Shrinking generalized gradients
Non-Linear Analysis
Constraint qualifications in quasidifferentiable optimization
Mathematical Programming: Series A and B
A generalized mean-value theorem and optimality conditions in composite nonsmooth minimization
Nonlinear Analysis: Theory, Methods & Applications
The Rise of Nonsmooth Analysis: Its Main Tools
Cybernetics and Systems Analysis
Necessary Optimality Conditions for Bilevel Optimization Problems Using Convexificators
Journal of Global Optimization
A (CF)-mapping of integral functional of locally lipschitz functions
Journal of Global Optimization
Convexificators and strong Kuhn-Tucker conditions
Computers & Mathematics with Applications
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Certain useful basic results of the gradient (in the smooth case), the Clarkesubdifferential, the Michel–Penot subdifferential, which is also known asthe "small" subdifferential, and the directional derivative(in the nonsmooth case) are stated and discussed. One of the advantages ofthe Michel–Penot subdifferential is the fact that it is in general "smaller"than the Clarke subdifferential. In this paper it is shown that there existsubdifferentials which may be smaller than the Michel–Penot subdifferentialandwhich have certain useful calculus. It isfurther shown that in the case of quasidifferentiability, the Michel–Penotsubdifferential enjoys calculus whichhold for the Clarke subdifferential only in the regular case.