E
Ernesto G. Birgin
Researcher at University of São Paulo
Publications - 116
Citations - 11098
Ernesto G. Birgin is an academic researcher from University of São Paulo. The author has contributed to research in topics: Nonlinear programming & Augmented Lagrangian method. The author has an hindex of 35, co-authored 112 publications receiving 8757 citations. Previous affiliations of Ernesto G. Birgin include State University of Campinas.
Papers
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PACKMOL: a package for building initial configurations for molecular dynamics simulations.
TL;DR: This work has developed a code able to pack millions of atoms, grouped in arbitrarily complex molecules, inside a variety of three‐dimensional regions, which can be intersections of spheres, ellipses, cylinders, planes, or boxes.
Journal ArticleDOI
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.
Journal ArticleDOI
On Augmented Lagrangian Methods with General Lower-Level Constraints
TL;DR: The resolution of location problems in which many constraints of the lower-level set are nonlinear is addressed, employing the spectral projected gradient method for solving the subproblems.
Book
Practical Augmented Lagrangian Methods for Constrained Optimization
TL;DR: This book focuses on Augmented Lagrangian techniques for solving practical constrained optimization problems and rigorously delineates mathematical convergence theory based on sequential optimality conditions and novel constraint qualifications.
Journal ArticleDOI
A Spectral Conjugate Gradient Method for Unconstrained Optimization
TL;DR: The Perry, the Polak—Ribière and the Fletcher—Reeves formulae are compared using a spectral scaling derived from Raydan's spectral gradient optimization method to find the best combination of formula, scaling and initial choice of step-length.