An Active Set Trust-Region Method for Bound-Constrained Optimization
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AbstractThis paper discusses an active set trust-region algorithm for bound-constrained optimization problems. A sufficient descent condition is used as a computational measure to identify whether the function value is reduced or not. To get our complexity result, a critical measure is used which is computationally better than the other known critical measures. Under the positive definiteness of approximated Hessian matrices restricted to the subspace of non-active variables, it will be shown that unlimited zigzagging cannot occur. It is shown that our algorithm is competitive in comparison with the state-of-the-art solvers for solving an ill-conditioned bound-constrained least-squares problem.
2011 ◽
Vol 26
(4-5)
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pp. 873-894
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2014 ◽
Vol 28
(5)
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pp. 1128-1147
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2016 ◽
Vol 2016
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pp. 1-10
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2007 ◽
Vol 22
(5)
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pp. 835-848
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