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scopus(26)
Circumventing storage limitations in Variational data assimilation studies
ArticleAbstract: An application of Pontryagin's maximum principle data assimilation is used to blend possibly incomplPalabras claves:Adjoint model, Climate, Gradient, Meteorology, Natural resources recovery, Recursion, STORAGE, variational data assimilationAutores:Andreas Griewank, Leaf G.K., Restrepo J.M.Fuentes:scopusAn algorithm for nonsmooth optimization by successive piecewise linearization
ArticleAbstract: We present an optimization method for Lipschitz continuous, piecewise smooth (PS) objective functionPalabras claves:abs-normal form, Algorithmic differentiation, Clarke stationary, Nonsmooth optimization, Piecewise smoothnessAutores:Andreas Griewank, Fiege S., Walther A.Fuentes:scopusA first look at quasi-Monte Carlo for lattice field theory problems
Conference ObjectAbstract: In this project we initiate an investigation of the applicability of Quasi-Monte Carlo methods to laPalabras claves:Autores:Andreas Griewank, Jansen K., Leovey H., Müller-Preussker M., Nube A.Fuentes:scopusADIFOR: A fortran system for portable automatic differentiation
Conference ObjectAbstract: Automatic differentiation provides the foundation for sensitivity analysis and subsequent design optPalabras claves:Autores:Andreas Griewank, Bischof C.Fuentes:scopusADIFOR: Automatic differentiation in a source translator environment
Conference ObjectAbstract: The numerical methods employed in the solution of many scientific computing problems require the comPalabras claves:Automatic differentiation, Chain rule, Derivative, Gradient, Jacobian, Parascope parallel programming environment, Source transformation and optimizationAutores:Andreas Griewank, Bischof C., Carle A., Corliss G.F.Fuentes:scopusADOL-C: Computing higher-order derivatives and sparsity pattern for functions written in C/C++
Conference ObjectAbstract: This paper presents ADOL-C, a software package for the Automatic Differentiation of C and C++ codes.Palabras claves:Automatic differentiation, Forward mode, Higher-order derivatives, Reverse mode, Sparsity detectionAutores:Andreas Griewank, Walther A.Fuentes:scopusAccumulating Jacobians as chained sparse matrix products
ArticleAbstract: The chain rule - fundamental to any kind of analytical differentiation - can be applied in various wPalabras claves:Chained matrix product, Combinatorial optimization, Dynamic programming, Edge elimination in computational graphsAutores:Andreas Griewank, Naumann U.Fuentes:scopusAchieving logarithmic growth of temporal and spatial complexity in reverse automatic differentiation
ArticleAbstract: In its basic form the reverse mode of automatic differentiation yields gradient vectors at a small mPalabras claves:Adjoint, Checkpointing, Complexity, Gradient, RecursionAutores:Andreas GriewankFuentes:scopusAdifor—generating derivative codes from fortran programs
ArticleAbstract: The numerical methods employed in the solution of many scientific computing problems require the comPalabras claves:Autores:Andreas Griewank, Bischof C., Carle A., Corliss G.F., Hovland P.Fuentes:scopusAlgorithm 755: ADOL-C: A Package for the Automatic Differentiation of Algorithms Written in C/C++
ArticleAbstract: The C++ package ADOL-C described here facilitates the evaluation of first and higher derivatives ofPalabras claves:ALGORITHMS, Automatic differentiation, G.1.4 [Numerical Analysis]: Quadrature and Numerical Differentiation - computational differentiation, I.1.2 [Algebraic Manipulation]: Algorithms - analysis of algorithms, performanceAutores:Andreas Griewank, Juedes D., Utke J.Fuentes:scopus