product of two functions. The combination of three product-type quadrature rules results in a rule with a lower error than each of the original rules. Rules of different exactness degrees as well as compounded rules of different step sizes can be taken for such a combination. Two explicit rules are constructed for demonstration; numerical examples confirm the asymptotic rates of convergence of these rules.     

Pivot rules for linear programming: A survey on recent theoretical developments

The purpose of this paper is to discuss the various pivot rules of the simplex method and its variants that have been developed in the last two decades, starting from the appearance of the minimal index rule of Bland. We are mainly concerned with finiteness properties of simplex type pivot rules. Well known classical results concerning the simplex method are not considered in this survey, but the connection between the new pivot methods and the classical ones, if there is any, is discussed.In this paper we discuss three classes of recently developed pivot rules for linear programming. The first and largest class is the class of essentially combinatorial pivot rules including minimal index type rules and recursive rules. These rules only use labeling and signs of the variables. The second class contains those pivot rules which can actually be considered as variants or generalizations or specializations of Lemke's method, and so they are closely related to parametric programming. The last class has the common feature that the rules all have close connections to certain interior point methods. Finally, we mention some open problems for future research.On leave from the Eötvös University, Budapest, and partially supported by OTKA No. 2115.     

A family of rules for parameter choice in Tikhonov regularization of ill-posed problems with inexact noise level

We consider Tikhonov regularization of linear ill-posed problems with noisy data. The choice of the regularization parameter by classical rules, such as discrepancy principle, needs exact noise level information: these rules fail in the case of an underestimated noise level and give large error of the regularized solution in the case of very moderate overestimation of the noise level. We propose a general family of parameter choice rules, which includes many known rules and guarantees convergence of approximations. Quasi-optimality is proved for a sub-family of rules. Many rules from this family work well also in the case of many times under- or overestimated noise level. In the case of exact or overestimated noise level we propose to take the regularization parameter as the minimum of parameters from the post-estimated monotone error rule and a certain new rule from the proposed family. The advantages of the new rules are demonstrated in extensive numerical experiments.     

A distance-based comparison of basic voting rules

In this paper we provide a comparison of different voting rules in a distance-based framework with the help of computer simulations. Taking into account the informational requirements to operate such voting rules and the outcomes of two well-known reference rules, we identify the Copeland rule as a good compromise between these two reference rules. It will be shown that the outcome of the Copeland rule is “close” to the outcomes of the reference rules, but it requires less informational input and has lower computational complexity.     

Good Lattice Rules in Weighted Korobov Spaces with General Weights

We study the problem of multivariate integration and the construction of good lattice rules in weighted Korobov spaces with general weights. These spaces are not necessarily tensor products of spaces of univariate functions. Sufficient conditions for tractability and strong tractability of multivariate integration in such weighted function spaces are found. These conditions are also necessary if the weights are such that the reproducing kernel of the weighted Korobov space is pointwise non-negative. The existence of a lattice rule which achieves the nearly optimal convergence order is proven. A component-by-component (CBC) algorithm that constructs good lattice rules is presented. The resulting lattice rules achieve tractability or strong tractability error bounds and achieve nearly optimal convergence order for suitably decaying weights. We also study special weights such as finite-order and order-dependent weights. For these special weights, the cost of the CBC algorithm is polynomial. Numerical computations show that the lattice rules constructed by the CBC algorithm give much smaller worst-case errors than the mean worst-case errors over all quasi-Monte Carlo rules or over all lattice rules, and generally smaller worst-case errors than the best Korobov lattice rules in dimensions up to hundreds. Numerical results are provided to illustrate the efficiency of CBC lattice rules and Korobov lattice rules (with suitably chosen weights), in particular for high-dimensional finance problems.     

Efficient calculation of the worst-case error and (fast) component-by-component construction of higher order polynomial lattice rules

We show how to obtain a fast component-by-component construction algorithm for higher order polynomial lattice rules. Such rules are useful for multivariate quadrature of high-dimensional smooth functions over the unit cube as they achieve the near optimal order of convergence. The main problem addressed in this paper is to find an efficient way of computing the worst-case error. A general algorithm is presented and explicit expressions for base 2 are given. To obtain an efficient component-by-component construction algorithm we exploit the structure of the underlying cyclic group. We compare our new higher order multivariate quadrature rules to existing quadrature rules based on higher order digital nets by computing their worst-case error. These numerical results show that the higher order polynomial lattice rules improve upon the known constructions of quasi-Monte Carlo rules based on higher order digital nets.     

A SET OF SYMMETRIC QUADRATURE RULES ON TRIANGLES AND TETRAHEDRA

We present a program for computing symmetric quadrature rules on triangles and tetrahedra. A set of rules are obtained by using this program. Quadrature rules up to order 21 on triangles and up to order 14 on tetrahedra have been obtained which are useful for use in finite element computations. All rules presented here have positive weights with points lying within the integration domain.     

An explicit basis for the admissible inference rules of the modal logics extending <Emphasis Type="Italic">S</Emphasis>4.1 And <Emphasis Type="Italic">Grz</Emphasis>

We study bases for the admissible inference rules in a broad class of modal logics. We construct an explicit basis for all admissible rules in the logics S4.1, Grz, and their extensions whose number is at least countable. The resulting basis consists of an infinite sequence of rules in a concise and simple form. In the case of a logic of finite width a basis for all admissible rules consists of a finite sequence of rules.