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Optimization of upper semidifferentiable functions

https://doi.org/10.1007/bf00938396
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What this work says

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

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An analog of the method of linearization in problems of minimizing nondifferentiable functions
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Mathematical programming study 2
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Optimization of lipschitz continuous functions
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A method of conjugate subgradients for minimizing nondifferentiable functions
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On the variational principle
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Algorithms for some minimax problems
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Generalized Gradients and Applications
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A Descent Numerical Method for Optimization Problems with Nondifferentiable Cost Functionals
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Convergence Conditions for Ascent Methods
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A New Approach to Lagrange Multipliers
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An Algorithm for Constrained Optimization with Semismooth Functions
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Convex Analysis
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Conjugate-Gradient Methods for Large-Scale Nonlinear Optimization.
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The variational principle

What points at this work

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

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Diagonal Bundle Method for Nonsmooth Sparse Optimization
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Comparing Nonsmooth Nonconvex Bundle Methods in Solving Hemivariational Inequalities
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