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Ragnar Frisch and interior-point methods

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Abstract

The distinguished econometrician Ragnar Frisch (1895–1973) also played an important role in optimization theory. In fact, he was a pioneer of interior-point methods. This note reconsiders his contribution, relating it to history and modern developments.

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Notes

  1. Later [3] introduced the inverse barrier function. He referred to Frisch’s [21] multiplex method, also evoked by [48].

  2. For an exception, see [1] who already knew and appreciated the logarithmic potential and multiplex methods of [17, 21]. See also [48].

  3. To wit, there are time gaps between [16], the 1968 classic of Fiacco and McCormick, and the [35, 43] papers on pathways in linear programming.

  4. Many optimizers cite Frisch with an extra initial letter M—probably transmitted from the many papers or presentations in France of Monsieur Frisch [11] somewhat mysteriously cited Frisch, a misnomer which has propagated through citation trees.

  5. Other studies of interior-point methods, from that period, include [7] and [25].

  6. See also [19, 20].

  7. After Frisch’s time, optimization has grown impressively, in theory and applications. But alas, it is now less emphasized in economists’ training.

  8. See also [6].

  9. Frisch, also a trained goldsmith and passionate bee-keeper (1952), was editor of Econometrica where the 1949 paper of Dantzig appeared.

  10. http://www.sv.uio.no/econ/om/tall-og-fakta/nobelprivinnere/ragnar-frisch/memoranda-and-others/.

  11. The discrete nature of the simplex method has taken some hold. However, [5], Sect. 7.2, pages 156-160] also stressed its continuous nature, seen as a (reduced) gradient procedure.

  12. For nice presentations of the Khachiyan and Karmarkar methods, as well as for historical remarks, see [41].

  13. The logarithm also enters stochastic programming. There, constraints often come as reliability requirements-say, in the form \(\Pr \left\{ c(x)\ge \underline{c}\right\} \ge p>0\) where \(\underline{c}\) is a random vector. Then, it’s very convenient if that vector has log-concave distribution, to the effect that \(\ln \Pr \left\{ c(x)\ge \underline{c}\right\} \) becomes concave; see [39].

  14. Henceforth, to assist economic intuition, the symbol \(*\) signals a price transform or variable.

  15. Sonnevend [43] first proposed these specially for linear programs.

  16. Conf. [32, 33, 36]. For implementation see Gondzio and Terlaky (1996).

  17. The editor of a conservative Oslo magazine felt compelled to inform Frisch, who had socialist inclinations, that there were competing concepts of freedom.

  18. In essence, this is the pivot step—or Jordan exchange—that drives the simplex algorithm; see [45] or [9].

References

  1. Arrow, K.J.: The work of Ragnar Frisch, econometrician. Econometrica 28(2), 175–192 (1960)

    Article  MathSciNet  Google Scholar 

  2. Bjerkholt, O.: Some unresolved problems of mathematical programming. In: Basu, D. (ed.) Economic models–methods, theory and applications, pp. 3–19. World Scientific, Singapore (2009)

  3. Carroll, C.W.: The created response surface technique for optimizing nonlinear restrained systems. Oper. Res. 9(2), 169–185 (1961)

    Article  MATH  Google Scholar 

  4. Dantzig, G.B.: Programming of interdependent activitities: II mathematical model. Econometrica 17, 200–211 (1949)

    Article  MathSciNet  Google Scholar 

  5. Dantzig, G.B.: Linear programming and extensions. Princeton University Press, Princeton (1963)

    MATH  Google Scholar 

  6. Dantzig, G.B.: The diet problem. Interfaces 20(4), 43–47 (1990)

    Article  Google Scholar 

  7. Dikin, I.: Iterative solution of problems of linear and quadratic programming. Sov. Math. Dokl. 8, 674–675 (1967)

    MATH  Google Scholar 

  8. Dorfman, R., Samuelson, P.A., Solow, R.M.: Linear programming and economic analysis. McGraw-Hill, New York (1958)

    MATH  Google Scholar 

  9. Ferris, M., Mangasarian, O. L., Wright, S. J.: Linear programming with MATLAB, MPS-SIAM series on optimization (2007)

  10. Fiacco, A.V., McCormick, G.P.: Computational algorithm for the sequential unconstrained minimization technique for nonlinear programming. Manag. Sci. 10(4), 601–617 (1964)

