Hierarchical sets in mathematical programming modeling languages

The mathematical notation commonly applied for the formulation of mathematical programming models is extended to include hierarchical structures. The proposed notation is related to hierarchical set concepts in the languages UIMP, AMPL, and LPL. With the proposed notation it is possible to aggregate and disaggregate over hierarchical structures. In addition, views are introduced to permit the use of hierarchical substructures and to create new hierarchies out of existing ones. The proposed notation for hierarchical sets and views is illustrated by applying it to the representation and estimation of social accounting matrices (SAMs).     

Representation of time in mathematical programming modeling languages

Time-staged mathematical programming models have a planning horizon that is divided into a sequence of consecutive time periods. For the modeling of this sequence of time periods the use of calendars is proposed as an additional set concept for mathematical programming modeling languages. The definition of calendars is based on familiar notions such as set, ordering, interval length and functions. A calendar is an interval set and can be used to verify automatically the proper time referencing in stock balances. When a calendar is also a difference set, then backward and forward time referencing can be stated with the explicit use of time units. For models with a rolling horizon, concise and flexible ways to specify the structure of calendars are presented. The aggregation of raw data into model parameter values is supported by linking calendars that represent different time scales. The influence of the proposed calendar concept on the human ability to understand, maintain and verify models is analyzed throughout the paper on the basis of selected examples.     

Contradictions and uncertainty in scientists’ mathematical modeling and interpretation of data

Contradictions have been recognized as important factors in learning (conceptual change), because they require students to engage in deep reflection that leads to accommodation and learning. However, in the face of uncertainty, confirmation bias and the theory-laden nature of observation may not allow the recognition of a situation as harboring a contradiction. In the present study, I analyze a meeting in which a scientific research team presents its results to an informed audience. I show that with hindsight, there are contradictions in the mathematical models that the scientists use and the graph interpretations that they produce. Because the contradictions went unnoticed, they could not become a determinant factor in the process. This has implications for thinking about the role of uncertainty and contradiction as factors in and of mathematical learning.     

On a method for mathematical modeling of chemical synapses

We introduce a new mathematical model of a circular neural network with unidirectional chemical bonds. The model is a singularly perturbed system of delay differential-difference equations. We study the existence and stability of relaxation periodic motions in the system. It is proved that the well-known buffer phenomenon can occur in the model.     

On accuracy of mathematical languages used to deal with the Riemann zeta function and the Dirichlet eta function

The Riemann Hypothesis has been of central interest to mathematicians for a long time and many unsuccessful attempts have been made to either prove or disprove it. Since the Riemann zeta function is defined as a sum of the infinite number of items, in this paper, we look at the Riemann Hypothesis using a new applied approach to infinity allowing one to easily execute numerical computations with various infinite and infinitesimal numbers in accordance with the principle ‘The part is less than the whole’ observed in the physical world around us. The new approach allows one to work with functions and derivatives that can assume not only finite but also infinite and infinitesimal values and this possibility is used to study properties of the Riemann zeta function and the Dirichlet eta function. A new computational approach allowing one to evaluate these functions at certain points is proposed. Numerical examples are given. It is emphasized that different mathematical languages can be used to describe mathematical objects with different accuracies. The traditional and the new approaches are compared with respect to their application to the Riemann zeta function and the Dirichlet eta function. The accuracy of the obtained results is discussed in detail.     

Faces of mathematical modeling

In this paper I will discuss and exemplify my perspectives on how to teach mathematical modeling, as well as discuss quite different faces of mathematical modeling. The field of mathematical modeling is so enormous and vastly outspread and just not possible to comprehend in one single paper; or in one single book, or even in one single book shelf. Nevertheless, I have found that the more I can illuminate some of the various interpretations and perceptions of mathematical modeling which exists in the world around us when introducing and starting a course in mathematical modeling, the more benefit I will have during the course when discussing the need and purpose of mathematical modeling with the students. The fact that only some models fit within the practical teaching and assessing of a course in mathematical modeling, does not exclude the importance to illustrate that the world of today cannot go on without mathematical modeling. Students are nevertheless much more charmed with some models of reality than others.     

On context-free and Szilard languages

Szilard languages of context-free grammars are studied. Especially, classical pumping, generalized pumping, Sokolowski's criterion, and semilinearity are considered as possible distinguishing properties between context-free and Szilard languages.This work was supported by the Academy of Finland.     

On intermediate factorial languages

We prove that factorial languages defined over non-trivial finite alphabets under some natural conditions have intermediate complexity functions, i.e., the number of words in such a language grows faster than any polynomial but slower than any exponential function.     

On dos languages and dos mappings

Roughly speaking,DOS systems formalize the notion of generatively deterministic context free grammars. We explore the containment relationships among the class of languages generated byDOS systems and other subclasses of the class of context free languages. Leaving the axiom of aDOS system unspecified yields aDOS scheme, which defines a mapping from words to languages over a given alphabet. We explore the algebraic properties ofDOS mappings and obtain an algebraic characterization of a fundamental subclass of theDOS mappings generated byDOS schemes which are propagating (non erasing) and have no cycles of derivability among letters of the alphabet. We apply this characterization to show that the mapping equivalence problem for propagatingDOS schemes is decidable.     

