www.Autodiff.org - Publication Database

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[1-20] [21-34]

Order by: [Title], [Author], [Editor], [Year]
	Max Sagebaum, Tim Albring, Nicolas R. Gauger Expression templates for primal value taping in the reverse mode of algorithmic differentiation Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	Application Area: General Tools: CoDiPack Theory & Techniques: Adjoint, Black Box, Code Optimization, Implementation Strategies, Performance, Reverse Mode
	Filip Srajer, Zuzana Kukelova, Andrew Fitzgibbon A benchmark of selected algorithmic differentiation tools on some problems in computer vision and machine learning Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	Tools: Adept, ADiMat, ADOL-C, DiffSharp, TAPENADE, Theano
	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
	Bruce Christianson, Shaun A. Forth, Andreas Griewank Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation Taylor & Francis, 2018	Theory & Techniques: General
	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
	Lisa Kusch, Tim Albring, Andrea Walther, Nicolas R. Gauger A one-shot optimization framework with additional equality constraints applied to multi-objective aerodynamic shape optimization Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	not yet classified
	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
	Olivier Mullier, Alexandre Chapoutot, dit Sandretto, Julien Alexandre Validated computation of the local truncation error of Runge--Kutta methods with automatic differentiation Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	not yet classified
	John D. Pryce, Nedialko S. Nedialkov, Guangning Tan, Xiao Li How AD can help solve differential-algebraic equations Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	not yet classified
	Ulrich Römer, Mahesh Narayanamurthi, Adrian Sandu Solving parameter estimation problems with discrete adjoint exponential integrators Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	not yet classified
	Siegfried M. Rump Mathematically rigorous global optimization in floating-point arithmetic Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	not yet classified
	Mu Wang, Guang Lin, Alex Pothen Using automatic differentiation for compressive sensing in uncertainty quantification Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	not yet classified
	Laurent Hascoët, Mathieu Morlighem Source-to-source adjoint Algorithmic Differentiation of an ice sheet model written in C Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	not yet classified
	Alexander Hück, Christian Bischof, Max Sagebaum, Nicolas R. Gauger, Benjamin Jurgelucks, Eric Larour, Gilberto Perez A usability case study of algorithmic differentiation tools on the ISSM ice sheet model Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	not yet classified
	Benjamin Jurgelucks, Leander Claes, Andrea Walther, Bernd Henning Optimization of triple-ring electrodes on piezoceramic transducers using algorithmic differentiation Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	not yet classified
	Bradley M. Bell, Kasper Kristensen Newton step methods for AD of an objective defined using implicit functions Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	not yet classified
	H. Martin Bücker, Johannes Willkomm Estimating the expansion coefficients of a geomagnetic field model using first-order derivatives of associated Legendre functions Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	Application Area: Geophysics Tools: ADiMat Theory & Techniques: Hierarchical Approach
	Isabelle Charpentier, Bruno Cochelin Towards a full higher order AD-based continuation and bifurcation framework Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	not yet classified
	Isabelle Charpentier, Jens Gustedt Arbogast: Higher order automatic differentiation for special functions with Modular C Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	not yet classified
	Bruce Christianson Differentiating through conjugate gradient Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018	not yet classified

[1-20] [21-34]

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