1992
DOI: 10.1007/bf03167197
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Successive linearization methods for large-scale nonlinear programming problems

Abstract: We propose a spavsity preserving algorithm for solving large-scale, nonlineav programming problems. The algorithm solves at each iteration a subproblem, which contalns a lineavized objective function augmented by a simple quadratic term and linearized constraints. The quadratic term added to the lineavized objective function plays the role of step restriction which is essential in ensuring global convergence of the algorithm. Ir the conjugate gradient method or successive over-relaxation method is used to solv… Show more

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Cited by 5 publications
(1 citation statement)
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“…Successive linearization methods for standard nonlinear programs can be found in [7,9,17,23]. These methods are typically quite robust and can usually be applied to larger problems than sequential quadratic programming algorithms since they deal with simpler subproblems.…”
Section: Introductionmentioning
confidence: 99%
“…Successive linearization methods for standard nonlinear programs can be found in [7,9,17,23]. These methods are typically quite robust and can usually be applied to larger problems than sequential quadratic programming algorithms since they deal with simpler subproblems.…”
Section: Introductionmentioning
confidence: 99%