Integrating Lucid’s Declarative Dataflow Paradigm into Object-Orientation

The dataflow language Lucid applies concepts from intensional logic to declarative ISWIM expressions which are intensionalised relative to the dimension of time, thus introducing the notion of an expression’s history. Lucian, a language derived from Lucid, embeds dataflow into object-orientation allowing the intensionalisation of objects. Lucian introduces the notion of a declarative intensional object as the history of an object’s transformations. This paper discusses the embedding relationships and semantics of conjoining the dataflow and object-oriented paradigms to provide the language Lucian for defining intensional objects.      

CTL AgentSpeak(L): A specification language for agent programs

This work introduces CTL AgentSpeak(L), a logic to specify and verify expected properties of rational agents implemented in the well-known agent oriented programming language AgentSpeak(L). Our approach is closely related to the BDICTL multi-modal logic, used to reason about agents in terms of their beliefs (B), desires (D), intentions (I), and the temporal logic CTL. A new interpretation for the temporal operators, grounded in the transition system induced by the operational semantics of AgentSpeak(L), is proposed. The main contribution of the approach is a better understanding of the relation between the programming language and its logical specification, enabling us to prove expected or desired properties for any agent programmed in the language, e.g., commitment strategies. The results, as well as the specification language proposed, are very useful to reconcile computational and philosophical aspects of practical reasoning, e.g., approaching single-minded commitment as a policy-based reconsideration case.     

On specifications,subset types and interpretation of proposition in type theory

In this paper we discuss some practical aspects of using type theory as a programming and specification language, where the viewpoint is to use it not only as a basis for program synthesis but also as a programming language with a programming logic allowing us to do ordinary verification.The subset type has been added to type theory in order to avoid irrelevant information in programs. We give an example of a proof which illustrates the problems that may occur if the subset type is used in specifications when we have the standard interpretation of propositions as types. Harrop-formulas and Squash are then discussed as solutions to these problems. It is argued that they are not acceptable from a practical point of view.An extension of the theory to include the two new judgment forms:A is a proposition, andA is true, is then given and explained in terms of the old theory. The logical constants are no longer identified with the corresponding type theoretical constants, but propositions are interpreted as Gödel formulas, which allow us to introduce and justify logical rules similar to rules for classical logic. The interpretation is extended to include predicates defined by using reflections of the ordinary definition of Gödel formulas in a type of small propositions.The programming example is then revisited and stronger elimination rules are discussed.     

Symbolic language in early modern mathematics: The Algebra of Pierre Hérigone (1580–1643)

The creation of a formal mathematical language was fundamental to making mathematics algebraic. A landmark in this process was the publication of In artem analyticem isagoge by François Viète (1540–1603) in 1591. This work was diffused through many other algebra texts, as in the section entitled Algebra in the Cursus mathematicus (Paris, 1634, 1637, 1642; second edition 1644) by Pierre Hérigone (1580–1643). The aim of this paper is to analyze several features of Hérigone's Algebra. Hérigone was one of the first mathematicians to consider that symbolic language might be used as a universal language for dealing with pure and mixed mathematics. We show that, although Hérigone generally used Viète's statements, his notation, presentation style, and procedures in his algebraic proofs were quite different from Viète's. In addition, we emphasize how Hérigone handled algebraic operations and geometrical procedures by making use of propositions from Euclid's Elements formulated in symbolic language.     

A Comparison of Programming Languages and Algebraic Notation as Expressive Languages for Physics

The purpose of the present work is to consider some of the implications of replacing, for the purposes of physics instruction, algebraic notation with a programming language. Whatis novel is that, more than previous work, I take seriously the possibility that a programming language can function as the principle representational system for physics instruction. This means treating programming as potentially having a similar status and performing a similar function to algebraic notation in physics learning. In order to address the implications of replacing the usual notational system with programming, I begin with two informal conjectures: (1) Programming-based representations might be easier for students to understand than equation-based representations, and (2) programming-based representations might privilege a somewhat different ``intuitive vocabulary.' If the second conjecture is correct, it means that the nature of the understanding associated with programming-physics might be fundamentally different than the understanding associated with algebra-physics.In order to refine and address these conjectures, I introduce a framework based around two theoretical constructs, what I callinterpretive devices and symbolic forms. A conclusion of this work is that algebra-physics can be characterized as a physics of balance and equilibrium, and programming-physics as a physics of processes and causation. More generally, this work provides a theoretical and empirical basis for understanding how the use of particular symbol systems affects students' conceptualization.This revised version was published online in September 2005 with corrections to the Cover Date.     

