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Nondifferential optimization via adaptive smoothing

https://doi.org/10.1007/bf00935008
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What this work says

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

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Stochastic Approximation Methods for Constrained and Unconstrained Systems
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Optimization of lipschitz continuous functions
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Algorithms for some minimax problems
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Generalized Gradients and Applications
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Theoretical and computational aspects of the optimal design centering, tolerancing, and tuning problem
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IEEE Transactions on Circuits and Systems
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An Adaptive Precision Gradient Method for Optimal Control
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A Descent Numerical Method for Optimization Problems with Nondifferentiable Cost Functionals
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Semismooth and Semiconvex Functions in Constrained Optimization
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On the Extension of Constrained Optimization Algorithms from Differentiable to Nondifferentiable Problems

What points at this work

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

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Constrained Global Optimization by Smoothing
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Perturbed steepest-descent technique in multiextremal problems
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Complexity guarantees for an implicit smoothing-enabled method for stochastic MPECs
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Error bounds of two smoothing approximations for semi-infinite minimax problems
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A spline smoothing Newton method for finite minimax problems
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Implicit Smoothing and Its Application to Optimization with Piecewise Smooth Equality Constraints1
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An Effective Nonsmooth Optimization Algorithm for Locally Lipschitz Functions
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A truncated aggregate smoothing Newton method for minimax problems
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Solving variational inequality problems via smoothing-nonsmooth reformulations
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On nonsmooth and discontinuous problems of stochastic systems optimization
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Adaptive Smoothing Method, Deterministically Computable Generalized Jacobians, and the Newton Method
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Approximate Newton Methods for Nonsmooth Equations
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Error Stability Properties of Generalized Gradient-Type Algorithms
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Parallel Versions of the Modified Coordinate and Gradient Descent Methods and Their Application to a Class of Global Optimization Problems
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Solution of Nonconvex Nonsmooth Stochastic Optimization Problems
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Algorithms with Adaptive Smoothing for Finite Minimax Problems
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Portfolio reshaping under 1st-order stochastic dominance constraints by the exact penalty function methods
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David Quinn Mayne. 23 April 1930—27 May 2024
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Zeroth-order randomized block methods for constrained minimization of expectation-valued Lipschitz continuous functions
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Iterative Methods for Optimization
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Zeroth-Order Gradient and Quasi-Newton Methods for Nonsmooth Nonconvex Stochastic Optimization
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The Gradient’s Limit of a Definable Family of Functions Admits a Variational Stratification
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The Minimization of Semicontinuous Functions: Mollifier Subgradients
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An Adaptive Smoothing Method for Continuous Minimax Problems
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A Spline Smoothing Newton Method for L∞ Distance Regression with Bound Constraints
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An Active Set Smoothing Method for Solving Unconstrained Minimax Problems
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