A nonsmooth version of Newton's method
Mathematical Programming: Series A and B
Superlinearly convergent approximate Newton methods for LC1 optimization problems
Mathematical Programming: Series A and B
A globally convergent Newton method for convex SC1minimization problems
Journal of Optimization Theory and Applications
Journal of Optimization Theory and Applications
A New Merit Function For Nonlinear Complementarity Problems And A Related Algorithm
SIAM Journal on Optimization
Differentiability and semismoothness properties of integral functions and their applications
Mathematical Programming: Series A and B
Expected Residual Minimization Method for Stochastic Linear Complementarity Problems
Mathematics of Operations Research
Stochastic $R_0$ Matrix Linear Complementarity Problems
SIAM Journal on Optimization
Robust solution of monotone stochastic linear complementarity problems
Mathematical Programming: Series A and B
Minimization of SC1 functions and the Maratos effect
Operations Research Letters
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The stochastic nonlinear complementarity problem has been recently reformulated as an expected residual minimization problem which minimizes an expected residual function defined by an NCP function. In this work, we show that the expected residual function defined by the Fischer-Burmeister function is an SC^1 function.