What is he best known for?
The fields of study he is best known for:
- Eigenvalues and eigenvectors
- Mathematical analysis
- Algebra
His primary areas of study are Algorithm, Numerical analysis, Reduction, Applied mathematics and Linear system.
Paul Van Dooren is interested in Singular value decomposition, which is a field of Algorithm.
His Numerical analysis study combines topics from a wide range of disciplines, such as Lanczos algorithm, Lanczos resampling, Dynamical systems theory and Schur decomposition.
His research in Reduction intersects with topics in Krylov subspace, Geometry, Degree, Interpolation and Interconnection.
His work deals with themes such as Orthonormal basis, Computation, Mathematical optimization and Order, which intersect with Applied mathematics.
His biological study spans a wide range of topics, including Linear subspace and Multivariable calculus.
His most cited work include:
- A Measure of Similarity between Graph Vertices: Applications to Synonym Extraction and Web Searching (362 citations)
- Geographical dispersal of mobile communication networks (342 citations)
- A Generalized Eigenvalue Approach for Solving Riccati Equations (339 citations)
What are the main themes of his work throughout his whole career to date?
Paul Van Dooren focuses on Applied mathematics, Matrix, Combinatorics, Eigenvalues and eigenvectors and Algorithm.
His Applied mathematics research incorporates themes from Linear system, Discrete time and continuous time, Norm, Mathematical optimization and Numerical analysis.
The concepts of his Matrix study are interwoven with issues in Polynomial and Rank.
His studies deal with areas such as Discrete mathematics, Factorization, Nonnegative matrix and Singular value as well as Combinatorics.
Paul Van Dooren interconnects Structure, Block and Pure mathematics in the investigation of issues within Eigenvalues and eigenvectors.
He combines subjects such as Lanczos resampling and Topology with his study of Algorithm.
He most often published in these fields:
- Applied mathematics (21.84%)
- Matrix (21.84%)
- Combinatorics (20.87%)
What were the highlights of his more recent work (between 2016-2021)?
- Eigenvalues and eigenvectors (19.90%)
- Matrix (21.84%)
- Applied mathematics (21.84%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Eigenvalues and eigenvectors, Matrix, Applied mathematics, Transfer function and Robustness.
His Eigenvalues and eigenvectors study integrates concerns from other disciplines, such as Structure and Degree, Block, Combinatorics.
His Matrix research is multidisciplinary, incorporating perspectives in Linear system, Diagonal, Pure mathematics and Polynomial, Matrix polynomial.
The concepts of his Applied mathematics study are interwoven with issues in Schur decomposition and Hamiltonian.
The Transfer function study combines topics in areas such as Computation and Topology.
The study incorporates disciplines such as Linear matrix inequality and Algebra in addition to Robustness.
Between 2016 and 2021, his most popular works were:
- Block Kronecker Linearizations of Matrix Polynomials and their Backward Errors (39 citations)
- A framework for structured linearizations of matrix polynomials in various bases (33 citations)
- Robust port-Hamiltonian representations of passive systems (27 citations)
In his most recent research, the most cited papers focused on:
- Eigenvalues and eigenvectors
- Algebra
- Mathematical analysis
His scientific interests lie mostly in Polynomial, Matrix, Robustness, Eigenvalues and eigenvectors and Matrix polynomial.
His research integrates issues of Linear subspace, Pure mathematics, Basis and Finite set in his study of Polynomial.
In general Matrix study, his work on Elementary divisors often relates to the realm of Degree matrix, thereby connecting several areas of interest.
His Eigenvalues and eigenvectors research incorporates themes from Structure, Approximations of π, Applied mathematics, Rational matrices and Nonlinear system.
The various areas that Paul Van Dooren examines in his Matrix polynomial study include Eigendecomposition of a matrix, Combinatorics, Kronecker delta, Matrix pencil and Pencil.
His study in Pencil is interdisciplinary in nature, drawing from both Discrete mathematics, Modulo and Block.
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