World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
44
Citations
15767
World Ranking
1528
National Ranking
89

Engineering and Technology

D-Index
44
Citations
15806
World Ranking
5670
National Ranking
186

Research.com Recognitions

  • 2017 - SIAM Fellow For fundamental contributions to algorithmic differentiation and to iterative methods for nonlinear optimization.

Overview

Andreas Griewank is affiliated with Humboldt-Universität zu Berlin in Germany. Their research primarily spans the fields of Mathematics and Computer Science, with a notable focus on Computational Theory and Mathematics, Numerical Analysis, Modeling and Simulation, Computational Mechanics, and Mathematical Physics.

The scientist's work covers several advanced research topics, including:

  • Advanced Optimization Algorithms Research
  • Optimization and Variational Analysis
  • Fractional Differential Equations Solutions
  • Sparse and Compressive Sensing Techniques
  • Matrix Theory and Algorithms
  • Homotopy and Cohomology in Algebraic Topology
  • Topological and Geometric Data Analysis

Andreas Griewank has authored papers in respected publication venues, notably:

  • Optimization methods & software
  • Applicable Analysis
  • Algorithms

Recent publications highlight ongoing contributions to optimization and applied mathematical analysis:

  • "Nonsmooth optimization by successive abs-linearization in function spaces," 2020, Applicable Analysis
  • "Polyhedral DC Decomposition and DCA Optimization of Piecewise Linear Functions," 2020, Algorithms
  • "On the abs-polynomial expansion of piecewise smooth functions," 2020, Optimization methods & software
  • "A semismooth conjugate gradients method - theoretical analysis," 2024, Optimization methods & software

Collaborations feature a number of frequent co-authors, including Andrea Walther, Olga Weiß, Stephan Schmidt, Tom Streubel, and Caren Tischendorf.

Andreas Griewank has been recognized with the SIAM Fellow award in 2017 for contributions pertaining to algorithmic differentiation and iterative methods for nonlinear optimization.

Best Publications

  • Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation

    Andreas Griewank;Andrea Walther

  • Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Second Edition

    Andreas Griewank;Andrea Walther

  • Algorithm 755: ADOL-C: a package for the automatic differentiation of algorithms written in C/C++

    Andreas Griewank;David Juedes;Jean Utke

  • ADIFOR-Generating Derivative Codes from Fortran Programs

    Christian Bischof;Alan Carle;George Corliss;Andreas Griewank

  • Algorithm 799: revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation

    Andreas Griewank;Andrea Walther

  • Achieving logarithmic growth of temporal and spatial complexity in reverse automatic differentiation

    Andreas Griewank

  • Generalized descent for global optimization

    A. O. Griewank

  • Automatic differentiation of algorithms : theory, implementation, and application

    Andreas Griewank;George F. Corliss

  • Automatic Differentiation Of Algorithms: From Simulation To Optimization

    George Corliss;Christèle Faure;Andreas Griewank;Lauren Hascoët

  • Automatic Differentiation of Algorithms

    George Corliss;Christèle Faure;Andreas Griewank;Laurent Hascoët

  • Partitioned variable metric updates for large structured optimization problems

    A. Griewank;Ph. L. Toint

  • On the unconstrained optimization of partially separable functions

    Andreas Griewank;Philippe Toint

  • Local convergence analysis for partitioned quasi-Newton updates

    A. Griewank;Ph. L. Toint

  • Characterization and Computation of Generalized Turning Points

    A. Griewank;G. W. Reddien

  • Approximate inverse preconditionings for sparse linear systems

    J. D. F. Cosgrove;J. C. Díaz;A. Griewank

  • On Solving Nonlinear Equations with Simple Singularities or Nearly Singular Solutions

    A. Griewank

  • Introduction to Automatic Differentiation

    Andreas Griewank;Andrea Walther

  • The Calculation of Hopf Points by a Direct Method

    A. Griewank;G. Reddien

  • On the calculation of Jacobian matrices by the Markowitz rule

    A. Griewank;S. Reese

  • A mathematical view of automatic differentiation

    Andreas Griewank

  • New strategy for phase equilibrium and critical point calculations by thermodynamic energy analysis. Part I. Stability analysis and flash

    N.R. Nagarajan;A.S. Cullick;A. Griewank

Frequent Co-Authors

Christian Bischof
Christian Bischof Technical University of Darmstadt
Philippe L. Toint
Philippe L. Toint University of Namur
Frank Scherbaum
Frank Scherbaum University of Potsdam
Rolf Rannacher
Rolf Rannacher Heidelberg University
Jorge J. Moré
Jorge J. Moré Argonne National Laboratory
Günter Leugering
Günter Leugering University of Erlangen-Nuremberg
Sebastian Engell
Sebastian Engell TU Dortmund University
Ya-xiang Yuan
Ya-xiang Yuan Chinese Academy of Sciences
Frances Y. Kuo
Frances Y. Kuo University of New South Wales
Ian H. Sloan
Ian H. Sloan University of New South Wales

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