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FILTRANE, a Fortran 95 filter-trust-region package for solving nonlinear least-squares and nonlinear feasibility problems

Authors:

Nicholas I. M. Gould,

Philippe L. TointAuthors Info & Claims

ACM Transactions on Mathematical Software (TOMS), Volume 33, Issue 1

Pages 3 - es

https://doi.org/10.1145/1206040.1206043

Published: 01 March 2007 Publication History

Abstract

FILTRANE, a new Fortran 95 package for finding vectors satisfying general sets of nonlinear equations and/or inequalities, is presented. Several algorithmic variants are discussed and extensively compared on a set of CUTEr test problems, indicating that the default variant is both reliable and efficient. This discussion provides a first experimental study of the parameters inherent in filter algorithms.

References

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Fletcher, R., Gould, N. I. M., Leyffer, S., Toint, Ph. L., and Wächter, A. 2002a. Global convergence of trust-region SQP-filter algorithms for nonlinear programming. SIAM J. Optimiz. 13, 3, 635--659.

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Fletcher, R., Leyffer, S., and Toint, Ph. L. 2002b. On the global convergence of a filter-SQP algorithm. SIAM J. Optimiz. 13, 1, 44--59.

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Cited By

Chen XToint P(2020)High-order evaluation complexity for convexly-constrained optimization with non-Lipschitzian group sparsity termsMathematical Programming10.1007/s10107-020-01470-9Online publication date: 28-Jan-2020
https://doi.org/10.1007/s10107-020-01470-9
Chen XToint PWang H(2019)Complexity of Partially Separable Convexly Constrained Optimization with Non-Lipschitzian SingularitiesSIAM Journal on Optimization10.1137/18M116651129:1(874-903)Online publication date: 28-Mar-2019
https://doi.org/10.1137/18M1166511
Hiermeier MWall WPopp A(2018)A truly variationally consistent and symmetric mortar-based contact formulation for finite deformation solid mechanicsComputer Methods in Applied Mechanics and Engineering10.1016/j.cma.2018.07.020342(532-560)Online publication date: Dec-2018
https://doi.org/10.1016/j.cma.2018.07.020
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Index Terms

FILTRANE, a Fortran 95 filter-trust-region package for solving nonlinear least-squares and nonlinear feasibility problems
1. Mathematics of computing
  1. Mathematical software

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Information

Published In

cover image ACM Transactions on Mathematical Software

ACM Transactions on Mathematical Software Volume 33, Issue 1

March 2007

134 pages

ISSN:0098-3500

EISSN:1557-7295

DOI:10.1145/1206040

Issue’s Table of Contents

Copyright © 2007 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 March 2007

Published in TOMS Volume 33, Issue 1

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Cited By

Chen XToint P(2020)High-order evaluation complexity for convexly-constrained optimization with non-Lipschitzian group sparsity termsMathematical Programming10.1007/s10107-020-01470-9Online publication date: 28-Jan-2020
https://doi.org/10.1007/s10107-020-01470-9
Chen XToint PWang H(2019)Complexity of Partially Separable Convexly Constrained Optimization with Non-Lipschitzian SingularitiesSIAM Journal on Optimization10.1137/18M116651129:1(874-903)Online publication date: 28-Mar-2019
https://doi.org/10.1137/18M1166511
Hiermeier MWall WPopp A(2018)A truly variationally consistent and symmetric mortar-based contact formulation for finite deformation solid mechanicsComputer Methods in Applied Mechanics and Engineering10.1016/j.cma.2018.07.020342(532-560)Online publication date: Dec-2018
https://doi.org/10.1016/j.cma.2018.07.020
Macêdo MCosta MRocha AKaras E(2017)Combining Filter Method and Dynamically Dimensioned Search for Constrained Global OptimizationComputational Science and Its Applications – ICCSA 201710.1007/978-3-319-62398-6_9(119-134)Online publication date: 14-Jul-2017
https://doi.org/10.1007/978-3-319-62398-6_9
Ferreira PKaras ESachine MSobral F(2016)Global convergence of a derivative-free inexact restoration filter algorithm for nonlinear programmingOptimization10.1080/02331934.2016.126362966:2(271-292)Online publication date: 22-Dec-2016
https://doi.org/10.1080/02331934.2016.1263629
Gonçalves DSantos S(2016)Local analysis of a spectral correction for the Gauss-Newton model applied to quadratic residual problemsNumerical Algorithms10.1007/s11075-016-0101-373:2(407-431)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s11075-016-0101-3
Chen JVuik C(2016)Globalization technique for projected Newton-Krylov methodsInternational Journal for Numerical Methods in Engineering10.1002/nme.5426110:7(661-674)Online publication date: 11-Oct-2016
https://doi.org/10.1002/nme.5426
Saeidian ZReza Peyghami MHabibi MGhasemi S(2015)A new trust-region method for solving systems of equalities and inequalitiesComputational and Applied Mathematics10.1007/s40314-015-0257-936:1(769-790)Online publication date: 29-Jul-2015
https://doi.org/10.1007/s40314-015-0257-9
Grapsa T(2012)A modified Newton direction for unconstrained optimizationOptimization10.1080/02331934.2012.69611563:7(983-1004)Online publication date: 26-Jun-2012
https://doi.org/10.1080/02331934.2012.696115
Morini BPorcelli M(2012)TRESNEI, a Matlab trust-region solver for systems of nonlinear equalities and inequalitiesComputational Optimization and Applications10.1007/s10589-010-9327-551:1(27-49)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.1007/s10589-010-9327-5
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