Program control of operating systems

The traditional job control language becomes superfluous if the existing programming languages are extended slightly. Such extensions also allow drivers and operating systems to be programmed entirely in high level languages. Ultimately, we may see machine independent operating systems. A framework is presented for an extendable operating system which allows a simple, uniform implementation of these language extensions.     

Aspects of compact programs and directly executed languages

Some aspects of directly executed languages (DEL's) are described. Principles for the design of a DEL are given and in particular a survey of principles used to design a DEL yielding very compact programs. The idea of a refined display to get small address fields in DEL's for blockstructured languages is introduced. A comparison between IBM S/360 machine language and a specific DEL for the machine-oriented higher level language MARY gives quantitative results for the applicability of the techniques described.     

Control extension in a recursive language

Techniques for language extension are of interest today as a means for language design experimentation without language proliferation. Attention thus far has been focused on methods for data structure definition and manipulation. The problem of program control extensibility has been recognized by workers in the field but less thoroughly treated. This paper outlines an approach to control extensibility applicable on the source language level to appropriate programming languages.Using the recursive language LISP as an example, extensions are defined that provide call by name function parameters, generator functions (as in IPL-V), non-deterministic functions, and general coroutines. LISP facilitates this exercise by features including dynamic scope of variables, source programs manipulatable as data, simplicity of program state representations, and control over context of function evaluation. The results of this paper suggest criteria for evaluating base languages in extensible programming systems, as well as a possible insight into formal program analysis.     

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)     

Analyzing Mathematical Programs Using MProbe

Just as modern general-purpose programming languages (e.g., C++, Java) are supported by a suite of tools (debuggers, profilers, etc.), mathematical programming languages need supporting tools. MProbe is an example of a suite of tools supporting a mathematical programming language, in this case AMPL. MProbe includes tools for empirically estimating the shape of nonlinear functions of many variables, nonlinearly-constrained region shape, the effect of the objective shape on the ability to find a global optimum, tools for estimating the effectiveness of constraints and for navigating through the model, among others.     

Interval-valued preference structures

Different languages that are offered to model vague preferences are reviewed and an interval-valued language is proposed to resolve a particular difficulty encountered with other languages. It is shown that interval-valued languages are well defined for De Morgan triples constructed by continuous triangular norms, conorms and a strong negation function. A new transitivity condition for vague preferences is suggested and its relationships to known transitivity conditions are established. A complete characterization of interval-valued preference structures is also provided.     

Programming Languages,Microcomputers and O.R.

The relationship between O.R. and computer programming is considered. The programming languages involved, and their respective roles, are reviewed. Criteria for assessing a programming language for O.R. are discussed, particularly in view of the increasing use of microcomputers within O.R.     

Modeling languages and Condor: metacomputing for optimization

A generic framework is postulated for utilizing the computational resources provided by a metacomputer to concurrently solve a large number of optimization problems generated by a modeling language. An example of the framework using the Condor resource manager and the AMPL and GAMS modeling languages is provided. A mixed integer programming formulation of a feature selection problem from machine learning is used to test the mechanism developed. Due to this application’s computational requirements, the ability to perform optimizations in parallel is necessary in order to obtain results within a reasonable amount of time. Details about the simple and easy to use tool and implementation are presented so that other modelers with applications generating many independent mathematical programs can take advantage of it to significantly reduce solution times. Received: October 28, 1998 / Accepted: December 01, 1999?Published online June 8, 2000     

The Dynamic Geometrisation of Computer Programming

The goal of this paper is to explore dynamic geometry environments (DGE) as a type of computer programming language. Using projects created by secondary students in one particular DGE, we analyse the extent to which the various aspects of computational thinking—including both ways of doing things and particular concepts—were evident in their work, drawing specifically on frameworks for computational thinking that are designed for the purpose of mathematics education. We show how many of the practices associated with the use of propositional programming languages also feature in the more spatial and temporal register of the geometric ‘language’ of DGEs.     

Index to Volume 1 (1992)

Abstract

Since its introduction, APL has frequently been touted as an ideal programming language for statistical applications. Among the attractive features of APL for statistics are its extensibility, the presence of primitives for operations such as sorting, matrix inversion, and arranging data, and powerful facilities for handling matrices and other arrays. Newer programming languages incorporating some of the features of APL—notably New S and XLisp-Stat—have been designed specifically for statistical applications. Is there still a niche for APL?

We believe that APL2 continues to offer some significant benefits for statistical computation, including user-defined operators, nested arrays, and convenient implementation of arrays of any dimension. We use these characteristics of the language in the course of designing an extensible computing environment for data analysis and programming based on APL2 that incorporates some of the features of modern statistical programming languages, such as data objects, symbolic model specification, missing-data handling, and automatic search of a path of files. The system, which includes interactive statistical graphics, general linear models, and robust estimation methods, has been implemented using IBM's APL2 for PC-compatibles—both the standard version of this system and the freeware TryAPL2. The latter provides students with a free environment for modern data analysis and one in which they can explore the design of statistical software.     

