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Subdifferentials of compactly lipschitzian vector-valued functions

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/bf00932859openalex-snapshot
Analysis and optimization of Lipschitz continuous mappings
inferredcites →https://doi.org/10.1007/bf00935191openalex-snapshot
Necessary optimality criteria in mathematical programming in normed linear spaces
inferredcites →https://doi.org/10.1007/bf00967012openalex-snapshot
Subdifferentials of convex operators
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On the Strassen disintegration theorem
inferredcites →https://doi.org/10.1007/bf01584320openalex-snapshot
Optimization of lipschitz continuous functions
inferredcites →https://doi.org/10.1007/bfb0059132openalex-snapshot
Ordered linear spaces
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Convex Analysis and Measurable Multifunctions
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Generalized gradients of Lipschitz functionals
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Calcul sous-differentiel et optimisation
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A duality theorem for a convex programming problem in order complete vector lattices
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Generalized Gradients and Applications
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Semismooth and Semiconvex Functions in Constrained Optimization
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Regularity and Stability for Convex Multivalued Functions
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A New Approach to Lagrange Multipliers
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Convex Analysis
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On the inverse function theorem
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Topological Vector Spaces.
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Problème des inégalités. Applications à la programmation et au contrôle optimal
inferredcites →https://doi.org/10.2969/jmsj/00930281openalex-snapshot
Locally convex lattices.
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Sur les programmes convexes définis dans des espaces vectoriels topologiques
inferredcites →https://doi.org/10.7146/math.scand.a-11064openalex-snapshot
Sous-Différentiabilité de fonctions convexes à valeurs dans un espace vectoriel ordonné.
inferredcites →https://doi.org/10.7146/math.scand.a-11507openalex-snapshot
Subdifferentiability of Convex Functions with Values in an Ordered Vector Space.

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Limiting Subdifferentials
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The contingent and the paratingent as generalized derivatives for vector-valued and set-valued mappings
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Vector Optimization
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Generalized Newton’s method based on graphical derivatives
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DIRECTIONAL COMPACTNESS, SCALARIZATION AND NONSMOOTH SEMI-FREDHOLM MAPPINGS
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