What is he best known for?
The fields of study he is best known for:
- Eigenvalues and eigenvectors
- Algorithm
- Algebra
Danny C. Sorensen mostly deals with Eigenvalues and eigenvectors, Mathematical optimization, Applied mathematics, Singular value decomposition and Algorithm.
Danny C. Sorensen works in the field of Eigenvalues and eigenvectors, namely Arnoldi iteration.
Danny C. Sorensen has included themes like Newton's method and Trust region in his Mathematical optimization study.
His research in Applied mathematics intersects with topics in Jacobi eigenvalue algorithm and Algebra.
His work in Singular value decomposition addresses subjects such as Scale, which are connected to disciplines such as Dynamical systems theory and Reduction methods.
His studies deal with areas such as Stability, Matrix and Numerical stability as well as Algorithm.
His most cited work include:
- ARPACK Users' Guide: Solution of Large-Scale Eigenvalue Problems with Implicitly Restarted Arnoldi Methods (2399 citations)
- Computing a Trust Region Step (1100 citations)
- Nonlinear Model Reduction via Discrete Empirical Interpolation (1035 citations)
What are the main themes of his work throughout his whole career to date?
Danny C. Sorensen focuses on Eigenvalues and eigenvectors, Applied mathematics, Mathematical optimization, Mathematical analysis and Algorithm.
His Eigenvalues and eigenvectors research is multidisciplinary, incorporating perspectives in Matrix, Software, Numerical analysis and Linear algebra.
His research investigates the link between Matrix and topics such as Factorization that cross with problems in Parallel computing.
His biological study spans a wide range of topics, including Iterative method, Linear system, Generalized minimal residual method, Arnoldi iteration and Lanczos algorithm.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Singular value decomposition, Reduction, Hessian matrix and Trust region.
His work is dedicated to discovering how Mathematical analysis, Nonlinear system are connected with Interpolation, Partial differential equation, Projection and Domain decomposition methods and other disciplines.
He most often published in these fields:
- Eigenvalues and eigenvectors (23.68%)
- Applied mathematics (21.05%)
- Mathematical optimization (21.05%)
What were the highlights of his more recent work (between 2005-2020)?
- Nonlinear system (9.87%)
- Mathematical analysis (16.45%)
- Mathematical optimization (21.05%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Nonlinear system, Mathematical analysis, Mathematical optimization, Applied mathematics and Reduction.
His Nonlinear system study integrates concerns from other disciplines, such as Projection, Domain decomposition methods, Partial differential equation, Interpolation and Discretization.
His work deals with themes such as Dynamical systems theory and Eigenvalues and eigenvectors, which intersect with Mathematical analysis.
The various areas that he examines in his Mathematical optimization study include Shape optimization, Hessian matrix and Trust region.
His Applied mathematics research is multidisciplinary, incorporating elements of LTI system theory, Jacobian matrix and determinant, Generalized minimal residual method, Numerical analysis and Solver.
While the research belongs to areas of Factorization, Danny C. Sorensen spends his time largely on the problem of Matrix, intersecting his research to questions surrounding Singular value decomposition.
Between 2005 and 2020, his most popular works were:
- Nonlinear Model Reduction via Discrete Empirical Interpolation (1035 citations)
- The ground state correlation energy of the random phase approximation from a ring coupled cluster doubles approach. (213 citations)
- Discrete Empirical Interpolation for nonlinear model reduction (127 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Eigenvalues and eigenvectors
- Mathematical analysis
His main research concerns Mathematical analysis, Nonlinear system, Projection, Interpolation and Mathematical optimization.
His work on Dynamical systems theory expands to the thematically related Mathematical analysis.
The Projection study combines topics in areas such as Finite difference, Galerkin method and Applied mathematics.
His Interpolation study which covers Dimension that intersects with Dimensionality reduction, State variable and Reduction.
His work in Mathematical optimization covers topics such as Shape optimization which are related to areas like Continuous optimization.
His Numerical analysis research includes elements of Truncation and Differential algebraic equation.
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