Languages for Statistics and Data Analysis

Abstract

Languages for data analysis and statistics must be able to cover the entire spectrum from improvisation and fast prototyping to the implementation of streamlined, specialized systems for routine analyses. Such languages must not only be interactive but also programmable, and the distinctions between language, operating system, and user interface get blurred. The issues are discussed in the context of natural and computer languages, and of the different types of user interfaces (menu, command language, batch). It is argued that while such languages must have a completely general computing language kernel, they will contain surprisingly few items specific to data analysis—the latter items more properly belong to the “literature” (i.e., the programs) written in the language.     

Understanding computer understanding of narrative text

This paper surveys recent successes in getting computers to understand human language by imbuing them with cultural knowledge and everyday common sense. The paper begins with a discussion of the centrality of language comprehension in giving computing machines their universality. Next, differences between formal programming languages currently understood by machines and properties of natural languages are discussed. The central problems of representing thought, encoding cultural knowledge, and generating inferences and cultural expectations are addressed within the context of the development of a number of computer programs dealing with narrative text. Finally, the paper ends with a discussion of the author's own computer program, BORIS, which extends the theory of language understanding and narrative comprehension in a number of directions. In particular, BORIS implements a theory of knowledge interactions, multiple perspectives, emotional reactions, parsing integrated with episodic memory, expectation failures, planning errors, and abstract narrative themes.     

A logic language for combinatorial optimization

CHIP (Constraint Handling In Prolog) is a new logic programming language combining the declarative aspect of logic programming for stating search problems with the efficiency of constraint handling techniques for solving them. CHIP has been applied to many real-life problems in Operations Research and hardware design with an efficiency comparable to specific programs written in procedural languages. The main advantage of CHIP is the short development time of the programs and their great modifiability and extensibility. In this paper, we discuss the application of the finite domain part of CHIP to the solving of discrete combinatorial problems occurring in Operations Research. The basic mechanisms underlying CHIP are explained through simple examples. Solutions in CHIP of several real-life problems (e.g., cutting stock, warehouses location problems) are presented and compared with usual approaches, showing the versatility and the interest of the approach.     

A structure-conveying modelling language for mathematical and stochastic programming

We present a structure-conveying algebraic modelling language for mathematical programming. The proposed language extends AMPL with object-oriented features that allows the user to construct models from sub-models, and is implemented as a combination of pre- and post-processing phases for AMPL. Unlike traditional modelling languages, the new approach does not scramble the block structure of the problem, and thus it enables the passing of this structure on to the solver. Interior point solvers that exploit block linear algebra and decomposition-based solvers can therefore directly take advantage of the problem’s structure. The language contains features to conveniently model stochastic programming problems, although it is designed with a much broader application spectrum.     

A basic course on compiler principles

An attempt to devise a methodology of compiler design is described, and an outline is given for a possible course on this subject. The theoretical basis is formed by the concepts of phrase-structure language, finite-state- and stack-acceptor, and transducer. As their extension capable of processing context-dependent elements of languages, a so-called Table-Transducer is postulated, and it serves as the core-algorithm upon which compilers are based. The developed theory and method of compiler construction is applied to an example of a simple programming language.     

Logic and Natural Selection

Is logic, feasibly, a product of natural selection? In this paper we treat this question as dependent upon the prior question of where logic is founded. After excluding other possibilities, we conclude that logic resides in our language, in the shape of inferential rules governing the logical vocabulary of the language. This means that knowledge of (the laws of) logic is inseparable from the possession of the logical constants they govern. In this sense, logic may be seen as a product of natural selection: the emergence of logic requires the development of creatures who can wield structured languages of a specific complexity, and who are capable of putting the languages to use within specific discursive practices.     

A fuzzy language

With the aim of designing and implementing programming languages that take into account the fuzzy paradigm we will modify the classical lambda calculus by adding a degree to each term and by redefining the b-reduction. Thus, for the new calculus to verify the Church–Rosser property, the degree computed with can be made through a function that is a t-norm or an s-conorm. With this new tool we design a nondeterminist language that satisfies fuzzy data programming requirements, and an example of its behaviour is shown.     

The efficient solution of large-scale linear programming problems—some algorithmic techniques and computational results

First, this paper presents the results of experiments with algorithmic techniques for efficiently solving medium and large scale linear and mixed integer programming problems. The techniques presented here are either original or recent.The solution of a great number of problems has shown that efficient problem solving requires automatic adaptation of algorithmic techniques upon problem characteristics. We show when a given technique should be used for a particular problem.The last part of this paper describes an attempt to provide a powerful mathematical programming language, allowing an easy programming of specific studies on medium-size models such as the recursive use of LP or the build-up of algorithms based on the simplex method.All these features have been implemented in the IBM Mathematical Programming System, MPSX/370, and its feature MIP/370. Extensive numerical results and comparisons on real-life problems are provided and commented upon.Presented at the IXth International Symposium on Mathematical Programming in Budapest (1976).     

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.