We consider a class of smoothing methods for minimization problems where the feasible set
is convex but the objective function is not convex, not differentiable and perhaps not even …

Stochastic approximation methods have been extensively studied in the literature for solving
systems of stochastic equations and stochastic optimization problems where function values …

Uncertainty Quantification (UQ) is an emerging and extremely active research discipline
which aims to quantitatively treat any uncertainty in applied models. The primary objective of …

Variational inequality modeling, analysis and computations are important for many
applications, but much of the subject has been developed in a deterministic setting with no …

The concept of a stochastic variational inequality has recently been articulated in a new way
that is able to cover, in particular, the optimality conditions for a multistage stochastic …

The stochastic variational inequality (VI) has been used widely in engineering and
economics as an effective mathematical model for a number of equilibrium problems …

We consider the stochastic linear complementarity problem (SLCP) involving a random
matrix whose expectation matrix is positive semi-definite. We show that the expected …

We study the problem of learning a single neuron with respect to the $ L_2^ 2$-loss in the
presence of adversarial label noise. We give an efficient algorithm that, for a broad family of …

In this paper, we propose a discretization scheme for the two-stage stochastic linear
complementarity problem (LCP) where the underlying random data are continuously …

Trajectory optimization with contact-rich behaviors has recently gained attention for
generating diverse locomotion behaviors without pre-specified ground contact sequences …