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Benar Fux Svaiter
Researcher at Instituto Nacional de Matemática Pura e Aplicada
Publications - 175
Citations - 9330
Benar Fux Svaiter is an academic researcher from Instituto Nacional de Matemática Pura e Aplicada. The author has contributed to research in topics: Monotone polygon & Convex function. The author has an hindex of 44, co-authored 174 publications receiving 8148 citations. Previous affiliations of Benar Fux Svaiter include National Council for Scientific and Technological Development.
Papers
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Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods
TL;DR: This work proves an abstract convergence result for descent methods satisfying a sufficient-decrease assumption, and allowing a relative error tolerance, that guarantees the convergence of bounded sequences under the assumption that the function f satisfies the Kurdyka–Łojasiewicz inequality.
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Steepest descent methods for multicriteria optimization
Jörg Fliege,Benar Fux Svaiter +1 more
TL;DR: A steepest descent method for unconstrained multicriteria optimization and a “feasible descent direction” method for the constrained case, both of which converge to a point satisfying certain first-order necessary conditions for Pareto optimality.
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A New Projection Method for Variational Inequality Problems
TL;DR: In this paper, the authors proposed a projection algorithm for solving the variational inequality problem, where the underlying function is continuous and satisfies a certain generalized monotonicity assumption (e.g., it can be pseudomonotone).
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Forcing strong convergence of proximal point iterations in a Hilbert space
TL;DR: In this article, a proximal-point algorithm for finding zeros of maximal monotone operators in an infinite-dimensional Hilbert space is proposed, which converges strongly to a solution provided the problem has a solution.
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Newton's Method for Multiobjective Optimization
TL;DR: An extension of Newton's method for unconstrained multiobjective optimization (multicriteria optimization) that is locally superlinear convergent to optimal points and uses a Kantorovich-like technique.