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LARGESCALE LINEARLY CONSTRAINED OPTIMIZATION
, 1978
"... An algorithm for solving largescale nonlinear ' programs with linear constraints is presented. The method combines efficient sparsematrix techniques as in the revised simplex method with stable quasiNewton methods for handling the nonlinearities. A generalpurpose production code (MINOS) is ..."
Abstract

Cited by 108 (21 self)
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An algorithm for solving largescale nonlinear ' programs with linear constraints is presented. The method combines efficient sparsematrix techniques as in the revised simplex method with stable quasiNewton methods for handling the nonlinearities. A generalpurpose production code (MINOS) is described, along with computational experience on a wide variety of problems.
Chapter 1
"... The objective of this research is to develop new learning methods based upon optimization techniques. Two different but related areas are focused on. The first is to develop new learning algorithms for neural networks. These new learning techniques could later be used in a variety of applications su ..."
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The objective of this research is to develop new learning methods based upon optimization techniques. Two different but related areas are focused on. The first is to develop new learning algorithms for neural networks. These new learning techniques could later be used in a variety of applications such as control and pattern
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"... The objective of this research is to develop new learning methods based upon optimization techniques. Two different but related areas are focused on. The first is to develop new learning algorithms for neural networks. These new learning ..."
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The objective of this research is to develop new learning methods based upon optimization techniques. Two different but related areas are focused on. The first is to develop new learning algorithms for neural networks. These new learning
NorthHolland Publishing Company MATRIX FACTOR1ZATIONS IN OPTIMIZATION OF NON LINEAR FUNCTIONS SUBJECT TO LINEAR CONSTRAINTS*
, 1974
"... Several ways of implementing methods for solving nonlinear optimization problems involving linear inequality and equality constraints using numerically stable matrix factorizations are described. The methods considered all follow an active constraint set approach and include quadratic programming, v ..."
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Several ways of implementing methods for solving nonlinear optimization problems involving linear inequality and equality constraints using numerically stable matrix factorizations are described. The methods considered all follow an active constraint set approach and include quadratic programming, variable metric, and modified Newton methods. 1.