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Optimality conditions for non-finite valued convex composite functions
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Practical Methods of Optimization
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Descent methods for composite nondifferentiable optimization problems
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Second order necessary and sufficient conditions for convex composite NDO
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Nonlinear approximation problems in vector norms
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A model algorithm for composite nondifferentiable optimization problems
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The Fritz John necessary optimality conditions in the presence of equality and inequality constraints
inferredcites →https://doi.org/10.1016/0362-546x(85)90012-4openalex-snapshot
Extensions of subgradient calculus with applications to optimization
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Practical methods of optimization
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First- and Second-Order Epi-Differentiability in Nonlinear Programming
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Directionally Lipschitzian Functions and Subdifferential Calculus
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Necessary and Sufficient Conditions for a Local Minimum. 2: Conditions of Levitin–Miljutin–Osmolovskii Type
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An Exact Penalization Viewpoint of Constrained Optimization
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Optimization and Nonsmooth Analysis
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Second-Order Optimality Conditions in Nonlinear Programming Obtained by Way of Epi-Derivatives
inferredcites →https://doi.org/10.4153/cjm-1990-027-2openalex-snapshot
Proto-Differentiation of Subgradient Set-Valued Mappings
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Iteration Functions in Some Nonsmooth Optimization Algorithms
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Second-Order Analysis of Penalty Function
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Augmented Lagrangian Duality for Composite Optimization Problems
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First- and Second-Order Necessary Conditions Via Exact Penalty Functions
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Convergence of Linearized Proximal Algorithms with Adaptive Stepsizes for Convex Composite Optimization
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