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On the discovery and description of mathematical programming algorithms

Conference paper
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Part of the Lecture Notes in Mathematics book series (LNM, volume 506)

Keywords

Markov Transition Probability Algorithm NNLS Problem NNLS Mathematical Programming Literature Minimal Euclidean Length 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Richard Bartels, Constrained Least Squares, Quadratic Programming, Complementary Pivot Programming and Duality, Proceedings of the 8th Annual Symposium on the Interface of Computer Science & Statistics, Health Science Computing Facility, Univ. of Calif., Los Angeles, Feb. 1975, pp. 267–271.Google Scholar
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    P.J. Denning, guest editor, ACM Computing Surveys, Special issue on programming, Vol 6, No. 4, (1974), pp. 209–319.Google Scholar
  3. 3.
    James K. Hightower, An Algorithm for Computing Restricted Least-Squares Estimates of Markov Transition Probabilities from Time-Series Data, Proceedings of the 8th Annual Symposium on the interface of Computer Science and Statistics, Health Science Computing Facility, Univ. of Calif., Los Los Angeles, Feb. 1975, pp. 238–241.Google Scholar
  4. 4.
    C.L. Lawson and R.J. Hanson, Solving Least Squares Problems, Prentice-Hall, Inc., (1974)Google Scholar
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    G.W. Stewart, Introduction to Matrix Computations, Academic Press, (1973).Google Scholar
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    Josef Stoer, On the Numerical Solution of Constrained Least-Squares Problems, SIAM J. Numer. Anal., Vol 8, No. 2, (1971), pp. 382–411.MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Philip Wolfe, Algorithm for a Least-Distance Programming Problem, Mathematical Programming Study 1, (1974), pp. 190–205, North-Holland Publ. Co.Google Scholar

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© Springer-Verlag 1976

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