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Large-scale linearly constrained optimization

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Abstract

An algorithm for solving large-scale nonlinear programs with linear constraints is presented. The method combines efficient sparse-matrix techniques as in the revised simplex method with stable quasi-Newton methods for handling the nonlinearities. A general-purpose production code (MINOS) is described, along with computational experience on a wide variety of problems.

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Authors and Affiliations

  1. University of New South Wales, Sydney, Australia

    B. A. Murtagh

  2. DSIR, Wellington, New Zealand

    M. A. Saunders

  3. Stanford University, Stanford, CA, USA

    M. A. Saunders

Authors
  1. B. A. Murtagh
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  2. M. A. Saunders
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Additional information

This research was supported by the U.S. Office of Naval Research (Contract N00014-75-C-0267), the National Science Foundation (Grants MCS71-03341 A04, DCR75-04544), the U.S. Energy Research and Development Administration (Contract E(04-3)-326 PA #18), the Victoria University of Wellington, New Zealand, and the Department of Scientific and Industrial Research Wellington, New Zealand.

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Murtagh, B.A., Saunders, M.A. Large-scale linearly constrained optimization. Mathematical Programming 14, 41–72 (1978). https://doi.org/10.1007/BF01588950

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