What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Quantum mechanics
- Geometry
Benar Fux Svaiter focuses on Mathematical optimization, Monotone polygon, Algorithm, Variational inequality and Solution set.
His Mathematical optimization research incorporates themes from Proximal Gradient Methods, Convex optimization and Lipschitz continuity.
Benar Fux Svaiter works mostly in the field of Monotone polygon, limiting it down to concerns involving Discrete mathematics and, occasionally, Zero.
His Algorithm research includes themes of Rate of convergence and Monotonic function.
His studies examine the connections between Variational inequality and genetics, as well as such issues in Feasible region, with regards to Constant, Function and Dykstra's projection algorithm.
The Mathematical analysis study which covers Pure mathematics that intersects with Newton's method.
His most cited work include:
- Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods (716 citations)
- A New Projection Method for Variational Inequality Problems (356 citations)
- Steepest descent methods for multicriteria optimization (316 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Monotone polygon, Mathematical optimization, Applied mathematics, Mathematical analysis and Discrete mathematics.
His Monotone polygon research incorporates elements of Banach space, Pure mathematics, Combinatorics, Variational inequality and Monotonic function.
He has researched Variational inequality in several fields, including Bounded function and Feasible region.
His Mathematical optimization study integrates concerns from other disciplines, such as Convex optimization, Proximal Gradient Methods, Algorithm and Iterated function.
In his study, Rate of convergence and Lipschitz continuity is strongly linked to Newton's method, which falls under the umbrella field of Mathematical analysis.
The concepts of his Discrete mathematics study are interwoven with issues in Strongly monotone, Pointwise and Subderivative.
He most often published in these fields:
- Monotone polygon (29.31%)
- Mathematical optimization (24.71%)
- Applied mathematics (20.69%)
What were the highlights of his more recent work (between 2012-2020)?
- Applied mathematics (20.69%)
- Monotone polygon (29.31%)
- Discrete mathematics (16.67%)
In recent papers he was focusing on the following fields of study:
Benar Fux Svaiter mainly investigates Applied mathematics, Monotone polygon, Discrete mathematics, Convex optimization and Mathematical optimization.
His study in Applied mathematics is interdisciplinary in nature, drawing from both Regularization, Mathematical analysis, Lipschitz continuity and Nonlinear system.
The Monotone polygon study combines topics in areas such as Weak convergence, Zero, Hilbert space, Ergodic theory and Newton's method.
In his study, Rate of convergence and Pure mathematics is inextricably linked to Subderivative, which falls within the broad field of Weak convergence.
His research integrates issues of Pointwise, Monotonic function and Maximal function in his study of Discrete mathematics.
His Mathematical optimization research incorporates elements of Algorithm, Iterated function, Quadratic equation and Proximal Gradient Methods.
Between 2012 and 2020, his most popular works were:
- Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods (716 citations)
- ITERATION-COMPLEXITY OF BLOCK-DECOMPOSITION ALGORITHMS AND THE ALTERNATING DIRECTION METHOD OF MULTIPLIERS ∗ (163 citations)
- An Accelerated Hybrid Proximal Extragradient Method for Convex Optimization and Its Implications to Second-Order Methods (92 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Quantum mechanics
- Geometry
His primary scientific interests are in Convex optimization, Monotone polygon, Algorithm, Mathematical optimization and Mathematical analysis.
His Convex optimization study deals with Conic section intersecting with Differentiable function.
His studies in Monotone polygon integrate themes in fields like Ergodic theory, Rate of convergence and Hilbert space.
His work deals with themes such as Approximations of π, Acceleration and Resolvent, which intersect with Algorithm.
His Mathematical optimization study frequently draws connections to adjacent fields such as Proximal Gradient Methods.
His Mathematical analysis research includes themes of Regularization, Newton's method, Scalar and Applied mathematics.
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