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Mathematics

D-Index
40
Citations
7338
World Ranking
2038
National Ranking
15

Overview

Mikhail V. Solodov is affiliated with the Instituto Nacional de Matemática Pura e Aplicada in Brazil. The research work is situated primarily in the fields of Mathematics and Engineering, with a particular focus on Numerical Analysis, Computational Theory and Mathematics, Computational Mechanics, Management Science and Operations Research, and Electrical and Electronic Engineering.

Their research covers a broad range of topics, prominently including Advanced Optimization Algorithms Research, Optimization and Variational Analysis, Iterative Methods for Nonlinear Equations, Risk and Portfolio Optimization, Sparse and Compressive Sensing Techniques, Electric Power System Optimization, and Matrix Theory and Algorithms.

Frequent coauthors collaborating with Mikhail V. Solodov include:

  • A. F. Izmailov
  • Claudia Sagastizábal
  • Pedro Augusto Pereira Borges
  • Andreas Fischer
  • Felipe Atenas

Notable publication venues where this researcher has frequently published are:

  • Computational Optimization and Applications
  • SIAM Journal on Optimization
  • Mathematical Programming
  • Journal of Optimization Theory and Applications
  • Set-Valued and Variational Analysis

Key recent papers authored or co-authored by Mikhail V. Solodov include:

  • The Levenberg-Marquardt method: an overview of modern convergence theories and more (2024), published in Computational Optimization and Applications
  • A regularized smoothing method for fully parameterized convex problems with applications to convex and nonconvex two-stage stochastic programming (2020), published in Mathematical Programming
  • Unit stepsize for the Newton method close to critical solutions (2020), published in Mathematical Programming
  • Accelerating convergence of the globalized Newton method to critical solutions of nonlinear equations (2020), published in Computational Optimization and Applications
  • A Unified Analysis of Descent Sequences in Weakly Convex Optimization, Including Convergence Rates for Bundle Methods (2023), published in SIAM Journal on Optimization

Best Publications

  • A New Projection Method for Variational Inequality Problems

    M. V. Solodov;B. F. Svaiter

  • Forcing strong convergence of proximal point iterations in a Hilbert space

    Mikhail V. Solodov;Benar Fux Svaiter

  • Modified Projection-Type Methods for Monotone Variational Inequalities

    Michael V. Solodov;Paul Tseng

  • A HYBRID APPROXIMATE EXTRAGRADIENT - PROXIMAL POINT ALGORITHM USING THE ENLARGEMENT OF A MAXIMAL MONOTONE OPERATOR

    M. V. Solodov;B. F. Svaiter

  • A hybrid projection-proximal point algorithm.

    M. V. Solodov;B. F. Svaiter

  • An Inexact Hybrid Generalized Proximal Point Algorithm and Some New Results on the Theory of Bregman Functions

    M. V. Solodov;B. F. Svaiter

  • Nonlinear complementarity as unconstrained and constrained minimization

    O. L. Mangasarian;M. V. Solodov

  • Newton-Type Methods for Optimization and Variational Problems

    Alexey F. Izmailov;Mikhail V. Solodov

  • On the projected subgradient method for nonsmooth convex optimization in a Hilbert space

    Ya. I. Alber;A. N. Iusem;M. V. Solodov

  • A UNIFIED FRAMEWORK FOR SOME INEXACT PROXIMAL POINT ALGORITHMS

    M. V. Solodov;B. F. Svaiter

  • Incremental Gradient Algorithms with Stepsizes Bounded Away from Zero

    M. V. Solodov

  • Serial and parallel backpropagation convergence via nonmonotone perturbed minimization

    O.L. Mangasarian;M.V. Solodov

  • Error bounds for proximal point subproblems and associated inexact proximal point algorithms

    Mikhail V. Solodov;Benar Fux Svaiter

  • Error stability properties of generalized gradient-type algorithms

    M. V. Solodov;S. K. Zavries

  • An Infeasible Bundle Method for Nonsmooth Convex Constrained Optimization without a Penalty Function or a Filter

    Claudia Sagastizábal;Mikhail Solodov

  • Local Convergence of Exact and Inexact Augmented Lagrangian Methods under the Second-Order Sufficient Optimality Condition

    Damián R. Fernández;Mikhail V. Solodov

  • A bundle-filter method for nonsmooth convex constrained optimization

    Elizabeth Karas;Ademir Ribeiro;Claudia Sagastizábal;Mikhail Solodov

  • Stabilized SQP revisited

    A. F. Izmailov;M. V. Solodov

  • A proximal bundle method for nonsmooth nonconvex functions with inexact information

    W. Hare;C. Sagastizábal;M. Solodov

  • Mathematical Programs with Vanishing Constraints: Optimality Conditions, Sensitivity, and a Relaxation Method

    Alexey F. Izmailov;Mikhail V. Solodov

Frequent Co-Authors

Claudia Sagastizábal
Claudia Sagastizábal State University of Campinas
Benar Fux Svaiter
Benar Fux Svaiter Instituto Nacional de Matemática Pura e Aplicada
Olvi L. Mangasarian
Olvi L. Mangasarian University of Wisconsin–Madison
Michael C. Ferris
Michael C. Ferris University of Wisconsin–Madison
Alfredo N. Iusem
Alfredo N. Iusem Fundação Getulio Vargas

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