Abstract
Standard methods for unconstrained optimization base each iteration on a quadratic model of the objective function. Recently, methods using two generalizations of the standard models have been proposed. Nonlinear scaling invariance methods use a model that is a nonlinear regular scaling of a quadratic function, and conic methods use a model that is the ratio of a quadratic function divided by the square of a linear function. This paper will attempt to survey some interesting developments in these fields.
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© 1994 Kluwer Academic Publishers
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Deng, N.Y., Li, Z.F. (1994). Nonquadratic Model Methods in Unconstrained Optimization. In: Spedicato, E. (eds) Algorithms for Continuous Optimization. NATO ASI Series, vol 434. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0369-2_6
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DOI: https://doi.org/10.1007/978-94-009-0369-2_6
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