    Article  Google Scholar 

  11. Fiacco, A.V., McCormick, G.P.: Nonlinear programming, sequential unconstrained minimization techniques, Research Analysis Corporation (1968) and SIAM (1990)

  12. Frisch, R.: Problems and methods of econometrics. In: Bjerkholt, O., Dupont-Kieffer, A. (eds.) The Poincaré lectures 1933. Routledge, London (2009)

    Google Scholar 

  13. Frisch, R.: Circulation planning: proposal for a national organization of a commodity and service exchange. Econometrica 2, 258–336 and 422–435 (1934)

  14. Frisch, R.: Introduction. In: Wold, K. (ed.) Kosthold og levestandard, en økonomisk undersøkelse (Nutrition and standard of living, an economic investigation), pp. 1–13. Fabritius og Sønners Forlag, Oslo (1941)

  15. Frisch, R.: Das Ausleseproblem in der Bienenzüchtung. Zeitschrift für Bienenforschung 1, 7 (1952)

  16. Frisch, R.: Principles of linear programming—with particular reference to the double gradient form of the logarithmic potential method. Memorandum from the Institute of Economics, University of Oslo, Oslo (1954)

    Google Scholar 

  17. Frisch, R.: The logarithmic potential method of convex programming with particular application to the dynamics of planning for national development. Memorandum from the Institute of Economics, University of Oslo, Oslo (1955)

    Google Scholar 

  18. Frisch, R.: La résolution des problèmes de programmes linéaires par la méthode du potential logarithmique, Cahiers du Séminaire D’Économetrie, No 4—Programme linéaire—Agrégation et nombre indices 7–23 (1956a)

  19. Frisch, R.: Formulazione di un piano di sviluppo nazional come problema di programmazione convessa, L’industria (1956b)

  20. Frisch, R.: Macroeconomics and linear programming. In: 25 Economic Essays in Honour of Erik Lindahl, Ekonomisk Tidskrift, pp. 38–67 Stockholm (1956c).

  21. Frisch, R.: The multiplex method for linear programming. Sankhya 18(3&4), 329–362 (1957)

    MathSciNet  MATH  Google Scholar 

  22. Gale, D.: The theory of linear economic models. The University of Chicago Press, Chicago (1960)

    Google Scholar 

  23. Gondzio, J., Terlaky, T.: A computational view of interior-point methods for linear programming. In: Beasley, J. (ed.) Advances in linear and integer programming, Ch. 3, pp. 103–144. Oxford University Press, Oxford (1996)

    Google Scholar 

  24. Hitchcock, F.L.: The distribution of a product from several sources to numerous localities. J. Math. Phys. 20, 224–230 (1941)

    MathSciNet  Google Scholar 

  25. Huard, P.: Resolution of mathematical programming with nonlinear constraints by the method of centers. North-Holland, Amsterdam (1967)

    Google Scholar 

  26. Kantorovich, L.V.: A new method of solving some classes of extremal problems. Dokl. Akad Sci USSR 28, 211–214 (1940)

    Google Scholar 

  27. Karmarkar, N.: A new polynomial-time algorithm for linear programming. Combinatorica 4(4), 373–395 (1984)

    Article  MathSciNet  Google Scholar 

  28. Khachiyan, L.G.: A polynomial algorithm for linear programming. Sov. Math. Dokl. 20, 191–194 (1979)

    MATH  Google Scholar 

  29. Klee, V., Minty, G.J.: How good is the simplex algorithm, in inequalities III, pp. 159–172. Academic Press, New York (1972)

