Introduction

Summary

Today, algebraic modeling languages are widely accepted as the best way to represent and solve mathematical programming problems. Their main distinguishing features are the use of relational algebra and the ability to provide partial derivatives on multidimensional, very large and sparse structures. In this chapter we will briefly describe some of the origins of GAMS and provide background information that shaped early design decisions.

The Origins of GAMS

The initial Research and Development of GAMS was funded by the International Bank for Reconstructionand Development, usually referred to as The World Bank. Since 1987, further Research and Development has been funded by GAMS Development Corporation. GAMS was developed in close cooperation of mathematical economists who were and still are an important group of GAMS users. The synergy between economics, computer science and operations research was the most important success factor in the development of the system. Mathematical Programming and economics theory are closely intertwined. The Nobel Prize in Economics awarded to Leonid Kantorovich and Tjalling Koopmans in 1975 for their “contribution to the theory of optimal allocation of resources” was really a prize in mathematical programming. Other Nobel laureates like Kenneth Arrow in 1972, Wassily Leontief in 1973, and Harry Markowitz in 1990 are well known names in math programming. Another early example of this synergy is the use of LP in refining operations, which was started by Alan Manne, an economist, with his book on Scheduling of Petroleum Refinery Operations in 1956.

The origins of linear programming algorithms all go back to George Dantzig’s early work in the 1940s and 1950s. Computing technology and algorithmic theory had developed at a rapid pace. Thirty years later, we could solve problems of practical size and complexity that allowed us to test of the economic theory on real life problems. The research agenda at the World Bank in the 1970s and 1980s created the perfect environment to bring different disciplines together to apply mathematical programming to research and operational questions in Economic Development.

Background and Motivation

From the very beginning, the driving force behind the development of the General Algebraic Modeling System (GAMS) has been the users of mathematical programming who believed in optimization as a the powerful and elegant framework for solving real life problems in the sciences and engineering. At the same time, these users were frustrated with the high cost, skill requirements, and overall low reliability of applying optimization tools. Most of our initiatives and support for new development came from the worlds of economics, finance, and chemical engineering. These disciplines find it natural to view and understand the world and its behavior as a mathematical program.

GAMS’s impetus for development arose out of the frustrating experiences of a large economic modeling group at the World Bank. In hindsight, one may call it a historical accident that in the 1970s mathematical economists and statisticians were assembled to address problems of development. They used the best techniques available at the time to solve multisectoral economy-wide models and large simulation and optimization models in agriculture, steel, fertilizer, power, water use, and other sectors. Although the group produced impressive research, initial successes were difficult to reproduce outside their well functioning research environment. The existing techniques to construct, manipulate, and solve such models required several manual, time-consuming, and error-prone translations into the different, problem-specific representations required by each solution method. During seminar presentations, modelers had to defend the existing versions of their models, sometimes quite irrationally, because the time and money needed to make proposed changes were prohibitive. Their models just could not be moved to other environments, because special programming knowledge was needed, and data formats and solution methods were not portable.

The idea of an algebraic approach to represent, manipulate, and solve largescale mathematical models fused old and new paradigms into a consistent and computationally tractable system. Using matrix generators (see appendix GAMS versus Fortran Matrix Generators) for linear programs taught us the importance of naming rows and columns in a consistent manner. The connection to the emerging relational data model became evident. Painful experience using traditional programming languages to manage those name spaces naturally lead one to think in terms of sets and tuples, and this led to the relational data model. Combining multidimensional algebraic notation with the relational data model was the obvious answer. Compiler writing techniques were by now widespread, and languages like GAMS could be implemented relatively quickly. However, translating this rigorous mathematical representation into the algorithm specific format required the computation of partial derivatives on very large systems. In the 1970s, TRW developed a system called PROSE that took the ideas of chemical engineers to compute point derivatives that were exact derivatives at a given point, and to embed them in a consistent, fortran-style calculus modeling language. The resulting system allowed the user to use automatically generated exact first and second order derivatives. This was a pioneering system and an important demonstration of a concept. However, in our opinion PROSE had a number of shortcomings: it could not handle large systems, problem representation was tied to an array-type data structure that required address calculations, and the system did not provide access to state-of-the art solution methods. From linear programming, we learned that exploitation of sparsity was the key to solve large problems. Thus, the final piece of the puzzle was the use of sparse data structures.

With all pieces in place, all we had to do was adopt the techniques to fit into one consistent framework and make it work for large problems

Design Goals and Changing Focus

The original and still valid goal is to improve the model builder’s productivity, reduce costs, and improve reliability and overall credibility of the modeling process. To achieve this, we established the following key principles to guide the GAMS development:

  • The problem representation is independent of the solution method.
  • The data representation follows the relational data model.
  • The problem and data representations are independent of computing platforms.
  • The problem and data representations are independent of user interfaces.
  • Optimization methods will fail, and systems have to be designed to be fail-safe.

Another way to express these principles is to think in terms of layers of representations and capabilities that have clearly defined interfaces and functions. The oldest and most basic layer is the solver layer or implementation of a specific algorithm. Above the solver is the model layer, expressed in an algebraic modeling language. The modeling layer translates the mathematical representation into a computational structure required by a specific solution method and provides various services such as function and derivative evaluations and error recovery. Above the modeling layer is the application or domain layer, which is highly context sensitive and has knowledge about the problem to be solved and the kind of user interacting with the system.

The representation of the model in GAMS is in a form that can be easily read by humans and by machines. This means that the GAMS program itself is the documentation of the model, and that the separate description required in the past (which was a burden to maintain, and which was seldom up-to-date) is no longer needed. Moreover, the design of GAMS incorporates the following features that specifically address the user's documentation needs:

  • A GAMS model representation is concise, and makes full use of the elegance of the mathematical representation.
  • All data transformations are specified concisely and algebraically. This means that all data can be entered in their most elemental form and that all transformations made in constructing the model and in reporting are available for inspection.
  • Explanatory text can be made part of the definition of all symbols and is reproduced whenever associated values are displayed.
  • All information needed to understand the model is in one document.

Of course some discipline is needed to take full advantage of these design features, but the aim is to make models more accessible, more understandable, more verifiable, and hence more credible.

It is instructive to put the development of modeling systems into some historic perspective and see how the focus and technical constraints have changed in the last 30 years. We can observe three major phases that shift the emphasis from computational issues to modeling issues and finally the application or the real problems. Each phase defined one of the main system layers discussed above. The dominant constraints in the first phase were the computational limits of our algorithms. Problem representation had to abide by algorithmic convenience, centralized expert groups managed large, expensive and long lasting projects and end users were effectively left out. The second phase has the model in focus. Applications are limited by modeling skill, project groups are much smaller and decentralized, the computational cost are low and the users are involved in the design of the application. Applications are designed to be independent of computing platforms and frequently operate in a client-server environment.

We believe that we are entering a third phase which has the application as its focus and the optimization model is just one of many analytic tools that help making better decisions. The users are often completely unaware of any optimization model or use a mental model that is different from the actual model to solved by optimization techniques. User interfaces are build with off-theshelf components and frequently change to adjust to evolving environments and new computing technologies. As with databases, modeling components have a much longer life than user interfaces. We have observed cases where the model has remained basically unchanged over many years, whereas the computing environments and user interfaces have changed several times. The solvers used to solve the models have changed, the computing platforms have changed, the user interfaces have changed and the overall performance of the model has changed without any change in the model representation.