Nondifferentiable Minimax Fractional Programming Problems with (C, α, ρ, d)-Convexity

In this paper, we present necessary optimality conditions for nondifferentiable minimax fractional programming problems. A new concept of generalized convexity, called (C, α, ρ, d)-convexity, is introduced. We establish also sufficient optimality conditions for nondifferentiable minimax fractional programming problems from the viewpoint of the new generalized convexity. When the sufficient conditions are utilized, the corresponding duality theorems are derived for two types of dual programs. This research was partially supported by NSF and Air Force grants     

On <Emphasis Type="Italic">G</Emphasis>-invex multiobjective programming. Part II. Duality

This paper represents the second part of a study concerning the so-called G-multiobjective programming. A new approach to duality in differentiable vector optimization problems is presented. The techniques used are based on the results established in the paper: On G-invex multiobjective programming. Part I. Optimality by T.Antczak. In this work, we use a generalization of convexity, namely G-invexity, to prove new duality results for nonlinear differentiable multiobjective programming problems. For such vector optimization problems, a number of new vector duality problems is introduced. The so-called G-Mond–Weir, G-Wolfe and G-mixed dual vector problems to the primal one are defined. Furthermore, various so-called G-duality theorems are proved between the considered differentiable multiobjective programming problem and its nonconvex vector G-dual problems. Some previous duality results for differentiable multiobjective programming problems turn out to be special cases of the results described in the paper.     

Root Decomposition of the Ternary Malcev Algebra <Emphasis Type="Italic">M</Emphasis><Subscript>8</Subscript>

A root decomposition is constructed of the simple eight-dimensional ternary Malcev algebra M ₈. In result, M ₈ is equipped with a structure of a Z ₃-graded ternary algebra.Original Russian Text Copyright © 2005 Pozhidaev A. P.The author was supported by the Russian Science Support Foundation and partially by the Russian Foundation for Basic Research (Grant 05-01-00230).__________Translated from Sibirskii Matematicheskii Zhurnal, Vol. 46, No. 4, pp. 901–906, July–August, 2005.     

Inverse optimization for linearly constrained convex separable programming problems

In this paper, we study inverse optimization for linearly constrained convex separable programming problems that have wide applications in industrial and managerial areas. For a given feasible point of a convex separable program, the inverse optimization is to determine whether the feasible point can be made optimal by adjusting the parameter values in the problem, and when the answer is positive, find the parameter values that have the smallest adjustments. A sufficient and necessary condition is given for a feasible point to be able to become optimal by adjusting parameter values. Inverse optimization formulations are presented with ℓ₁ and ℓ₂ norms. These inverse optimization problems are either linear programming when ℓ₁ norm is used in the formulation, or convex quadratic separable programming when ℓ₂ norm is used.     

Normalising geometrical figures: dynamic manipulation and construction of meanings for ratio and proportion

Enlarging-shrinking geometrical figures by 13 year-olds is studied during the implementation of proportional geometric tasks in the classroom. Students worked in groups of two using ‘Turtleworlds’, a piece of geometrical construction software which combines symbolic notation, through a programming language, with dynamic manipulation of geometrical objects by dragging on sliders representing variable values. In this paper we study the students’ normalising activity, as they use this kind of dynamic manipulation to modify ‘buggy’ geometrical figures while developing meanings for ratio and proportion. We describe students’ normative actions in terms of four distinct Dynamic Manipulation Schemes (Reconnaissance, Correlation, Testing, Verification). We discuss the potential of dragging for mathematical insight in this particular computational environment, as well as the purposeful nature of the task which sets up possibilities for students to appreciate the utility of proportional relationships.     

Pairwise ?-independence of the sets TiA for a mixing transformation T

If an ergodic automorphism T of a probability space is not partially rigid, then for any numbers a ∈ (0, 1) and ɛ > 0 there exists a set A such that all sets T ⁱ A, i > 0, are pairwise ɛ-independent. __________ Translated from Funktsional’nyi Analiz i Ego Prilozheniya, Vol. 43, No. 2, pp. 88–91, 2009 Original Russian Text Copyright ? by V. V. Ryzhikov This research was carried out under the Program for Support of Leading Scientific Schools in the Russian Federation (grant no. 6849.2006.1).     

Path-following interior point algorithms for the Cartesian <Emphasis Type="Italic">P</Emphasis><Subscript>*</Subscript>(κ)-LCP over symmetric cones

In this paper, we establish a theoretical framework of path-following interior point algorithms for the linear complementarity problems over symmetric cones (SCLCP) with the Cartesian P _*(κ)-property, a weaker condition than the monotonicity. Based on the Nesterov-Todd, xy and yx directions employed as commutative search directions for semidefinite programming, we extend the variants of the short-, semilong-, and long-step path-following algorithms for symmetric conic linear programming proposed by Schmieta and Alizadeh to the Cartesian P _*(κ)-SCLCP, and particularly show the global convergence and the iteration complexities of the proposed algorithms. This work was supported by National Natural Science Foundation of China (Grant Nos. 10671010, 70841008)     

On <Emphasis Type="Italic">G</Emphasis>-invex multiobjective programming. Part I. Optimality

In this paper, a generalization of convexity, namely G-invexity, is considered in the case of nonlinear multiobjective programming problems where the functions constituting vector optimization problems are differentiable. The modified Karush-Kuhn-Tucker necessary optimality conditions for a certain class of multiobjective programming problems are established. To prove this result, the Kuhn-Tucker constraint qualification and the definition of the Bouligand tangent cone for a set are used. The assumptions on (weak) Pareto optimal solutions are relaxed by means of vector-valued G-invex functions.     

