| RECONDITIONED SPECTRAL PROJECTED GRADIENT METHOD ON CONVEX SETS |
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| 引用本文： | Lenys Bello,Marcos Raydan. RECONDITIONED SPECTRAL PROJECTED GRADIENT METHOD ON CONVEX SETS[J]. 计算数学(英文版), 2005, 0(3) |
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| 作者姓名： | Lenys Bello Marcos Raydan |
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| 作者单位： | Dpto.de Matemática(Lenys Bello)，Facultad(Marcos Raydan) |
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| 基金项目： | This author was partially supported by UCV-PROJECT 97-003769. |
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| 摘 要： | The spectral gradient method has proved to be effective for solving large-scale uncon-strained optimization problems.It has been recently extended and combined with theprojected gradient method for solving optimization problems on convex sets.This combi-nation includes the use of nonmonotone line search techniques to preserve the fast localconvergence.In this work we further extend the spectral choice of steplength to accept pre-conditioned directions when a good preconditioner is available.We present an algorithmthat combines the spectral projected gradient method with preconditioning strategies toincrease the local speed of convergence while keeping the global properties.We discussimplementation details for solving large-scale problems.
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RECONDITIONED SPECTRAL PROJECTED GRADIENT METHOD ON CONVEX SETS |
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| Lenys Bello. RECONDITIONED SPECTRAL PROJECTED GRADIENT METHOD ON CONVEX SETS[J]. Journal of Computational Mathematics, 2005, 0(3) |
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| Authors: | Lenys Bello |
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| Abstract: | The spectral gradient method has proved to be effective for solving large-scale uncon-strained optimization problems.It has been recently extended and combined with theprojected gradient method for solving optimization problems on convex sets.This combi-nation includes the use of nonmonotone line search techniques to preserve the fast localconvergence.In this work we further extend the spectral choice of steplength to accept pre-conditioned directions when a good preconditioner is available.We present an algorithmthat combines the spectral projected gradient method with preconditioning strategies toincrease the local speed of convergence while keeping the global properties.We discussimplementation details for solving large-scale problems. |
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| Keywords: | :Spectral gradient method Projected gradient method Preconditioning techniques Nonmonotone line search. |
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