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Implicit multifunction theorems for the sensitivity analysis of variational conditions

https://doi.org/10.1007/bf02592203
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Proto-Derivatives and the Geometry of Solution Mappings in Nonlinear Programming
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Nonlinear Optimization Problems under Data Perturbations
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Characterizations of Lipschitz Stability in Optimization
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Local analysis of Newton-type methods for variational inequalities and nonlinear programming
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Sensitivity analysis for nonsmooth generalized equations
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A degree-theoretic approach to parametric nonsmooth equations with multivalued perturbed solution sets
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Implicit function theorems for generalized equations
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Sensitivity analysis in nonlinear programming under second order assumptions
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Nonsmooth analysis and parametric optimization
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Local structure of feasible sets in nonlinear programming, part I: Regularity
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Generalized equations and their solutions, Part I: Basic theory
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Generalized equations and their solutions, part II: Applications to nonlinear programming
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Local structure of feasible sets in nonlinear programming, Part III: Stability and sensitivity
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Variational conditions and the proto-differentiation of partial subgradient mappings
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Mathematical Programming
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Proto-Differentiability of Set-Valued Mappings and its Applications in Optimization
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Sensitivity Analysis of Solutions to Generalized Equations
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Sensitivity Analysis of Nonlinear Programs and Differentiability Properties of Metric Projections
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Lipschitzian Stability in Nonlinear Control and Optimization
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Sensitivity of Solutions in Nonlinear Programming Problems with Nonunique Multipliers
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Necessary and Sufficient Conditions for Solution Stability of Parametric Nonsmooth Equations
inferredcites →https://doi.org/10.1287/moor.16.2.292openalex-snapshot
An Implicit-Function Theorem for a Class of Nonsmooth Functions
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Sensitivity Analysis for Variational Inequalities
inferredcites →https://doi.org/10.1287/moor.17.2.341openalex-snapshot
Parametric Variational Inequalities with Multivalued Solution Sets
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Implicit Functions, Lipschitz Maps, and Stability in Optimization
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Stability Analysis of Variational Inequalities and Nonlinear Complementarity Problems, via the Mixed Linear Complementarity Problem and Degree Theory
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Strongly Regular Generalized Equations

What points at this work

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Lipschitzian Stability of Newton’s Method for Variational Inclusions
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