From: Jean-Philippe ARGAUD Date: Thu, 26 Jan 2017 18:15:18 +0000 (+0100) Subject: Documentation update for DFO X-Git-Tag: V8_3_0rc1~33 X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=75478b691579978b6255ed17b1c7a65fbeb8e3f0;p=modules%2Fadao.git Documentation update for DFO --- diff --git a/doc/en/bibliography.rst b/doc/en/bibliography.rst index b236c4d..4e261ae 100644 --- a/doc/en/bibliography.rst +++ b/doc/en/bibliography.rst @@ -31,9 +31,9 @@ Bibliography .. [GilBellosta15] Gil Bellosta C. J., *rPython: Package Allowing R to Call Python*, CRAN, 2015, https://cran.r-project.org/web/packages/rPython/ and http://rpython.r-forge.r-project.org/ -.. [Bouttier99] Bouttier B., Courtier P., *Data assimilation concepts and methods*, Meteorological Training Course Lecture Series, ECMWF, 1999, http://www.ecmwf.int/sites/default/files/Data%20assimilation%20concepts%20and%20methods.pdf +.. [Bouttier99] Bouttier B., Courtier P., *Data assimilation concepts and methods*, Meteorological Training Course Lecture Series, ECMWF, 1999 -.. [Bocquet04] Bocquet M., *Introduction aux principes et méthodes de l'assimilation de données en géophysique*, Lecture Notes, 2014, http://cerea.enpc.fr/HomePages/bocquet/Doc/assim-mb.pdf +.. [Bocquet04] Bocquet M., *Introduction aux principes et méthodes de l'assimilation de données en géophysique*, Lecture Notes, 2014 .. [Buchinsky98] Buchinsky M., *Recent Advances in Quantile Regression Models: A Practical Guidline for Empirical Research*, Journal of Human Resources, 33(1), pp.88-126, 1998 @@ -43,20 +43,34 @@ Bibliography .. [Ide97] Ide K., Courtier P., Ghil M., Lorenc A. C., *Unified notation for data assimilation: operational, sequential and variational*, Journal of the Meteorological Society of Japan, 75(1B), pp.181-189, 1997 +.. [Johnson08] Johnson S. G., *The NLopt nonlinear-optimization package*, http://ab-initio.mit.edu/nlopt + .. [Kalnay03] Kalnay E., *Atmospheric Modeling, Data Assimilation and Predictability*, Cambridge University Press, 2003 .. [Koenker00] Koenker R., Hallock K. F., *Quantile Regression: an Introduction*, 2000, http://www.econ.uiuc.edu/~roger/research/intro/intro.html .. [Koenker01] Koenker R., Hallock K. F., *Quantile Regression*, Journal of Economic Perspectives, 15(4), pp.143-156, 2001 -.. [Morales11] Morales J.L., Nocedal J., *L-BFGS-B: Remark on Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization*, ACM Transactions on Mathematical Software, 38(1), 2011 +.. [Morales11] Morales J. L., Nocedal J., *L-BFGS-B: Remark on Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization*, ACM Transactions on Mathematical Software, 38(1), 2011 + +.. [Nelder65] Nelder J. A., Mead R., *A simplex method for function minimization*, The Computer Journal, 7, pp.308-313, 1965 + +.. [Powell64] Powell M. J. D., *An efficient method for finding the minimum of a function of several variables without calculating derivatives*, Computer Journal, 7(2), pp.155-162, 1964 + +.. [Powell94] Powell M. J. D., *A direct search optimization method that models the objective and constraint functions by linear interpolation*, in Advances in Optimization and Numerical Analysis, eds. S. Gomez and J-P Hennart, Kluwer Academic (Dordrecht), pp. 51-67, 1994 -.. [Nelder] Nelder J.A., Mead R., *A simplex method for function minimization*, The Computer Journal, 7, pp.308-313, 1965 +.. [Powell98] Powell M. J. D., *Direct search algorithms for optimization calculations*, Acta Numerica 7, 287-336, 1998 -.. [Powell] Powell M.J.D., *An efficient method for finding the minimum of a function of several variables without calculating derivatives*, Computer Journal, 7(2), pp.155-162, 1964 +.. [Powell04] Powell M. J. D., *The NEWUOA software for unconstrained optimization without derivatives*, Proc. 40th Workshop on Large Scale Nonlinear Optimization, Erice, Italy, 2004 + +.. [Powell07] Powell M. J. D., *A view of algorithms for optimization without derivatives*, Cambridge University Technical Report DAMTP 2007/NA03, 2007 + +.. [Powell09] Powell M. J. D., *The BOBYQA