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Interfaces Between Modeling Systems and Solution Algorithms

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Mathematical Models for Decision Support

Part of the book series: NATO ASI Series ((NATO ASI F,volume 48))

Abstract

Until recently, a modeler needed to know many details about solution algorithms, in the following referred to a ‘solvers’, especially about cumbersome input and output formats, and model building was consequently rather skill intensive and expensive. Although computer times often are quoted as a measure of the effort involved in a modeling exercise, the manpower to implement and debug a model is usually much more costly. Better algorithms has for some time not been the key to cheaper modeling, but rather systems that could manage the model building process. As a result of this demand, several so called modeling systems have emerged over the last years, see e.g. Bisschop and Meeraus [3], Fourer [8,9], and Geoffrion [10].

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References

  1. Ahlfeld, D., R. Dembo, J.M. Mulvey, and S.A. Zenios: Nonlinear Programming on Generalizes Networks, report EES-85-7, Department of Civil Engineering, Princeton University, Princeton, 1985.

    Google Scholar 

  2. Bisschop, J. and A. Brooke: How to place some of your own linear solvers in GAMS using the PC, mimeo, Development Research Department, 1987.

    Google Scholar 

  3. Bisschop, J. and A. Meeraus: On the Development of a General Algebraic Modeling System in a Strategic Planning Environment, Mathematical Programming Study, vol. 20, p. 1–29, 1982.

    Google Scholar 

  4. Brooke, A., A. Drud, and A. Meeraus: High Level Modeling Systems and Nonlinear Programming, in Nonlinear Optimization 1984, P.T. Boggs, R.H. Byrd, and R. E. Schnabell (eds.), P. 178–198, SIAM, Philadelphia, 1985.

    Google Scholar 

  5. Drud, A.: Interfacing new Solution Algorithms with Existing Modeling Systems, Journal of Economic Dynamics and Control, vol. 5, p. 131–149, 1983.

    Google Scholar 

  6. Drud, A.: Alternative Model Formulations in Nonlinear Programming — Some Disastrous Results, Operations Research, VOl 33, p. 218–222, 1985.

    Article  MATH  MathSciNet  Google Scholar 

  7. Drud, A.: CONOPT — A GRG Code for Large Sparse Dynamic Nonlinear Optimization Problems, Mathematical Programming, vol 31, p. 153–191, 1985.

    Article  MATH  MathSciNet  Google Scholar 

  8. Fourer, R.: Modeling Languages Versus Matrix Generators for Linear Programming, ACM Transactions on Mathematical Software, vol. 9, p. 143–183, 1983.

    Article  Google Scholar 

  9. Fourer, R., D.M. Gay, and B.W. Kernighan: AMPL: A Mathematical Programming Language, AT&T Bell Labs Working Paper, 1986.

    Google Scholar 

  10. Geoffrion, A.: An Introduction to Structured Modeling, Working Paper no 338, Western Management Sciences Institute, UCLA, Los Angeles, 1986.

    Google Scholar 

  11. Gill, E.P., W. Murray, M.A. Saunders, and M.H. Wright: User’s Guide for SOL/NPSOL: a FORTRAN Package for Nonlinear Programming, Department of Operations Research, Stanford University, 1984.

    Google Scholar 

  12. Kendrick, D. and A. Meeraus: GAMS — An Introduction, mimeo, Development Research Department, World Bank, 1987.

    Google Scholar 

  13. Lasdon, L.S., A.D. Waren, A. Jain, and M. Ratner: Design and Testing of a Generalized Reduced Gradient Code for Nonlinear Programming, ACM Transactions on Mathematical Software, vol. 4, p. 34–50, 1978.

    Article  MATH  Google Scholar 

  14. Marsten, R.E.: The Design of the XMP Linear Programming Library, ACM Transactions on Mathematical Software, vol 7, p. 481–497, 1981.

    Article  Google Scholar 

  15. Murtagh, B.A. and M.A. Saunders: “A Projected Lagrangian Algorithm and its Implementation for Sparse Nonlinear Constraints”, Mathematical Programming Study, vol 16, p. 84–117, 1982.

    Article  MATH  MathSciNet  Google Scholar 

  16. APEX IV Reference Manual, CDC Manual 76070000.

    Google Scholar 

  17. Mathematical Programming System — Extended (MPSX) and Generalized Upper Bounding (GUB) Program Description, IBM manual SH20-0968.

    Google Scholar 

  18. Sciconic, Product of Scicon Ltd, UK.

    Google Scholar 

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© 1988 Springer-Verlag Berlin Heidelberg

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Drud, A.S. (1988). Interfaces Between Modeling Systems and Solution Algorithms. In: Mitra, G., Greenberg, H.J., Lootsma, F.A., Rijkaert, M.J., Zimmermann, H.J. (eds) Mathematical Models for Decision Support. NATO ASI Series, vol 48. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83555-1_10

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  • DOI: https://doi.org/10.1007/978-3-642-83555-1_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-83557-5

  • Online ISBN: 978-3-642-83555-1

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