Large-scale linearly constrained optimization |
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| Authors: | B A Murtagh M A Saunders |
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| Affiliation: | (1) University of New South Wales, Sydney, Australia;(2) DSIR, Wellington, New Zealand;(3) Stanford University, Stanford, CA, USA |
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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.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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| Keywords: | Large-scale Systems Linear Constraints Linear Programming Nonlinear Programming Optimization Quasi-Newton Method Reduced-gradient Method Simplex Method Sparse Matrix Variable-metric Method |
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