What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Quantum mechanics
- Algebra
Andreas Griewank spends much of his time researching Automatic differentiation, Mathematical optimization, Algorithm, Applied mathematics and Source transformation.
A large part of his Automatic differentiation studies is devoted to Operator overloading.
His studies in Mathematical optimization integrate themes in fields like Separable space, Invariant, Linear algebra and Unconstrained optimization.
His research integrates issues of Set, Invertible matrix, Parameter dependent and Combinatorics in his study of Applied mathematics.
His Theoretical computer science study combines topics from a wide range of disciplines, such as Simple and Software, Software development.
His Software study combines topics in areas such as Jacobian matrix and determinant and Hessian matrix.
His most cited work include:
- Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (1930 citations)
- Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Second Edition (766 citations)
- Algorithm 755: ADOL-C: a package for the automatic differentiation of algorithms written in C/C++ (683 citations)
What are the main themes of his work throughout his whole career to date?
Andreas Griewank focuses on Automatic differentiation, Applied mathematics, Mathematical optimization, Algorithm and Jacobian matrix and determinant.
Andreas Griewank has researched Automatic differentiation in several fields, including Theoretical computer science, Taylor series, Fortran and Nonlinear system.
In Applied mathematics, he works on issues like Piecewise, which are connected to Piecewise linear function, Lipschitz continuity and Differentiable function.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Bounded function and Sensitivity.
His work deals with themes such as Function, Sequence and Chain rule, which intersect with Algorithm.
His work in Jacobian matrix and determinant covers topics such as Newton's method which are related to areas like Gravitational singularity.
He most often published in these fields:
- Automatic differentiation (37.00%)
- Applied mathematics (26.00%)
- Mathematical optimization (25.00%)
What were the highlights of his more recent work (between 2011-2021)?
- Applied mathematics (26.00%)
- Automatic differentiation (37.00%)
- Piecewise (12.50%)
In recent papers he was focusing on the following fields of study:
Andreas Griewank mainly focuses on Applied mathematics, Automatic differentiation, Piecewise, Mathematical optimization and Piecewise linear function.
His research integrates issues of Linearization, Karush–Kuhn–Tucker conditions, Jacobian matrix and determinant, Rate of convergence and Solver in his study of Applied mathematics.
His Automatic differentiation study is concerned with Algorithm in general.
His studies deal with areas such as Differentiable function, Piecewise linearization, Lipschitz continuity and Trapezoidal rule as well as Piecewise.
His biological study spans a wide range of topics, including Gradient descent, Process and Bounded function.
His studies in Piecewise linear function integrate themes in fields like Linear independence and Newton's method.
Between 2011 and 2021, his most popular works were:
- On stable piecewise linearization and generalized algorithmic differentiation (55 citations)
- Solving piecewise linear systems in abs-normal form (31 citations)
- Trends in PDE Constrained Optimization (20 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Quantum mechanics
- Algebra
His scientific interests lie mostly in Automatic differentiation, Applied mathematics, Piecewise, Mathematical optimization and Lipschitz continuity.
The Automatic differentiation study combines topics in areas such as Earthquake engineering, Sensitivity, Jacobian matrix and determinant, Elementary function and Partial derivative.
His Applied mathematics research is multidisciplinary, incorporating perspectives in Rate of convergence, Fixed point and Solver.
His study in Piecewise is interdisciplinary in nature, drawing from both Piecewise linear function, Differentiable function, Minor and Karush–Kuhn–Tucker conditions.
His studies examine the connections between Minor and genetics, as well as such issues in Numerical analysis, with regards to Algorithm.
His work on Optimization problem as part of general Mathematical optimization study is frequently connected to Linearity of differentiation, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
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