    Google Scholar 

  30. Koopmans, T.C. (ed.): Activity analysis of production and allocation, monograph 13 Cowles commision for research in economics. Wiley, Hoboken (1951)

    Google Scholar 

  31. Leontief, W.W.: Input-output economics, vol. 2. Oxford University Press, New York (1986)

    Google Scholar 

  32. Lootsma, F.A.: Hessian matrices of penalty functions for solving constrained optimization problems. Philips Res. Rep. 24, 322–331 (1969)

    MathSciNet  MATH  Google Scholar 

  33. Luenberger, D.: Introduction to linear and nonlinear programming. Addison-Wesley, Boston (1984)

    Google Scholar 

  34. Marsten, R., Subramanian, R., Saltzman, M., Lustig, I., Shanno, D.: Interior point methods for linear programming: just call Newton, Lagrange, and Fiacco and McCormick. Interfaces 20(4), 105–116 (1990)

  35. Meggido, N.: Pathways to the optimal set in linear programming. In: Meggido, N. (ed.) Progress in mathematical programming: interior-point and related methods, Chap 8, pp. 131–158. Springer, Berlin (1989)

    Chapter  Google Scholar 

  36. Murray, W.: Analytical expressions for the eigenvalues and eigenvectors of the Hessian matrices of barrier and penalty functions. J. Optim. Theory Appl. 7, 189–196 (1971)

    Article  MATH  Google Scholar 

  37. Nash, S.G.: SUMT (revisited). Oper. Res. 46, 763–775 (1998)

    Article  MATH  Google Scholar 

  38. Nocedal, J., Wright, S.J.: Numerical optimization. Springer, Berlin (2006)

    Google Scholar 

  39. Prékopa, A.: Contributions to the theory of stochastic programming. Math. Program. 4, 202–221 (1973)

    Article  MATH  Google Scholar 

  40. Sandmo, A.: Ragnar Frisch on the optimal diet. Hist. Polit. Econ. 25, 313–327 (1993)

    Article  Google Scholar 

  41. Schrijver, A.: Theory of linear and integer programming. Wiley, New York (1986)

    MATH  Google Scholar 

  42. Shanno, D.: Who invented the interior-point method? Documenta mathematica, optimization stories, 21st ISMP Berlin, pp. 55–64 (2012)

  43. Sonnevend, G.: An “analytic center” for polyhedrons and new classes of global algorithms for linear (smooth, convex) programming. In: Prekopa, A., Szelezsan, J., Strazicky, B.: (eds.) System modelling and optimization, Proceedings 12th IFIP Conference Lecture Notes in Control and Information Sciences 84, pp. 866–876 Springer, Berlin (1985)

  44. Sporre, G.: On some properties of interior point methods for optimization, Doctoral Thesis, Royal Institute of Technology, Stockholm (2003)

  45. Stiefel, E.: Note on Jordan elimination, linear programming and Tchebycheff approximation. Numerische Mathematik 2, 1–17 (1960)

    Article  MathSciNet  MATH  Google Scholar 

  46. Stiegler, G.J.: The cost of subsistence. J. Farm Econ. 27(2), 303–314 (1941)

    Article  Google Scholar 

  47. Vanderbei, R.J.: Linear programming. Kluwer, Boston (1996)

    MATH  Google Scholar 

  48. van Eijk, C.J., Sandee, J.: Quantitative determination of an optimum economic policy. Econometrica 27, 1–13 (1959)

    Article  MATH  Google Scholar 

  49. Wright, M.H.: Ill-conditioning and computational error in interior methods for nonlinear programming. SIAM J. Optim. 9, 84–111 (1999)

    Article  MathSciNet  Google Scholar 

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Bjerkholt, O., Flåm, S.D. Ragnar Frisch and interior-point methods. Optim Lett 9, 1053–1061 (2015). https://doi.org/10.1007/s11590-014-0807-x

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