algorithm for bound constrained optimization without derivatives*, Cambridge University Technical Report DAMTP NA2009/06, 2009 .. [R] *The R Project for Statistical Computing*, http://www.r-project.org/ +.. [Rowan90] Rowan T., *Functional Stability Analysis of Numerical Algorithms*, Ph.D. thesis, Department of Computer Sciences, University of Texas at Austin, 1990 + .. [Salome] *SALOME The Open Source Integration Platform for Numerical Simulation*, http://www.salome-platform.org/ .. [SalomeMeca] *Salome_Meca and Code_Aster, Analysis of Structures and Thermomechanics for Studies & Research*, http://www.code-aster.org/ diff --git a/doc/en/ref_algorithm_DerivativeFreeOptimization.rst b/doc/en/ref_algorithm_DerivativeFreeOptimization.rst index 3cf1021..9579eed 100644 --- a/doc/en/ref_algorithm_DerivativeFreeOptimization.rst +++ b/doc/en/ref_algorithm_DerivativeFreeOptimization.rst @@ -37,8 +37,8 @@ Description This algorithm realizes an estimation of the state of a system by minimization of a cost function :math:`J` without gradient. It is a method that doesn't use -the derivatives of the cost function. It fall for example in the same category -then the :ref:`section_ref_algorithm_ParticleSwarmOptimization`. +the derivatives of the cost function. It fall, for example, in the same category +than the :ref:`section_ref_algorithm_ParticleSwarmOptimization`. This is an optimization method allowing for global minimum search of a general error function :math:`J` of type :math:`L^1`, :math:`L^2` or :math:`L^{\infty}`, @@ -109,17 +109,19 @@ The options of the algorithm are the following: Minimizer This key allows to choose the optimization minimizer. The default choice is - "POWELL", and the possible ones are "POWELL" (modified Powell unconstrained - minimizer, see [Powell]_), "SIMPLEX" (simplex or Nelder-Mead unconstrained - minimizer, see [Nelder]_), "COBYLA" (constrained optimization by linear - approximation). It is recommended to stay with the default when there is no - bounds, and to choose "COBYLA" when there are bounds. Remark: the default - "POWELL" method perform a dual outer/inner loops optimization, leading then - to less control on the cost function evaluation number because it is the - outer loop limit than is controled. If precise control on this cost function - evaluation number is required, choose the "SIMPLEX" or the "COBYLA" one. - - Example : ``{"Minimizer":"POWELL"}`` + "BOBYQA", and the possible ones are + "BOBYQA" (minimization with or without contraints by quadratic approximation [Powell09]_), + "COBYLA" (minimization with or without contraints by linear approximation [Powell94]_ [Powell98]_). + "NEWUOA" (minimization with or without contraints by iterative quadratic approximation [Powell04]_), + "POWELL" (minimization unconstrained using conjugate directions [Powell64]_), + "SIMPLEX" (minimization with or without contraints using Nelder-Mead simplex algorithm [Nelder65]_), + "SUBPLEX" (minimization with or without contraints using Nelder-Mead on a sequence of subspaces [Rowan90]_). + Remark: the "POWELL" method perform a dual outer/inner loops optimization, + leading then to less control on the cost function evaluation number because + it is the outer loop limit than is controled. If precise control on this + cost function evaluation number is required, choose an another minimizer. + + Example : ``{"Minimizer":"BOBYQA"}`` MaximumNumberOfSteps This key indicates the maximum number of iterations allowed for iterative @@ -249,5 +251,12 @@ References to other sections: - :ref:`section_ref_algorithm_ParticleSwarmOptimization` Bibliographical references: - - [Nelder]_ - - [Powell]_ + - [Johnson08]_ + - [Nelder65]_ + - [Powell64]_ + - [Powell94]_ + - [Powell98]_ + - [Powell04]_ + - [Powell07]_ + - [Powell09]_ + - [Rowan90]_ diff --git a/doc/en/ref_algorithm_ParticleSwarmOptimization.rst b/doc/en/ref_algorithm_ParticleSwarmOptimization.rst index 2214157..aa6f918 100644 --- a/doc/en/ref_algorithm_ParticleSwarmOptimization.rst +++ b/doc/en/ref_algorithm_ParticleSwarmOptimization.rst @@ -33,7 +33,7 @@ Description This algorithm realizes an estimation of the state of a dynamic system by minimization of a cost function :math:`J` by using a particle swarm. It is a method that doesn't use the derivatives of the cost function. It fall in the -same category then the :ref:`section_ref_algorithm_DerivativeFreeOptimization`. +same category than the :ref:`section_ref_algorithm_DerivativeFreeOptimization`. This is an optimization method allowing for global minimum search of a general error function :math:`J` of type :math:`L^1`, :math:`L^2` or :math:`L^{\infty}`, diff --git a/doc/fr/bibliography.rst b/doc/fr/bibliography.rst index a48d205..3fa58ad 100644 --- a/doc/fr/bibliography.rst +++ b/doc/fr/bibliography.rst @@ -31,9 +31,9 @@ Bibliographie .. [GilBellosta15] Gil Bellosta C. J., *rPython: Package Allowing R to Call Python*, CRAN, 2015, https://cran.r-project.org/web/packages/rPython/ and http://rpython.r-forge.r-project.org/ -.. [Bouttier99] Bouttier B., Courtier P., *Data assimilation concepts and methods*, Meteorological Training Course Lecture Series, ECMWF, 1999, http://www.ecmwf.int/sites/default/files/Data%20assimilation%20concepts%20and%20methods.pdf +.. [Bouttier99] Bouttier B., Courtier P., *Data assimilation concepts and methods*, Meteorological Training Course Lecture Series, ECMWF, 1999 -.. [Bocquet04] Bocquet M., *Introduction aux principes et méthodes de l'assimilation de données en géophysique*, Lecture Notes, 2014, http://cerea.enpc.fr/HomePages/bocquet/Doc/assim-mb.pdf +.. [Bocquet04] Bocquet M., *Introduction aux principes et méthodes de l'assimilation de données en géophysique*, Lecture Notes, 2014 .. [Buchinsky98] Buchinsky M., *Recent Advances in Quantile Regression Models: A Practical Guidline for Empirical Research*, Journal of Human Resources, 33(1), pp.88-126, 1998 @@ -43,20 +43,34 @@ Bibliographie .. [Ide97] Ide K., Courtier P., Ghil M., Lorenc A. C., *Unified notation for data assimilation: operational, sequential and variational*, Journal of the Meteorological Society of Japan, 75(1B), pp.181-189, 1997 +.. [Johnson08] Johnson S. G., *The NLopt nonlinear-optimization package*, http://ab-initio.mit.edu/nlopt + .. [Kalnay03] Kalnay E., *Atmospheric Modeling, Data Assimilation and Predictability*, Cambridge University Press, 2003 .. [Koenker00] Koenker R., Hallock K. F., *Quantile Regression: an Introduction*, 2000, http://www.econ.uiuc.edu/~roger/research/intro/intro.html .. [Koenker01] Koenker R., Hallock K. F., *Quantile Regression*, Journal of Economic Perspectives, 15(4), pp.143-156, 2001 -.. [Morales11] Morales J.L., Nocedal J., *L-BFGS-B: Remark on Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization*, ACM Transactions on Mathematical Software, 38(1), 2011 +.. [Morales11] Morales J. L., Nocedal J., *L-BFGS-B: Remark on Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization*, ACM Transactions on Mathematical Software, 38(1), 2011 + +.. [Nelder65] Nelder J. A., Mead R., *A simplex method for function minimization*, The Computer Journal, 7, pp.308-313, 1965 + +.. [Powell64] Powell M. J. D., *An efficient method for finding the minimum of a function of several variables without calculating derivatives*, Computer Journal, 7(2), pp.155-162, 1964 + +.. [Powell94] Powell M. J. D., *A direct search optimization method that models the objective and constraint functions by linear interpolation*, in Advances in Optimization and Numerical Analysis, eds. S. Gomez and J-P Hennart, Kluwer Academic (Dordrecht), pp. 51-67, 1994 -.. [Nelder] Nelder J.A., Mead R., *A simplex method for function minimization*, The Computer Journal, 7, pp.308-313, 1965 +.. [Powell98] Powell M. J. D., *Direct search algorithms for optimization calculations*, Acta Numerica 7, 287-336, 1998 -.. [Powell] Powell M.J.D., *An efficient method for finding the minimum of a function of several variables without calculating derivatives*, Computer Journal, 7(2), pp.155-162, 1964 +.. [Powell04] Powell M. J. D., *The NEWUOA software for unconstrained optimization