Objective-derivative-free methods for constrained optimization |
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Authors: | S Lucidi M Sciandrone P Tseng |
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Institution: | (1) Università di Roma “La Sapienza”, Dipartimento di Informatica e Sistemistica, Via Buonarroti 12, 00185 Roma, Italy, e-mail: lucidi@dis.uniroma1.it, IT;(2) Istituto di Analisi dei Sistemi ed Informatica, Consiglio Nazionale delle Ricerche, Viale Manzoni 30, 00185 Roma, Italy, e-mail: sciandro@iasi.rm.cnr.it, IT;(3) Department of Mathematics, University of Washington, Seattle, Washington 98195, USA, e-mail: tseng@math.washington.edu, US |
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Abstract: | We propose feasible descent methods for constrained minimization that do not make explicit use of the derivative of the objective
function. The methods iteratively sample the objective function value along a finite set of feasible search arcs and decrease
the sampling stepsize if an improved objective function value is not sampled. The search arcs are obtained by projecting search
direction rays onto the feasible set and the search directions are chosen such that a subset approximately generates the cone
of first-order feasible variations at the current iterate. We show that these methods have desirable convergence properties
under certain regularity assumptions on the constraints. In the case of linear constraints, the projections are redundant
and the regularity assumptions hold automatically. Numerical experience with the methods in the linearly constrained case
is reported.
Received: November 12, 1999 / Accepted: April 6, 2001?Published online October 26, 2001 |
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Keywords: | : constrained optimization – derivative-free method – feasible descent – stationary point – metric regularity – MFCQ |
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