3 citations · 5 across the 7 of their papers we have counts for
7 papers
Statistical inference for piecewise normal distributions and stochastic variational inequalities
Shu Lu, Hongsheng Liu
In this paper we first provide a method to compute confidence intervals for the center of a piecewise normal distribution given a sample from this distribution, under certain assum…
Minimization of a Class of Rare Event Probabilities and Buffer Probabilities of Exceedance
Amarjit Budhiraja, Shu Lu, Yang Yu +1
We consider the problem of choosing design parameters to minimize the probability of an undesired rare event that is described through the average of iid random variables. Sinc…
Convergence of the Augmented Decomposition Algorithm
Hongsheng Liu, Shu Lu
We study the convergence of the Augmented Decomposition Algorithm (ADA) proposed in [32] for solving multi-block separable convex minimization problems subject to linear constraint…
Self-concordant inclusions: A unified framework for path-following generalized Newton-type algorithms
Quoc Tran-Dinh, Tianxiao Sun, Shu Lu
We study a class of monotone inclusions called "self-concordant inclusion" which covers three fundamental convex optimization formulations as special cases. We develop a new genera…
Symmetric confidence regions and confidence intervals for normal map formulations of stochastic variational inequalities
Shu Lu
Stochastic variational inequalities (SVI) model a large class of equilibrium problems subject to data uncertainty, and are closely related to stochastic optimization problems. The…
Individual confidence intervals for true solutions to stochastic variational inequalities
Michael Lamm, Shu Lu, Amarjit Budhiraja
Stochastic variational inequalities (SVI) provide a means for modeling various optimization and equilibrium problems where data are subject to uncertainty. Often it is necessary to…