without derivatives*, Proc. 40th Workshop on Large Scale Nonlinear Optimization, Erice, Italy, 2004 + +.. [Powell07] Powell M. J. D., *A view of algorithms for optimization without derivatives*, Cambridge University Technical Report DAMTP 2007/NA03, 2007 + +.. [Powell09] Powell M. J. D., *The BOBYQA algorithm for bound constrained optimization without derivatives*, Cambridge University Technical Report DAMTP NA2009/06, 2009 .. [R] *The R Project for Statistical Computing*, http://www.r-project.org/ +.. [Rowan90] Rowan T., *Functional Stability Analysis of Numerical Algorithms*, Ph.D. thesis, Department of Computer Sciences, University of Texas at Austin, 1990 + .. [Salome] *SALOME The Open Source Integration Platform for Numerical Simulation*, http://www.salome-platform.org/ .. [SalomeMeca] *Salome_Meca et Code_Aster, Analyse des Structures et Thermomécanique pour les Etudes et la Recherche*, http://www.code-aster.org/ diff --git a/doc/fr/ref_algorithm_DerivativeFreeOptimization.rst b/doc/fr/ref_algorithm_DerivativeFreeOptimization.rst index 96d186d..74def65 100644 --- a/doc/fr/ref_algorithm_DerivativeFreeOptimization.rst +++ b/doc/fr/ref_algorithm_DerivativeFreeOptimization.rst @@ -37,7 +37,7 @@ Description Cet algorithme réalise une estimation d'état d'un système par minimisation d'une fonctionnelle d'écart :math:`J` sans gradient. C'est une méthode qui n'utilise -pas les dérivées de la fonctionnelle d'écart. Elle entre par exemple dans la +pas les dérivées de la fonctionnelle d'écart. Elle entre, par exemple, dans la même catégorie que l':ref:`section_ref_algorithm_ParticleSwarmOptimization`. C'est une méthode d'optimisation permettant la recherche du minimum global d'une @@ -111,19 +111,20 @@ Les options de l'algorithme sont les suivantes: Minimizer Cette clé permet de changer le minimiseur pour l'optimiseur. Le choix par - défaut est "POWELL", et les choix possibles sont "POWELL" (minimisation sans - contraintes de type Powell modifiée, voir [Powell]_), "SIMPLEX" - (minimisation sans contraintes de type simplexe ou Nelder-Mead, voir - [Nelder]_), "COBYLA" (minimisation avec contraintes par approximation - linéaire). Il est conseillé de conserver la valeur par défaut lorsqu'il n'y - a pas de bornes, et de passer à "COBYLA" en cas de bornes. Remarque : la - méthode par défaut "POWELL" effectue une optimisation par boucles imbriquées - interne/externe, conduisant ainsi à un contrôle relaché du nombre - d'évaluations de la fonctionnelle à optimiser. Si un contrôle précis du - nombre d'évaluations de cette fonctionnelle est requis, il faut choisir - "SIMPLEX" ou "COBYLA". - - Exemple : ``{"Minimizer":"POWELL"}`` + défaut est "BOBYQA", et les choix possibles sont + "BOBYQA" (minimisation avec ou sans contraintes par approximation quadratique [Powell09]_), + "COBYLA" (minimisation avec ou sans contraintes par approximation linéaire [Powell94]_ [Powell98]_). + "NEWUOA" (minimisation avec ou sans contraintes par approximation quadratique itérative [Powell04]_), + "POWELL" (minimisation sans contraintes de type directions conjuguées [Powell64]_), + "SIMPLEX" (minimisation avec ou sans contraintes de type simplexe ou Nelder-Mead, voir [Nelder65]_), + "SUBPLEX" (minimisation avec ou sans contraintes de type simplexe sur une suite de sous-espaces [Rowan90]_). + Remarque : la méthode "POWELL" effectue une optimisation par boucles + imbriquées interne/externe, conduisant ainsi à un contrôle relaché du + nombre d'évaluations de la fonctionnelle à optimiser. Si un contrôle précis + du nombre d'évaluations de cette fonctionnelle est requis, il faut choisir + un autre minimiseur. + + Exemple : ``{"Minimizer":"BOBYQA"}`` MaximumNumberOfSteps Cette clé indique le nombre maximum d'itérations possibles en optimisation @@ -256,5 +257,12 @@ R - :ref:`section_ref_algorithm_ParticleSwarmOptimization` Références bibliographiques : - - [Nelder]_ - - [Powell]_ + - [Johnson08]_ + - [Nelder65]_ + - [Powell64]_ + - [Powell94]_ + - [Powell98]_ + - [Powell04]_ + - [Powell07]_ + - [Powell09]_ + - [Rowan90]_