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- Mikhail V. Solodov

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
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disciplines.
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
33
Citations
5,578
116
World Ranking
2180
National Ranking
19

- Mathematical analysis
- Mathematical optimization
- Algebra

Mikhail V. Solodov mainly focuses on Mathematical optimization, Algorithm, Solution set, Hilbert space and Constrained optimization. As a member of one scientific family, Mikhail V. Solodov mostly works in the field of Mathematical optimization, focusing on Stationary point and, on occasion, Limit point. In his study, which falls under the umbrella issue of Algorithm, Proximal point is strongly linked to Monotonic function.

His Hilbert space study combines topics in areas such as Rate of convergence and Weak convergence. His research investigates the connection with Constrained optimization and areas like Penalty method which intersect with concerns in Linear matrix inequality. His Variational inequality study combines topics from a wide range of disciplines, such as Function and Dykstra's projection algorithm.

- A New Projection Method for Variational Inequality Problems (356 citations)
- Forcing strong convergence of proximal point iterations in a Hilbert space (296 citations)
- A HYBRID APPROXIMATE EXTRAGRADIENT - PROXIMAL POINT ALGORITHM USING THE ENLARGEMENT OF A MAXIMAL MONOTONE OPERATOR (209 citations)

Mathematical optimization, Sequential quadratic programming, Rate of convergence, Newton's method and Optimization problem are his primary areas of study. His Mathematical optimization and Constrained optimization, Local convergence, Quadratic programming, Variational inequality and Lagrange multiplier investigations all form part of his Mathematical optimization research activities. His studies deal with areas such as Penalty method and Interior point method as well as Sequential quadratic programming.

His study looks at the intersection of Rate of convergence and topics like Algorithm with Monotonic function, Class and Zero. His Newton's method research includes elements of Iterated function, Local algorithm and System of linear equations. The various areas that Mikhail V. Solodov examines in his Optimization problem study include Lagrangian relaxation, Nonlinear programming and Applied mathematics.

- Mathematical optimization (69.23%)
- Sequential quadratic programming (24.79%)
- Rate of convergence (23.08%)

- Mathematical optimization (69.23%)
- Local convergence (17.09%)
- Sequential quadratic programming (24.79%)

Mikhail V. Solodov focuses on Mathematical optimization, Local convergence, Sequential quadratic programming, Lagrange multiplier and Rate of convergence. Mikhail V. Solodov integrates many fields, such as Mathematical optimization and Multiplier, in his works. His Local convergence study integrates concerns from other disciplines, such as Constrained optimization, Newton's method and Augmented Lagrangian method.

His biological study spans a wide range of topics, including Lagrangian relaxation and Quadratic growth. His Newton's method research integrates issues from Iterated function, Numerical analysis, Mathematical analysis and Applied mathematics. His work carried out in the field of Rate of convergence brings together such families of science as Generalized nash equilibrium, Reduction and Merit function.

- A proximal bundle method for nonsmooth nonconvex functions with inexact information (47 citations)
- A doubly stabilized bundle method for nonsmooth convex optimization (26 citations)
- Rejoinder on: Critical Lagrange multipliers: what we currently know about them, how they spoil our lives, and what we can do about it (20 citations)

- Mathematical analysis
- Mathematical optimization
- Algebra

Mikhail V. Solodov mostly deals with Mathematical optimization, Local convergence, Lagrange multiplier, Rate of convergence and Sequential quadratic programming. Mikhail V. Solodov has researched Mathematical optimization in several fields, including Critical point and Newton's method. His Rate of convergence research includes themes of Penalty method, Quadratic programming and Stationary point.

His Sequential quadratic programming research is multidisciplinary, relying on both Algorithm, Type and Unit. In Subgradient method, Mikhail V. Solodov works on issues like Bundle, which are connected to Iterated function. His study in Optimization problem is interdisciplinary in nature, drawing from both Almost surely, Differentiable function and Solver.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

A New Projection Method for Variational Inequality Problems

M. V. Solodov;B. F. Svaiter.

Siam Journal on Control and Optimization **(1999)**

619 Citations

A New Projection Method for Variational Inequality Problems

M. V. Solodov;B. F. Svaiter.

Siam Journal on Control and Optimization **(1999)**

619 Citations

Forcing strong convergence of proximal point iterations in a Hilbert space

Mikhail V. Solodov;Benar Fux Svaiter.

Mathematical Programming **(2000)**

449 Citations

Forcing strong convergence of proximal point iterations in a Hilbert space

Mikhail V. Solodov;Benar Fux Svaiter.

Mathematical Programming **(2000)**

449 Citations

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

M. V. Solodov;B. F. Svaiter.

Set-valued Analysis **(1999)**

337 Citations

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

M. V. Solodov;B. F. Svaiter.

Set-valued Analysis **(1999)**

337 Citations

A hybrid projection-proximal point algorithm.

M. V. Solodov;B. F. Svaiter.

Journal of Convex Analysis **(1998)**

294 Citations

A hybrid projection-proximal point algorithm.

M. V. Solodov;B. F. Svaiter.

Journal of Convex Analysis **(1998)**

294 Citations

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

M. V. Solodov;B. F. Svaiter.

Mathematics of Operations Research **(2000)**

267 Citations

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

M. V. Solodov;B. F. Svaiter.

Mathematics of Operations Research **(2000)**

267 Citations

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