A Declarative Modeling Framework that Integrates Solution Methods

Constraint programming offers modeling features and solution methods that are unavailable in mathematical programming but are often flexible and efficient for scheduling and other combinatorial problems. Yet mathematical programming is well suited to declarative modeling languages and is more efficient for some important problem classes. This raises this issue as to whether the two approaches can be combined in a declarative modeling framework. This paper proposes a general declarative modeling system in which the conditional structure of the constraints shows how to integrate any “checker” and any special-purpose “solver”. In particular this integrates constraint programming and optimization methods, because the checker can consist of constraint propagation methods, and the solver can be a linear or nonlinear programming routine.     

Counting systems and the First Hilbert problem

The First Hilbert problem is studied in this paper by applying two instruments: a new methodology distinguishing between mathematical objects and mathematical languages used to describe these objects; and a new numeral system allowing one to express different infinite numbers and to use these numbers for measuring infinite sets. Several counting systems are taken into consideration. It is emphasized in the paper that different mathematical languages can describe mathematical objects (in particular, sets and the number of their elements) with different accuracies. The traditional and the new approaches are compared and discussed.     

New architectures for constructed complex systems

This paper is an overview of our research program in intelligent systems. Our object of study is constructed complex systems, which are software and hardware systems mediated or managed by computers. We describe how biological systems provide stiff competition for constructed complex systems in the areas of autonomy and intelligence, robustness, adaptability, and communication. We describe our computationally reflective integration infrastructure, called ‘wrappings', and show how it can provide many of the necessary flexibilities. We also describe two directions of research in computational semiotics, which for us means the study of the use of symbols by computing systems. We describe our ‘conceptual categories', which are a method of knowledge representation that supports these flexibilities, and some new results on symbol systems, which leads to some new mathematical questions about what can be represented in formal systems and how they can be extended automatically. These are then combined to describe our architecture, which we are currently in the process of implementing.     

New constructs for the description of combinatorial optimization problems in algebraic modeling languages

Algebraic languages are at the heart of many successful optimization modeling systems, yet they have been used with only limited success for combinatorial (or discrete) optimization. We show in this paper, through a series of examples, how an algebraic modeling language might be extended to help with a greater variety of combinatorial optimization problems. We consider specifically those problems that are readily expressed as the choice of a subset from a certain set of objects, rather than as the assignment of numerical values to variables. Since there is no practicable universal algorithm for problems of this kind, we explore a hybrid approach that employs a general-purpose subset enumeration scheme together with problem-specific directives to guide an efficient search.     

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.     

Model management systems: A survey

This paper provides a survey of model management literature within the mathematical modeling domain. The first part of the survey is a review and a summary of the literature. After giving some basic definitions of modeling, modeling life cycle, and model management, two representative algebraic modeling languages followed by three approaches to modeling are introduced. These approaches are database, graph-based, and knowledge-based. The discussion is followed by a review of two specialized model management systems. The second part of the survey is a categorization of various modeling systems based on the modeling functions they provide and some of their features. These functions include life cycle support and model base administration. The degree of model independence provided by model management systems and the implemented environment systems is also summarized. The last part of the paper provides directions for future research.     

Integrating restoration and scheduling decisions for disrupted interdependent infrastructure systems

We consider the problem faced by managers of critical civil interdependent infrastructure systems of restoring essential public services after a non-routine event causes disruptions to these services. In order to restore the services, we must determine the set of components (or tasks) that will be temporarily installed or repaired, assign these tasks to work groups, and then determine the schedule of each work group to complete the tasks assigned to it. These restoration planning and scheduling decisions are often undertaken in an independent, sequential manner. We provide mathematical models and optimization algorithms that integrate the restoration and planning decisions and specifically account for the interdependencies between the infrastructure systems. The objective function of this problem provides a measure of how well the services are being restored over the horizon of the restoration plan, rather than just focusing on the performance of the systems after all restoration efforts are complete. We test our methods on realistic data representing infrastructure systems in New York City. Our computational results demonstrate that we can provide integrated restoration and scheduling plans of high quality with limited computational resources. We also discuss the benefits of integrating the restoration and scheduling decisions.     

