What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Mathematical analysis
- Algorithm
Amir Beck mostly deals with Mathematical optimization, Convex optimization, Algorithm, Rate of convergence and Convex analysis.
The various areas that he examines in his Mathematical optimization study include Function, Random coordinate descent and Proximal Gradient Methods.
The study incorporates disciplines such as Deconvolution, Sparse matrix and Inverse problem in addition to Random coordinate descent.
In his research on the topic of Convex optimization, Feasible region, Karush–Kuhn–Tucker conditions, Upper and lower bounds and Iterative method is strongly related with Monotone polygon.
His work in the fields of Sparse approximation overlaps with other areas such as Deblurring and Discrete-time Fourier transform.
Amir Beck integrates many fields, such as Deblurring and Gradient method, in his works.
His most cited work include:
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems (7861 citations)
- Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems (1450 citations)
- Mirror descent and nonlinear projected subgradient methods for convex optimization (757 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Mathematical optimization, Convex optimization, Applied mathematics, Algorithm and Rate of convergence.
Amir Beck undertakes multidisciplinary investigations into Mathematical optimization and Deblurring in his work.
The various areas that he examines in his Applied mathematics study include Frank–Wolfe algorithm, Semidefinite programming, Matrix and Approximation theory.
His study in the field of Sparse approximation also crosses realms of Nonlinear programming.
His research in the fields of Normal convergence and Compact convergence overlaps with other disciplines such as Sublinear function and Convex function.
Proximal Gradient Methods is frequently linked to Random coordinate descent in his study.
He most often published in these fields:
- Mathematical optimization (60.58%)
- Convex optimization (26.92%)
- Applied mathematics (20.19%)
What were the highlights of his more recent work (between 2015-2020)?
- Mathematical optimization (60.58%)
- Function (12.50%)
- Minification (8.65%)
In recent papers he was focusing on the following fields of study:
Amir Beck mainly focuses on Mathematical optimization, Function, Minification, Proximal Gradient Methods and Applied mathematics.
His research in Mathematical optimization intersects with topics in Algorithm, Numerical analysis, Dual and Convex optimization.
His Algorithm research is multidisciplinary, incorporating perspectives in Sequence and Phase retrieval, Fourier transform.
He studied Minification and Simple that intersect with Total least squares.
His studies in Proximal Gradient Methods integrate themes in fields like Dimension, Topology and DUAL.
Amir Beck combines subjects such as Frank–Wolfe algorithm and Regular polygon with his study of Applied mathematics.
Between 2015 and 2020, his most popular works were:
- First-Order Methods in Optimization (312 citations)
- Linearly convergent away-step conditional gradient for non-strongly convex functions (43 citations)
- On the Minimization Over Sparse Symmetric Sets: Projections, Optimality Conditions, and Algorithms (36 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Mathematical optimization
- Algorithm
Mathematical optimization, Function, Minification, Feasible region and Applied mathematics are his primary areas of study.
His Mathematical optimization research includes themes of Algorithm, Theory of computation, Numerical analysis and Linear combination.
His studies deal with areas such as Basis, Variety and Phase retrieval, Fourier transform as well as Algorithm.
Amir Beck interconnects Class, Order and Hierarchy in the investigation of issues within Function.
While working on this project, Amir Beck studies both Feasible region and Rate of convergence.
His research integrates issues of Randomized methods, Descent, Regular polygon, Linear-fractional programming and Stationary point in his study of Applied mathematics.
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