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Newton-type methods for unconstrained and linearly constrained optimization (1974)

Gill, Philip E. ; Murray, Walter

Springer

Mathematical programming 7 (1974), S. 311-350

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ISSN: 1436-4646

Source: Springer Online Journal Archives 1860-2000

Topics: Computer Science , Mathematics

Notes: Abstract This paper describes two numerically stable methods for unconstrained optimization and their generalization when linear inequality constraints are added. The difference between the two methods is simply that one requires the Hessian matrix explicitly and the other does not. The methods are intimately based on the recurrence of matrix factorizations and are linked to earlier work on quasi-Newton methods and quadratic programming.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1007/BF01585529

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