We exhibit an aggressive optimizing compiler for a functionalprogramming language which includes a first-class forward automatic differentiation (AD) operator. The compiler’s performance is competitive with FORTRAN-based systems on our numerical examples, despite the potential inefficiencies entailed by support of a functional-programming language and a first-class AD operator. These results are achieved by combining (1) a novel formulation of forward AD in terms of a reflexive mechanism that supports firstclass nestable nonstandard interpretation with (2) the migration to compile-time of the conceptually run-time nonstandard interpretation by whole-program inter-procedural flow analysis. Categories and Subject Descriptors G.1.4 [Quadrature...
PhD ThesisFunctional programming languages such as Haskell allow numerical algorithms to be expresse...
International audienceThe computation of gradients via the reverse mode of algorithmic differentiati...
We present ForwardDiff, a Julia package for forward-mode automatic differentiation (AD) featuring pe...
We exhibit an aggressive optimizing compiler for a functionalprogramming language which includes a f...
We show that Automatic Differentiation (AD) operators can be provided in a dynamic language without ...
We propose extensions to FORTRAN which integrate forward and reverse Automatic Differentiation (AD)...
The numerical methods employed in the solution of many scientific computing problems require the com...
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design opt...
The numerical methods employed in the solution of many scientific computing problems require the com...
The numerical methods employed in the solution of many scientific computing problems require the com...
We discuss the augmentation of a functional-programming language with a derivative-taking operator i...
We show that reverse-mode AD (Automatic Differentiation)—a generalized gradient-calculation operato...
This paper discusses a new Automatic Differentiation (AD) system that correctly and automatically ac...
This paper presents ideas for using coordinate-free numerics in modern Fortran to achieve code flexi...
The program described creates the first derivative functions of given function of limited complexit...
PhD ThesisFunctional programming languages such as Haskell allow numerical algorithms to be expresse...
International audienceThe computation of gradients via the reverse mode of algorithmic differentiati...
We present ForwardDiff, a Julia package for forward-mode automatic differentiation (AD) featuring pe...
We exhibit an aggressive optimizing compiler for a functionalprogramming language which includes a f...
We show that Automatic Differentiation (AD) operators can be provided in a dynamic language without ...
We propose extensions to FORTRAN which integrate forward and reverse Automatic Differentiation (AD)...
The numerical methods employed in the solution of many scientific computing problems require the com...
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design opt...
The numerical methods employed in the solution of many scientific computing problems require the com...
The numerical methods employed in the solution of many scientific computing problems require the com...
We discuss the augmentation of a functional-programming language with a derivative-taking operator i...
We show that reverse-mode AD (Automatic Differentiation)—a generalized gradient-calculation operato...
This paper discusses a new Automatic Differentiation (AD) system that correctly and automatically ac...
This paper presents ideas for using coordinate-free numerics in modern Fortran to achieve code flexi...
The program described creates the first derivative functions of given function of limited complexit...
PhD ThesisFunctional programming languages such as Haskell allow numerical algorithms to be expresse...
International audienceThe computation of gradients via the reverse mode of algorithmic differentiati...
We present ForwardDiff, a Julia package for forward-mode automatic differentiation (AD) featuring pe...