Encyclopedia of Operations Research and Management Science

2001 Edition
| Editors: Saul I. Gass, Carl M. Harris

Factorable programming

Reference work entry
DOI: https://doi.org/10.1007/1-4020-0611-X_328
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Factorable programming problems are mathematical programming problems of the form
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References

  1. [1]
    DeSilva, A. and McCormick, G.P. (1978). “Sensitivity Analysis in Nonlinear Programming Using Factorable Symbolic Input,” Technical Report T-365, The George Washington University, Institute for Management Science and Engineering, Washington, DC.Google Scholar
  2. [2]
    Emami, G. (1978). “Evaluating Strategies for Newton's Method Using a Numerically Stable Generalized Inverse Algorithm,” Dissertation, Department of Operations Research, George Washington University, Washington, DC.Google Scholar
  3. [3]
    Falk, J.E. (1973). “Global Solutions of Signomial Problems,” Technical report T-274, George Washington University, Department of Operations Research, Washington, DC.Google Scholar
  4. [4]
    Falk, J.E. and Soland, R.M. (1969). “An Algorithm for Separable Nonconvex Programming Problems,” Management Science, 15, 550–569.Google Scholar
  5. [5]
    Fiacco, A.V. and McCormick, G.P. (1968). Nonlinear Programming: Sequential Unconstrained Minimization Techniques, John Wiley, New York.Google Scholar
  6. [6]
    Ghaemi, A. and McCormick, G.P. (1979). “Factorable Symbolic SUMT: What Is It? How Is It Used?,” Technical Report No. T-402, Institute for Management Science and Engineering, George Washington University, Washington, DC.Google Scholar
  7. [7]
    Ghotb, F. (1980). “Evaluating Strategies for Newton's Method for Linearly Constrained Optimization Problems,” Dissertation, Department of Operations Research, George Washington University, Washington, DC.Google Scholar
  8. [8]
    Hoffman, K.L. (1975). “NUGLOBAL-User Guide,” Technical Report TM-64866, Department of Operations Research, George Washington University, Washington, DC.Google Scholar
  9. [9]
    Jackson, R.H. F. and McCormick, G.P. (1986). “The Polyadic Structure of Factorable Function Tensors with Applications to High-order Minimization Techniques,” JOTA, 51, 63–94.Google Scholar
  10. [10]
    Jackson, R.H. F. and McCormick, G.P. (1988). “Second-order Sensitivity Analysis in Factorable Programming: Theory and Applications,” Mathematical Programming, 41, 1–27.Google Scholar
  11. [11]
    Jackson, R.H. F., McCormick, G.P., and Sofer, A. (1989). “FACTUNC, A User-friendly System for Optimization,” Technical Report NISTIR 89-4159, National Institute of Standards and Technology, Gaithersburg, Maryland.Google Scholar
  12. [12]
    Kedem, G. (1980). “Automatic Differentiation of Computer Programs,” ACM Transactions on Mathematical Software, 6, 150–165.Google Scholar
  13. [13]
    Leaver, S.G. (1984). “Computing Global Maximum Likelihood Parameter Estimates for Product Models for Frequency Tables Involving Indirect Observation,” Dissertation, The George Washington University, Department of Operations Research, Washington, DC.Google Scholar
  14. [14]
    McCormick, G.P. (1974). “A Minimanual for Use of the SUMT Computer Program and the Factorable Programming Language,” Technical Report SOL 74-15, Department of Operations Research, Stanford University, Stanford, California.Google Scholar
  15. [15]
    McCormick, G.P. (1976). “Computability of Global Solutions to Factorable Nonconvex Programs: Part I–Convex Underestimating Problems,” Mathematical Programming, 10, 147–145.Google Scholar
  16. [16]
    McCormick, G.P. (1983). Nonlinear Programming: Theory, Algorithms and Applications, John Wiley, New York.Google Scholar
  17. [17]
    McCormick, G.P. (1985). “Global Solutions to Factorable Nonlinear Optimization Problems Using Separable Programming Techniques,” Technical Report NBSIR 85-3206, National Bureau of Standards, Gaithersburg, Maryland.Google Scholar
  18. [18]
    Miele, A. and Gonzalez, S. (1978). “On the Comparative Evaluation of Algorithms for Mathematical Programming Problems,” Nonlinear Programming, 3, edited by O.L. Mangasarian et al., Academic Press, New York, 337–359.Google Scholar
  19. [19]
    Mylander, W.C., Holmes, R., and McCormick, G.P. (1971). “A Guide to SUMT-Version 4: The Computer Program Implementing the Sequential Unconstrained Minimization Technique for Nonlinear Programming,” Technical Report RAC-P-63, Research Analysis Corporation, McLean, Virginia.Google Scholar
  20. [20]
    Pugh, R.E. (1972). “A Language for Nonlinear Programming Problems,” Mathematical Programming, 2, 176–206.Google Scholar
  21. [21]
    Rall, L.B. (1980). “Applications of Software for Automatic Differentiation in Numerical Computations,” Computing, Supplement, 2, 141–156.Google Scholar
  22. [22]
    Reiter, A. and Gray, J.H. (1967). “Compiler for Differentiable Expressions (CODEX) for the CDC 3600,” MRC Technical Report No. 791, University of Wisconsin, Madison, Wisconsin.Google Scholar
  23. [23]
    Shayan, M.E. (1978). “A Methodology for Comparing Algorithms and a Method for Computing mth Order Directional Derivatives Based on Factorable Programming,” Dissertation, Department of Operations Research, George Washington University, Washington, DC.Google Scholar
  24. [24]
    Sofer, A. (1983). “Computationally Efficient Techniques for Generalized Inversion,” Dissertation, Department of Operations Research, The George Washington University, Washington, DC.Google Scholar
  25. [25]
    Warner, D.D. (1975). “A Partial Derivative Generator,” Computing Science Technical Report No. 28, Bell Telephone Laboratories, Murray Hill, New Jersey.Google Scholar
  26. [26]
    Wengert, R.E. (1964). “A Simple Automatic Derivative Evaluation Program,” Communications of the ACM, 7, 463–464.Google Scholar

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© Kluwer Academic Publishers 2001

Authors and Affiliations

  1. 1.National Institute of Standards and TechnologyGaithersburgUSA