What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Algebra
- Statistics
His primary areas of investigation include Mathematical optimization, Nonlinear programming, Algorithm, Constrained optimization and Linear programming.
His research investigates the link between Mathematical optimization and topics such as Sequence that cross with problems in Term, Quadratic penalty function and Type.
He interconnects Subroutine, Fortran, Augmented Lagrangian method and Sequential quadratic programming in the investigation of issues within Nonlinear programming.
His work on Sparse approximation and Total variation denoising as part of general Algorithm study is frequently connected to Basis pursuit denoising and Basis pursuit, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His biological study spans a wide range of topics, including Quasi-Newton method and Hessian matrix.
His studies in Optimization problem integrate themes in fields like Factorization, Orthogonal basis, Wavelet packet decomposition and Cholesky decomposition.
His most cited work include:
- Atomic Decomposition by Basis Pursuit (5658 citations)
- Atomic Decomposition by Basis Pursuit (3891 citations)
- LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares (3237 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Mathematical optimization, Nonlinear programming, Algorithm, Quadratic programming and Applied mathematics.
His study on Mathematical optimization is mostly dedicated to connecting different topics, such as Nonlinear system.
His Nonlinear programming research focuses on subjects like Optimization problem, which are linked to Robustness and Sequence.
A large part of his Algorithm studies is devoted to Sparse approximation.
His study in the fields of Quadratically constrained quadratic program under the domain of Quadratic programming overlaps with other disciplines such as Second-order cone programming.
The Applied mathematics study combines topics in areas such as Function, Iterative method, Condition number and Mathematical analysis.
He most often published in these fields:
- Mathematical optimization (38.95%)
- Nonlinear programming (22.63%)
- Algorithm (20.00%)
What were the highlights of his more recent work (between 2015-2021)?
- Mathematical optimization (38.95%)
- Algorithm (20.00%)
- Nonlinear system (11.05%)
In recent papers he was focusing on the following fields of study:
Michael A. Saunders mostly deals with Mathematical optimization, Algorithm, Nonlinear system, Optimization problem and Applied mathematics.
His work is dedicated to discovering how Mathematical optimization, Nonlinear programming are connected with Penalty method and other disciplines.
His Algorithm research includes themes of Spectral line, Solver and Constrained optimization.
His work in Nonlinear system addresses issues such as Simplex, which are connected to fields such as Exponential growth.
His Optimization problem research includes elements of Sequence, Scale, Augmented Lagrangian method and Biochemical engineering.
His work deals with themes such as Iterative method, Black box, Canonical correlation and Minification, which intersect with Applied mathematics.
Between 2015 and 2021, his most popular works were:
- Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0 (297 citations)
- Structural basis of latent TGF-β1 presentation and activation by GARP on human regulatory T cells (45 citations)
- Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0. (35 citations)
In his most recent research, the most cited papers focused on:
- Algebra
- Statistics
- Mathematical optimization
His scientific interests lie mostly in Systems biology, Mathematical optimization, Antibody, Immune system and Cytokine.
As a part of the same scientific family, Michael A. Saunders mostly works in the field of Systems biology, focusing on Metabolism and, on occasion, Macromolecule and Biological system.
The various areas that Michael A. Saunders examines in his Mathematical optimization study include Algorithm, MATLAB, Subroutine and Nonlinear system.
His study in Algorithm is interdisciplinary in nature, drawing from both Expression, Nonlinear programming and Solver.
His work in the fields of Antibody, such as Immunoglobulin E, overlaps with other areas such as Mepolizumab and Interleukin 5.
His Linear programming research incorporates elements of Quadratic equation, Quadratic function, Quadratic programming, Constrained optimization and Fortran.
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.
