What is he best known for?
The fields of study he is best known for:
- Statistics
- Algorithm
- Algebra
His scientific interests lie mostly in Algorithm, Sparse approximation, Mathematical optimization, Compressed sensing and Matching pursuit.
His Algorithm research is multidisciplinary, incorporating perspectives in Block matrix, LU decomposition and Signal processing.
The study incorporates disciplines such as Dimension, Linear combination, Sparse matrix, Equiangular polygon and Eigenvalues and eigenvectors in addition to Sparse approximation.
His Mathematical optimization research integrates issues from Time complexity, Orthonormal basis and Kaczmarz method.
His Compressed sensing study combines topics in areas such as Sampling, Convolution and Signal reconstruction.
His Matching pursuit research is multidisciplinary, incorporating perspectives in Approximation algorithm, Greedy algorithm and Approximation theory.
His most cited work include:
- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit (7158 citations)
- CoSaMP: Iterative signal recovery from incomplete and inaccurate samples (3431 citations)
- Greed is good: algorithmic results for sparse approximation (3136 citations)
What are the main themes of his work throughout his whole career to date?
Joel A. Tropp spends much of his time researching Algorithm, Matrix, Random matrix, Combinatorics and Convex optimization.
Joel A. Tropp combines subjects such as Dimension, Sampling and Mathematical optimization with his study of Algorithm.
Joel A. Tropp interconnects Factorization, Semidefinite programming and Rank in the investigation of issues within Matrix.
His Random matrix research includes themes of Discrete mathematics, Matrix norm, Noncommutative geometry, Pure mathematics and Applied mathematics.
His studies examine the connections between Combinatorics and genetics, as well as such issues in Norm, with regards to Subspace topology.
The concepts of his Sparse approximation study are interwoven with issues in Linear combination, Greedy algorithm, Sparse matrix, Matching pursuit and Approximation theory.
He most often published in these fields:
- Algorithm (36.63%)
- Matrix (27.91%)
- Random matrix (18.60%)
What were the highlights of his more recent work (between 2017-2021)?
- Matrix (27.91%)
- Random matrix (18.60%)
- Pure mathematics (9.30%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Matrix, Random matrix, Pure mathematics, Applied mathematics and Dimensionality reduction.
In the field of Matrix, his study on Positive-definite matrix overlaps with subjects such as Decomposition.
His Random matrix course of study focuses on Concentration inequality and Mutually unbiased bases and Pauli exclusion principle.
His Dimensionality reduction study also includes fields such as
- Linear map together with Singular value, Embedding, Stochastic geometry and Universality,
- Randomized algorithm that intertwine with fields like Theoretical computer science, Singular value decomposition, Principal component analysis and Range,
- Numerical linear algebra which is related to area like Linear regression and Linear algebra.
With his scientific publications, his incorporates both A priori and a posteriori and Algorithm.
The study incorporates disciplines such as Dimension and Tensor in addition to Algorithm.
Between 2017 and 2021, his most popular works were:
- Universality laws for randomized dimension reduction, with applications (41 citations)
- Streaming Low-Rank Matrix Approximation With An Application To Scientific Simulation (22 citations)
- Randomized numerical linear algebra: Foundations and algorithms (20 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Algebra
- Mathematical analysis
Joel A. Tropp mostly deals with Matrix, Dimensionality reduction, Numerical linear algebra, Randomized algorithm and Semidefinite programming.
His Matrix research incorporates themes from Trace, Work, Pure mathematics, Smoothness and Product.
His research investigates the connection between Dimensionality reduction and topics such as Linear map that intersect with problems in Singular value, Embedding, Stochastic geometry and Universality.
His research integrates issues of Theoretical computer science, Data collection, Singular value decomposition, Compression and Low-rank approximation in his study of Numerical linear algebra.
His Semidefinite programming study is concerned with the field of Mathematical optimization as a whole.
His Mathematical optimization research integrates issues from Range, Quadratic equation, Phase retrieval and Convex optimization.
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.
