An implementable descent method for unconstrained minimization of convex nonsmooth functions of several variables is described. The algorithm is characterized by the use of a set of quadratic approximations of the objective function in order to compute the search direction. The resulting direction finfing subproblem is shown to be equivalent to a structured parametric quadratic programming problem. The convergence of the algorithm to the minimum is proved, and numerical experience is reported.

Quadratic approximations in convex nondifferentiable optimization

GAUDIOSO, Manlio;MONACO, Maria Flavia
1991

Abstract

An implementable descent method for unconstrained minimization of convex nonsmooth functions of several variables is described. The algorithm is characterized by the use of a set of quadratic approximations of the objective function in order to compute the search direction. The resulting direction finfing subproblem is shown to be equivalent to a structured parametric quadratic programming problem. The convergence of the algorithm to the minimum is proved, and numerical experience is reported.
Nondifferentiable optimization; Bundle methods
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/156740
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