On modeling left-truncated loss data using mixtures of distributions

A new statistical methodology is developed for fitting left-truncated loss data by using the G-component finite mixture model with any combination of Gamma, Lognormal, and Weibull distributions. The EM algorithm, along with the emEM initialization strategy, is employed for model fitting. We propose a new grid map which considers the model selection criterion (AIC or BIC) and risk measures at the same time, by using the entire space of models under consideration. A simulation study validates our proposed approach. The application of the proposed methodology and use of new grid maps are illustrated through analyzing a real data set that includes left-truncated insurance losses.     

Three modeling paradigms in mathematical programming

Celebrating the sixtieth anniversary since the zeroth International Symposium on Mathematical Programming was held in 1949, this paper discusses several promising paradigms in mathematical programming that have gained momentum in recent years but have yet to reach the main stream of the field. These are: competition, dynamics, and hierarchy. The discussion emphasizes the interplay between these paradigms and their connections with existing subfields including disjunctive, equilibrium, and nonlinear programming, and variational inequalities. We will describe the modeling approaches, mathematical formulations, and recent results of these paradigms, and sketch some open mathematical and computational challenges arising from the resulting optimization and equilibrium problems. Our goal is to elucidate the need for a systematic study of these problems and to inspire new research in the field.     

Case-based modeling and the SACS Toolkit: a mathematical outline

Researchers in the social sciences currently employ a variety of mathematical/computational models for studying complex systems. Despite the diversity of these models, the majority can be grouped into one of three types: agent (rule-based) modeling, dynamical (equation-based) modeling and statistical (aggregate-based) modeling. The purpose of the current paper is to offer a fourth type: case-based modeling. To do so, we review the SACS Toolkit: a new method for quantitatively modeling complex social systems, based on a case-based, computational approach to data analysis. The SACS Toolkit is comprised of three main components: a theoretical blueprint of the major components of a complex system (social complexity theory); a set of case-based instructions for modeling complex systems from the ground up (assemblage); and a recommended list of case-friendly computational modeling techniques (case-based toolset). Developed as a variation on Byrne (in Sage Handbook of Case-Based Methods, pp.?260?C268, 2009), the SACS Toolkit models a complex system as a set of k-dimensional vectors (cases), which it compares and contrasts, and then condenses and clusters to create a low-dimensional model (map) of a complex system??s structure and dynamics over time/space. The assembled nature of the SACS Toolkit is its primary strength. While grounded in a defined mathematical framework, the SACS Toolkit is methodologically open-ended and therefore adaptable and amenable, allowing researchers to employ and bring together a wide variety of modeling techniques. Researchers can even develop and modify the SACS Toolkit for their own purposes. The other strength of the SACS Toolkit, which makes it a very effective technique for modeling large databases, is its ability to compress data matrices while preserving the most important aspects of a complex system??s structure and dynamics across time/space. To date, while the SACS Toolkit has been used to study several topics, a mathematical outline of its case-based approach to quantitative analysis (along with a case study) has yet to be written?Chence the purpose of the current paper.     

A comparison of mathematical programming modeling systems

We compare three mathematical programming modeling languages, GAMS, OMNI and MathPro. To understand the properties of these languages, we formulate four linear programs in each language. The formulations are representative of the kinds of model structures one encounters in practice. Each of the languages focuses on a different view of linear programs. GAMS approximates algebra, OMNI uses the activity view and MathPro uses a block schematic. We summarize our experiences with the languages and suggest areas for further enhancement.     

LP modeling languages for personal computers: A comparison

The purpose of this paper is to compare and contrast the modeling capabilities of seven algebraic modeling languages (ML) available today, namely, AMPL, GAMS, LINGO, LPL, MPL, PC-PROG and XPRESS-LP. In general, these MLs do an excellent job of providing an interface with which the modeler can specify an algebraically formatted linear program (LP). That is, each ML provides a substantial improvement in time and convenience over the matrix generator/report writers of the last few decades. Further, each of the MLs provides: (1) significant flexibility in model specification, instantiation and modification, (2) effective and efficient conversion from algebraic to solver format, and (3) an understandable and, for the most part, self-documenting model representation. In addition, each of the MLs is constantly being updated and upgraded to provide additional capabilities sought by practitioners and users. However, as shown in the fifteen tables provided in the body of this paper, each ML has its own set of competitive advantages. For example, the most integrated environments (i.e. those integrating the modeling language with a full-screen editor, data import capabilities and a solver) are provided by LINGO and PC-PROG. The most user-friendly interfaces are provided by MPL and PC-PROG, both of which provide window-based interfaces to create models and pop-up windows to display error messages; MPL also uses pull-down menus to specify various operations, whereas PC-PROG uses function keys for operational control. Package costs are led by a current (March, 1991) introductory offer from LINGO. Modeling effectiveness, especially with respect to flexibility in specifying arithmetic statements, is led by GAMS and LPL. Model compactness, as measured by the number of lines required to specify a model, is led by AMPL, LPL, MPL and PC-PROG; LPL, MPL and PC-PROG also provide context sensitive editors which automatically position the cursor where the error was detected. And finally, the most comprehensive user documentation is provided by GAMS, whereas GAMS, LINGO and LPL provide extensive libraries of sample models for those users who learn by example.