Results 41 to 50 of about 162 (63)

Upper Subderivatives and Generalized Gradients of the Marginal Function of a Non-Lipschitzian Program

Annals of Operations Research, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Doug Ward, G.M. Lee
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Derivatives and subderivatives of buffered probability of exceedance

Operations Research Letters, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tong Zhang, Stan Uryasev, Yongpei Guan
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Lagrange multipliers and subderivatives of optimal value functions in nonlinear programming

, 1982
For finite-dimensional optimization problems with locally Lipschitzian equality and inequality constraints and also an abstract constraint described by a closed set, a Lagrange multiplier rule is derived that is sharper is in some respects than the ones of Clarke and Hiriart-Urruty.
R. T. Rockafellar
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Towards Subderivative-Based Zeroing Neural Networks

, 2023
Predrag S. Stanimirović, Dimitrios Gerontitis, V. N. Krutikov, Lev Kazakovtsev   +3 more
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Subderivative

, 2013
Saul I. Gass, Michael C. Fu
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Subderivatives and Subgradients

, 1998

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Role of Subgradients in Variational Analysis of Polyhedral Functions

Journal of Optimization Theory and Applications, 2022
Understanding the role that subgradients play in various second-order variational analysis constructions can help us uncover new properties of important classes of functions in variational analysis.
N. T. V. Hang, Woosuk L. Jung, Ebrahim Sarabi   +2 more
semanticscholar   +1 more source

Local optimality for stationary points of group zero-norm regularized problems and equivalent surrogates

Optimization, 2022
This paper focuses on the local optimality for the stationary points of the composite group zero-norm regularized problem and its equivalent surrogates.
S. Pan, Ling Liang, Yulan Liu
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Convergence of the Gradient Sampling Algorithm on Directionally Lipschitz Functions

Set-Valued and Variational Analysis, 2021
The convergence theory for the gradient sampling algorithm is extended to directionally Lipschitz functions. Although directionally Lipschitz functions are not necessarily locally Lipschitz, they are almost everywhere differentiable and well approximated
J. Burke, Q. Lin
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Upper Semismooth Functions and the Subdifferential Determination Property

Set-Valued and Variational Analysis, 2017
An upper semismooth function is a lower semicontinuous function whose radial subderivative satisfies a mild directional upper semicontinuity property. Examples of upper semismooth functions are the proper lower semicontinuous convex functions, the lower ...
Marc Lassonde
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