What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Mathematical optimization
- Algorithm
His primary areas of investigation include Mathematical optimization, Convex optimization, Rate of convergence, Proximal Gradient Methods and Convex analysis.
His Mathematical optimization research is multidisciplinary, incorporating elements of Bregman divergence, Algorithm, Convex function and Deblurring.
His biological study spans a wide range of topics, including Image processing and Minification.
His study in Proximal Gradient Methods is interdisciplinary in nature, drawing from both Modes of convergence, Optimization problem, Proximal gradient methods for learning and Random coordinate descent.
His Random coordinate descent research includes elements of Deconvolution and Inverse problem.
His Convex analysis research incorporates themes from Uniform convergence, Weak convergence, Subderivative and Applied mathematics.
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)
- Proximal alternating linearized minimization for nonconvex and nonsmooth problems (886 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Mathematical optimization, Convex optimization, Rate of convergence, Applied mathematics and Proximal Gradient Methods.
The study incorporates disciplines such as Algorithm, Bounded function and Nonlinear programming in addition to Mathematical optimization.
Marc Teboulle regularly ties together related areas like Deblurring in his Algorithm studies.
Marc Teboulle has researched Convex optimization in several fields, including Linear matrix inequality, Quadratic programming and Duality.
Characterization is closely connected to Metric in his research, which is encompassed under the umbrella topic of Rate of convergence.
His Proximal Gradient Methods research is multidisciplinary, incorporating perspectives in Proximal gradient methods for learning, Subgradient method and Random coordinate descent.
He most often published in these fields:
- Mathematical optimization (61.54%)
- Convex optimization (38.46%)
- Rate of convergence (21.37%)
What were the highlights of his more recent work (between 2014-2020)?
- Mathematical optimization (61.54%)
- Rate of convergence (21.37%)
- Minification (11.11%)
In recent papers he was focusing on the following fields of study:
Marc Teboulle focuses on Mathematical optimization, Rate of convergence, Minification, Applied mathematics and Lipschitz continuity.
His work carried out in the field of Mathematical optimization brings together such families of science as Nonlinear system and Proximal Gradient Methods.
His studies in Rate of convergence integrate themes in fields like Fixed point, Iterated function, Metric, Sequence and Monotonic function.
His Minification study incorporates themes from Subsequence, Vector-valued function and Convex set.
His Lipschitz continuity research integrates issues from Bregman divergence and Lemma.
While the research belongs to areas of Bregman divergence, he spends his time largely on the problem of Convex function, intersecting his research to questions surrounding Random coordinate descent.
Between 2014 and 2020, his most popular works were:
- A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications (157 citations)
- First Order Methods Beyond Convexity and Lipschitz Gradient Continuity with Applications to Quadratic Inverse Problems (63 citations)
- A simplified view of first order methods for optimization (54 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Mathematical optimization
- Algorithm
His primary areas of study are Mathematical optimization, Minification, Applied mathematics, Focus and Numerical analysis.
His Mathematical optimization research is multidisciplinary, incorporating perspectives in Convex function, Proximal Gradient Methods and Feature.
Marc Teboulle combines subjects such as Subderivative and Random coordinate descent with his study of Convex function.
His study with Proximal Gradient Methods involves better knowledge in Convex optimization.
His Numerical analysis study frequently links to related topics such as Rate of convergence.
His Rate of convergence study combines topics from a wide range of disciplines, such as Euclidean space and Combinatorics.
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