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.     

Teachers creating mathematical models to fairly distribute school funding

Our study aims to investigate what teachers do as they draw on their mathematical understanding and personal experiences to engage in social justice-oriented mathematical modeling. We analyze what ideas were expressed by teachers regarding their mathematical identities while they explore, wrestle with, and reconcile the underlying societal values that support mathematical models. We invited groups of teachers to make mathematical models for distributing school funding given real data from diverse, anonymized schools. Our results show that teachers created and refined diverse mathematical models to connect the mathematical world and societal space and these models reflected different societal values. Drawing on their own experiences, teachers expressed a sense of agency and critical consciousness while making decisions about school funding. This study delineates mathematical contents and processes necessary for advancing a societal goal of fairly distributing funds and we explore how teachers connect to this context as learners and members of society.     

Probabilistic decision graphs for optimization under uncertainty

This paper provides a survey on probabilistic decision graphs for modeling and solving decision problems under uncertainty. We give an introduction to influence diagrams, which is a popular framework for representing and solving sequential decision problems with a single decision maker. As the methods for solving influence diagrams can scale rather badly in the length of the decision sequence, we present a couple of approaches for calculating approximate solutions. The modeling scope of the influence diagram is limited to so-called symmetric decision problems. This limitation has motivated the development of alternative representation languages, which enlarge the class of decision problems that can be modeled efficiently. We present some of these alternative frameworks and demonstrate their expressibility using several examples. Finally, we provide a list of software systems that implement the frameworks described in the paper.     

Probabilistic decision graphs for optimization under uncertainty

This paper provides a survey on probabilistic decision graphs for modeling and solving decision problems under uncertainty. We give an introduction to influence diagrams, which is a popular framework for representing and solving sequential decision problems with a single decision maker. As the methods for solving influence diagrams can scale rather badly in the length of the decision sequence, we present a couple of approaches for calculating approximate solutions. The modeling scope of the influence diagram is limited to so-called symmetric decision problems. This limitation has motivated the development of alternative representation languages, which enlarge the class of decision problems that can be modeled efficiently. We present some of these alternative frameworks and demonstrate their expressibility using several examples. Finally, we provide a list of software systems that implement the frameworks described in the paper.     

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.     

Mathematical notation and the use of functions (Poster Presentation)

In contrast to natural languages, mathematical notation is accepted as being exceptionally precise. It shall make mathematical statements unambiguous, it shall allow formal manipulation, it is model for programming languages, computer algebra systems and machine provers. However, what is traditional notation and is it indeed as precise as expected? We discuss some examples of notation which require caution. How are they adapted in computer algebra systems? Can we improve them somehow? What can we learn from functional programming? (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Constraint propagation,relational arithmetic in AI systems and mathematical programs

This paper explores the interrelationships between methods developed in mathematical programming to discover the structure of constraint (feasibility) sets and constraint propagation over networks used by some AI systems to perform inferences about quantities. It is shown that some constraint set problems in mathematical programming are equivalent to inferencing problems for constraint networks with interval labels. This makes the inference and query capabilities associated with AI systems that use logic programming, directly accessible to mathematical programming systems. On the other hand, traditional and newer methods which mathematical programming uses to obtain information about its associated feasibility set can be used to determine the propagation of constraints in a network of nodes of an AI system. When viewed from this point of view, AI problems can access additional mathematical programming analytical tools including new ways to incorporate qualitative data into constraint sets via interval and fuzzy arithmetic.This work was partially supported by the Industrial Consortium to Develop an Intelligent Mathematical Programming System — Amoco Oil Company, General Research Corporation, Ketron Management Science, Shell Oil Company, MathPro, and US West Advanced Technologies.     

Reliability analysis of an Attitude Determination and Control System (ADCS) through the RAMSAS method

Reliability analysis of modern large-scale systems is a challenging task which could benefit from the jointly exploitation of recent model-based approaches and simulation techniques to flexibly evaluate the system reliability performances and compare different design choices. In this context, RAMSAS, a model-based method which supports the reliability analysis of systems through simulation by combining the benefits of popular OMG modeling languages with wide adopted simulation and analysis environments, has been recently proposed. This paper shows the effectiveness of RAMSAS through a real case study concerning the reliability analysis of an Attitude Determination and Control System (ADCS) of a satellite.     

