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Order by: [Title], [Author], [Editor], [Year]
Jan C. Hückelheim, Paul D. Hovland, Michelle M. Strout, Jens-Dominik Müller
Parallelizable adjoint stencil computations using transposed forward-mode algorithmic differentiation
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
Application Area:
Computational Fluid Dynamics
Tools:
TAPENADE
Theory & Techniques:
Adjoint, Code Optimization, control-flow reversal, Data Flow Analysis, data-flow reversal, Implementation Strategies, Parallelism, Performance, Reverse Mode, Source transformation
Shahadat Hossain, Nasrin Hakim Mithila
Pattern graph for sparse Hessian matrix determination
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
not yet classified
Andreas Griewank, Tom Streubel, Lutz Lehmann, Manuel Radons, Richard Hasenfelder
Piecewise linear secant approximation via algorithmic piecewise differentiation
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
Theory & Techniques:
Piecewise Linear
Bruce Christianson, Shaun A. Forth, Andreas Griewank
Preface
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
Theory & Techniques:
General
Yingyi Li, Haibin Zhang, Zhibao Li, Huan Gao
Proximal gradient method with automatic selection of the parameter by automatic differentiation
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
not yet classified

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