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The authors discuss the role of automatic differentiation tools in optimization software. We emphasi...
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design opt...
[Excerpt] Numerical Optimization and Applications Symposium emphasizes modeling, theory and study of...
Our work under this support broadly falls into five categories: automatic differentiation, sparsity,...
The accurate and efficient computation of gradients for partially separable functions is central to ...
The accurate and ecient computation of gradients for partially separable functions is central to the...
Engineering optimization problems may be formulated as nonlinear programs (NLP), defined by an objec...
. Automatic differentiation (AD) is a methodology for developing sensitivity-enhanced versions of ar...
The recent explosion in size and complexity of datasets and the increased availability of computatio...
Automatic dierentiation is a powerful technique for evaluating derivatives of functions given in the...
In this paper, we introduce automatic differentiation as a method for computing derivatives of large...
.<F3.833e+05> This paper describes a software implementation of Byrd and Omojokun's trust...
A Theory Institute on ''Differentiation of Computational Approximations to Functions'' was held at A...
Optimization Methods and Software publishes refereed papers on the latest developments in the theory...
Multidisciplinary Design Optimization (MDO) by means of formal sensitivity analysis requires that ea...
The authors discuss the role of automatic differentiation tools in optimization software. We emphasi...
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design opt...
[Excerpt] Numerical Optimization and Applications Symposium emphasizes modeling, theory and study of...
Our work under this support broadly falls into five categories: automatic differentiation, sparsity,...
The accurate and efficient computation of gradients for partially separable functions is central to ...
The accurate and ecient computation of gradients for partially separable functions is central to the...
Engineering optimization problems may be formulated as nonlinear programs (NLP), defined by an objec...
. Automatic differentiation (AD) is a methodology for developing sensitivity-enhanced versions of ar...
The recent explosion in size and complexity of datasets and the increased availability of computatio...
Automatic dierentiation is a powerful technique for evaluating derivatives of functions given in the...
In this paper, we introduce automatic differentiation as a method for computing derivatives of large...
.<F3.833e+05> This paper describes a software implementation of Byrd and Omojokun's trust...
A Theory Institute on ''Differentiation of Computational Approximations to Functions'' was held at A...
Optimization Methods and Software publishes refereed papers on the latest developments in the theory...
Multidisciplinary Design Optimization (MDO) by means of formal sensitivity analysis requires that ea...
The authors discuss the role of automatic differentiation tools in optimization software. We emphasi...
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design opt...
[Excerpt] Numerical Optimization and Applications Symposium emphasizes modeling, theory and study of...