The Theory of SML Schema-Directed Query

SML is a modeling language for the structured modeling framework, which represents the semantics as well as the mathematical structure of a model. This paper uses an SML approach to improve the object based universal relation data model. By this approach, both the relational structure of a database and the objects in relations are automatically derived by the associated SML schema. The interpretation part of an SML schema allows users to easily learn the meanings of the data before performing universal relation queries; the queries are then computed by using the automatically derived objects. With a goal of making queries simpler, this paper presents theories, table naming conventions, a confirmation approach, and a unified example illustrating many different concepts. It helps lay the foundation for the eventual development of a remarkably easy user interface for ad hoc query in computer-based modeling systems. We are hopeful that the results may in the future contribute to real applications in databases as well as in management science/operations research.     

QR methods and error analysis for computing Lyapunov and Sacker�CSell spectral intervals for linear differential-algebraic equations

In this paper, we propose and investigate numerical methods based on QR factorization for computing all or some Lyapunov or Sacker?CSell spectral intervals for linear differential-algebraic equations. Furthermore, a perturbation and error analysis for these methods is presented. We investigate how errors in the data and in the numerical integration affect the accuracy of the approximate spectral intervals. Although we need to integrate numerically some differential-algebraic systems on usually very long time-intervals, under certain assumptions, it is shown that the error of the computed spectral intervals can be controlled by the local error of numerical integration and the error in solving the algebraic constraint. Some numerical examples are presented to illustrate the theoretical results.     

A survey of quantum-like approaches to decision making and cognition

There has always been a steady interest in how humans make decisions amongst researchers from various fields. Based on this interest, many approaches such as rational choice theory or expected utility hypothesis have been proposed. Although these approaches provide a suitable ground for modeling the decision making process of humans, they are unable to explain the corresponding irrationalities and existing paradoxes and fallacies. Recently, a new formulation of decision theory that can correctly describe these paradoxes and possibly provide a unified and general theory of decision making has been proposed. This new formulation is founded based on the application of the mathematical structure of quantum theory to the fields of human decision making and cognition. It is shown that by applying these quantum-like models, one can better describe the uncertainty, ambiguity, emotions and risks involved in the human decision making process. Even in computational environments, an agent that follows the correct patterns of human decision making will have a better functionality in performing its role as a proxy for a real user. In this paper, we present a comprehensive survey of the researches and the corresponding recent developments. Finally, the benefits of leveraging the quantum-like modeling approaches in computational domains and the existing challenges and limitations currently facing the field are discussed.     

On the process of gaining language as a resource in mathematics education

In this paper, we expand our prior work on mathematics education in contexts of language diversity by elaborating on the three perspectives on language described by Ruiz (NABE J 8(2):15–34, 1984): language-as-right, language-as-resource, and language-as-problem. We illustrate our arguments with data taken from research contexts in Catalonia-Spain and South Africa. In these two parts of the world, the language policy in education has long been an issue, with a monolingual orientation that values one language (i.e., Catalan in Catalonia and English in South Africa) over others. Throughout the introduction of specific examples of policy documents, classroom practices, and participants’ reports, our main point is that the right of using the students’ languages makes sense because it is itself more than an intrinsic human right; it is an option that potentially benefits the creation of mathematics learning opportunities. Especially for the instances of classroom practices, our examples can be considered as representative in that they point to a common situation in our data: despite the fact of the language of learning and teaching being fixed, there is room for the learners and the teacher to take or react to a decision on what language to use, with whom, and how in concrete moments of the interaction. However, on the basis of our studies and drawing on the literature in mathematics education and language diversity, we argue that language rights are not sufficiently connected to language as a pedagogical resource. The enactment of these rights is still contributing in many ways to the social and political construction of problems concerning the role of certain languages in classroom interaction. We conclude the paper by discussing some possibilities for framing language as a resource that provide effective support to all students’ learning of mathematics.