M
Marcos Raydan
Researcher at Simón Bolívar University
Publications - 90
Citations - 4776
Marcos Raydan is an academic researcher from Simón Bolívar University. The author has contributed to research in topics: Gradient method & Matrix (mathematics). The author has an hindex of 24, co-authored 83 publications receiving 4296 citations. Previous affiliations of Marcos Raydan include University of São Paulo & University of Kentucky.
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Nonmonotone Spectral Projected Gradient Methods on Convex Sets
TL;DR: The classical projected gradient schemes are extended to include a nonmonotone steplength strategy that is based on the Grippo--Lampariello--Lucidi non monotone line search that is combined with the spectral gradient choice of steplENGTH to accelerate the convergence process.
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The Barzilai and Borwein Gradient Method for the Large Scale Unconstrained Minimization Problem
TL;DR: Results indicate that the global Barzilai and Borwein method may allow some significant reduction in the number of line searches and also in theNumber of gradient evaluations.
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On the Barzilai and Borwein choice of steplength for the gradient method
TL;DR: In this article, the convergence of the Barzilai and Borwein gradient method was established for the minimization of a strictly convex quadratic function of any number of variables.
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Spectral residual method without gradient information for solving large-scale nonlinear systems of equations
TL;DR: A fully derivative-free spectral residual method for solving largescale nonlinear systems of equations that uses in a systematic way the residual vector as a search direction, a spectral steplength that produces a nonmonotone process and a globalization strategy that allows for this nonmonothone behavior.
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Algorithm 813: SPG—Software for Convex-Constrained Optimization
TL;DR: Fortran 77 software implementing the SPG method, a nonmonotone projected gradient algorithm for solving large-scale convex-constrained optimization problems, which is substantially more efficient than existing general-purpose software on problems for which projections can be computed efficiently.