What is he best known for?
The fields of study he is best known for:
- 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.
His most cited work include:
- 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)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Mathematical optimization (69.23%)
- Sequential quadratic programming (24.79%)
- Rate of convergence (23.08%)
What were the highlights of his more recent work (between 2014-2021)?
- Mathematical optimization (69.23%)
- Local convergence (17.09%)
- Sequential quadratic programming (24.79%)
In recent papers he was focusing on the following fields of study:
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.
Between 2014 and 2021, his most popular works were:
- 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)
In his most recent research, the most cited papers focused on:
- 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.
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