What is he best known for?
The fields of study he is best known for:
- Algorithm
- Programming language
- Graph theory
John R. Gilbert mainly investigates Sparse matrix, Algorithm, Parallel computing, Pivot element and Gaussian elimination.
His Sparse matrix research is multidisciplinary, incorporating elements of Numerical linear algebra, Data structure and Speedup.
His study in Parallel algorithm and Computation is done as part of Algorithm.
He interconnects Multiplication, Array data structure and Fortran in the investigation of issues within Parallel computing.
The various areas that he examines in his Pivot element study include Factorization and Incomplete LU factorization.
John R. Gilbert studied Incomplete LU factorization and Incomplete Cholesky factorization that intersect with Combinatorics.
His most cited work include:
- A Supernodal Approach to Sparse Partial Pivoting (663 citations)
- Sparse matrices in matlab: design and implementation (470 citations)
- Provably good mesh generation (344 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Sparse matrix, Theoretical computer science, Parallel computing, Algorithm and Discrete mathematics.
His Sparse matrix study is related to the wider topic of Matrix.
His Theoretical computer science research is multidisciplinary, incorporating perspectives in Graph, Graph, Power graph analysis, Graph operations and Graph theory.
John R. Gilbert has researched Parallel computing in several fields, including Multiplication, Correctness, Data structure and Fortran.
His work in Algorithm addresses subjects such as Incomplete LU factorization, which are connected to disciplines such as LU decomposition.
His Discrete mathematics study incorporates themes from Approximation algorithm and Combinatorics.
He most often published in these fields:
- Sparse matrix (35.82%)
- Theoretical computer science (28.36%)
- Parallel computing (24.63%)
What were the highlights of his more recent work (between 2013-2018)?
- Theoretical computer science (28.36%)
- Graph (8.96%)
- Graph (13.43%)
In recent papers he was focusing on the following fields of study:
John R. Gilbert mostly deals with Theoretical computer science, Graph, Graph, Algorithm and Power graph analysis.
His Theoretical computer science research is multidisciplinary, relying on both Graph theory and Graph operations.
His Graph research focuses on Data structure and how it relates to Path and Mathematical optimization.
His biological study spans a wide range of topics, including Cycle basis and Enumeration.
In his study, Approximation algorithm is inextricably linked to Discrete mathematics, which falls within the broad field of Combinatorics.
When carried out as part of a general Matrix research project, his work on Sparse matrix is frequently linked to work in Programming complexity, therefore connecting diverse disciplines of study.
Between 2013 and 2018, his most popular works were:
- Mathematical foundations of the GraphBLAS (96 citations)
- Parallel Triangle Counting and Enumeration Using Matrix Algebra (82 citations)
- Mathematical Foundations of the GraphBLAS (57 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Algorithm
- Graph theory
His primary scientific interests are in Theoretical computer science, Matrix, Graph operations, Matrix multiplication and Adjacency matrix.
His research in Theoretical computer science intersects with topics in Algorithm and Graph, Graph rewriting.
The concepts of his Algorithm study are interwoven with issues in Query language and Data mining.
His Matrix multiplication research incorporates themes from Semiring, Enumeration, Graph energy, Sparse matrix and Adjacency list.
His Sparse matrix study combines topics from a wide range of disciplines, such as Overhead, Composability, Approximation algorithm, Range and Parallel algorithm.
His Adjacency matrix research is within the category of Discrete mathematics.
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