A class of collinear scaling algorithms for bound-constrained optimization: Derivation and computational results

Authors:
K. A. Ariyawansa;Wayne L. Tabor
Affiliations:
Department of Mathematics, Washington State University, Pullman, WA 99164-3113, United States;Department of Mathematics, Washington State University, Pullman, WA 99164-3113, United States and Department of Mathematics, East Carolina University, Greenville, NC 27858-4353, United States
Venue:
Journal of Computational and Applied Mathematics
Year:
2009

Citing 5
Cited 0

Deriving collinear scaling algorithms as extension of quasi-Newton methods and the local convergence of DFP-and BFGS-related collinear scaling algorithms

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Trust-region methods

Trust-region methods
Optimization algorithms based on conic approximations and collinear scalings

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Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)

Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)

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Abstract

A family of algorithms for the approximate solution of the bound-constrained minimization problem is described. These algorithms employ the standard barrier method, with the inner iteration based on trust region methods. Local models are conic functions rather than the usual quadratic functions, and are required to match first and second derivatives of the barrier function at the current iterate. The various members of the family are distinguished by the choice of a vector-valued parameter, which is the zero vector in the degenerate case that quadratic local models are used. Computational results are used to compare the efficiency of various members of the family on a selection of test functions.