How to Play M 13?

A puzzle called “M ₁₃” J. H. Conway has described recently is explained. We report an implementation of the puzzle in the programming language Java. The program allows the human user to “play M ₁₃” interactively (and to cheat by solving it automatically). The program is an example on how to bring to life a nice piece of discrete mathematics. In this sense it presents not only a didactical way of seeing “mathematics at work”, but also displays the stabilizer chain method developed by C. Sims to solve group theoretic puzzles, the most famous of which being Rubik's cube.     

An Estimate for the <Emphasis Type="Italic">n</Emphasis>th Minimal Error of Linear Algorithms for One Problem in a Normed Vector Space

We find an estimate for the nth minimal error of linear algorithms for some problem defined in a finite-dimensional space with values in an arbitrary normed vector space.Original Russian Text Copyright © 2005 Sidorov S. P.The author was supported by the Russian Foundation for Basic Research (Grant 04-01-00060), the State Maintenance Programs for the Leading Scientific Schools of the Russian Federation (Grant 1295.2003.1), and the Program Universities of Russia (Grant 04.01.374).__________Translated from Sibirskii Matematicheskii Zhurnal, Vol. 46, No. 3, pp. 673–678, May–June, 2005.     

Least Squares Convex-Concave Data Smoothing

We consider n noisy measurements of a smooth (unknown) function, which suggest that the graph of the function consists of one convex and one concave section. Due to the noise the sequence of the second divided differences of the data exhibits more sign changes than those expected in the second derivative of the underlying function. We address the problem of smoothing the data so as to minimize the sum of squares of residuals subject to the condition that the sequence of successive second divided differences of the smoothed values changes sign at most once. It is a nonlinear problem, since the position of the sign change is also an unknown of the optimization process. We state a characterization theorem, which shows that the smoothed values can be derived by at most 2n – 2 quadratic programming calculations to subranges of data. Then, we develop an algorithm that solves the problem in about O(n ²) computer operations by employing several techniques, including B-splines, the use of active sets, quadratic programming and updating methods. A Fortran program has been written and some of its numerical results are presented. Applications of the smoothing technique may be found in scientific, economic and engineering calculations, when a potential shape for the underlying function is an S-curve. Generally, the smoothing calculation may arise from processes that show initially increasing and then decreasing rates of change.     

Real <Emphasis Type="Italic">AW</Emphasis><Superscript>*</Superscript>-Algebras of type I

Let R be a real AW ^*-algebra, and suppose that its complexification M = R + iR is also a (complex) AW ^*-algebra. We prove that R is of type I if and only if so is M.Translated from Funktsionalnyi Analiz i Ego Prilozheniya, Vol. 38, No. 4, pp. 79–81, 2004Original Russian Text Copyright © by Sh. A. Ayupov     

Simulating and visualizing neural networks with Mathematica

Artificial neural networks (ANNs) are both mathematical models of the neural basis of higher-order cognitive functions, such as learning, and adaptive variations of the general linear and nonlinear regression. Students of psychology and cognitive science typically encounter ANNs in both contexts of their studies, especially at the graduate level, however, many of these students do not possess the programming skills to write their own simulations to test their application as cognitive and statistical models. In this paper, simulations using the mathematical programming language Mathematica are used to develop appropriate visualizations of one the foundation topics in ANNs (understanding why linear associative networks cannot learn the nonlinearly separable XOR function). It is argued that Mathematica and similar high-level interpreted packages provide a more accessible environment for nonprogramming students to further their understanding of this key area of psychological science and mathematical modelling.     

Polynomials of Least Deviation from Zero

A new family of polynomials of least deviation from zero is defined on the unit disk B. Lower bounds for best approximations in the space L _p (B), p ≥ 1, are given.__________Translated from Matematicheskie Zametki, vol. 78, no. 2, 2005, pp. 308–313.Original Russian Text Copyright © 2005 by V. A. Yudin.     

Mixed-Type Duality on Nonsmooth Minimax Fractional Programming Involving Exponential (p,r)-Invexity

Considering a nonsmooth minimax fractional programming problem involving exponential (p, r)-invexity, we construct a mixed-type dual problem, which is performed by an incomplete Lagrangian dual model. This mixed-type dual model involves the Wolfe type dual and Mond-Weir type dual as the special cases under exponential (p, r)-invexity. We establish the mixed-type duality problem with conditions for exponential (p, r)-invexity and prove that the optimal values of the primary problem and the mixed-type duality problem have no duality gap under the framwork of exponential (p, r)-invexity.