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- Nicholas J. Higham

Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
69
Citations
18,128
191
World Ranking
130
National Ranking
5

Engineering and Technology
D-index
54
Citations
16,787
141
World Ranking
1064
National Ranking
64

2020 - ACM Fellow For contributions to numerical linear algebra, numerical stability analysis, and communication of mathematics

2016 - Member of Academia Europaea

2009 - SIAM Fellow For contributions to numerical linear algebra and rounding error analysis.

2007 - Fellow of the Royal Society, United Kingdom

- Algebra
- Complex number
- Eigenvalues and eigenvectors

His primary scientific interests are in Matrix, Mathematical analysis, Condition number, Algorithm and Matrix function. Matrix connects with themes related to Numerical analysis in his study. His Mathematical analysis study combines topics from a wide range of disciplines, such as Invertible matrix, Square root of a matrix, Eigenvalues and eigenvectors, Quadratic eigenvalue problem and Newton's method.

His research on Algorithm focuses in particular on Round-off error. In his research on the topic of Round-off error, Standard algorithms, Gaussian elimination, LU decomposition, Rounding and Pairwise summation is strongly related with Floating point. Nicholas J. Higham has included themes like Matrix exponential, Square matrix and Applied mathematics in his Matrix function study.

- Functions of Matrices: Theory and Computation (1514 citations)
- Accuracy and stability of numerical algorithms (1474 citations)
- Accuracy and Stability of Numerical Algorithms (1461 citations)

Nicholas J. Higham mainly investigates Matrix, Algorithm, Mathematical analysis, Combinatorics and Applied mathematics. His Eigenvalues and eigenvectors research extends to the thematically linked field of Matrix. His Algorithm research is multidisciplinary, relying on both Numerical analysis, Numerical stability and Schur decomposition.

The various areas that Nicholas J. Higham examines in his Applied mathematics study include Linear system and Numerical linear algebra. His studies in Matrix function integrate themes in fields like Matrix exponential, Square matrix and Discrete mathematics. The study incorporates disciplines such as Fréchet derivative and LU decomposition in addition to Condition number.

- Matrix (30.34%)
- Algorithm (21.36%)
- Mathematical analysis (16.10%)

- Matrix (30.34%)
- Algorithm (21.36%)
- Matrix function (13.31%)

His scientific interests lie mostly in Matrix, Algorithm, Matrix function, Floating point and LU decomposition. His research in Matrix intersects with topics in Factorization, Sine, Pure mathematics and Combinatorics. Nicholas J. Higham interconnects Condition number, MATLAB, Arithmetic underflow and Schur decomposition in the investigation of issues within Algorithm.

Nicholas J. Higham has researched Matrix function in several fields, including Matrix exponential, Discrete mathematics, Variety and Hyperbolic function. His Floating point research incorporates themes from Matrix multiplication, Rounding, Numerical linear algebra and Computational science. The concepts of his LU decomposition study are interwoven with issues in Iterative refinement, Linear system and Round-off error.

- Accelerating the Solution of Linear Systems by Iterative Refinement in Three Precisions (66 citations)
- Harnessing GPU tensor cores for fast FP16 arithmetic to speed up mixed-precision iterative refinement solvers (60 citations)
- A New Analysis of Iterative Refinement and Its Application to Accurate Solution of Ill-Conditioned Sparse Linear Systems (43 citations)

- Algebra
- Complex number
- Eigenvalues and eigenvectors

The scientist’s investigation covers issues in Matrix, Floating point, Algorithm, Double-precision floating-point format and Applied mathematics. Nicholas J. Higham interconnects Eigenvalues and eigenvectors and Combinatorics in the investigation of issues within Matrix. The various areas that Nicholas J. Higham examines in his Floating point study include Carry and Arithmetic.

His Matrix function research is multidisciplinary, incorporating elements of Matrix exponential, Band matrix and Mathematical analysis. His work deals with themes such as Linear system, Generalized minimal residual method, Arithmetic underflow, Round-off error and Row and column spaces, which intersect with LU decomposition. His Generalized minimal residual method study combines topics from a wide range of disciplines, such as Condition number and Solver.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Accuracy and Stability of Numerical Algorithms

Nicholas J. Higham.

Society for Industrial and Applied Mathematics; 2002. **(2002)**

5645 Citations

Functions of Matrices: Theory and Computation

Nicholas J. Higham.

Philadelphia, PA, USA: Society for Industrial and Applied Mathematics; 2008. **(2008)**

2371 Citations

Computing the nearest correlation matrix—a problem from finance

Nicholas J. Higham.

Ima Journal of Numerical Analysis **(2002)**

917 Citations

MATLAB Guide

Desmond J. Higham;Nicholas J. Higham.

**(2000)**

771 Citations

COMPUTING A NEAREST SYMMETRIC POSITIVE SEMIDEFINITE MATRIX

Nicholas J. Higham.

Linear Algebra and its Applications **(1988)**

694 Citations

The Scaling and Squaring Method for the Matrix Exponential Revisited

Nicholas J. Higham.

SIAM Journal on Matrix Analysis and Applications **(2005)**

554 Citations

Computing the polar decomposition with applications

Nicholas J Higham.

Siam Journal on Scientific and Statistical Computing **(1986)**

528 Citations

Functions of matrices

Nicholas J. Higham.

**(2008)**

428 Citations

Computing the Action of the Matrix Exponential, with an Application to Exponential Integrators

Awad H. Al-Mohy;Nicholas J. Higham.

SIAM Journal on Scientific Computing **(2011)**

377 Citations

The numerical stability of barycentric Lagrange interpolation

Nicholas J. Higham.

Ima Journal of Numerical Analysis **(2004)**

345 Citations

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