Over the past two decades, there has been explosive growth in the application of robust
optimization in operations management (robust OM), fueled by both significant advances in …

In portfolio optimization, the computational complexity of implementing almost stochastic
dominance has limited its practical applications. In this study, we introduce an optimization …

Optimization with stochastic dominance constraints has recently received an increasing
amount of attention in the quantitative risk management literature. Instead of requiring that …

It is a challenging problem to make the team of unmanned aerial vehicles perform well to
gather up-to-date situational awareness in dynamic environments. To solve the challenge, in …

Abstract The CoMirror algorithm, by Beck et al.(Oper Res Lett 38 (6): 493–498, 2010), is
designed to solve convex optimization problems with one functional constraint. At each …

This paper considers an inexact primal-dual algorithm for semi-infinite programming (SIP)
for which it provides general error bounds. We create a new prox function for nonnegative …

We consider a distributionally robust second-order stochastic dominance constrained
optimization problem. We require the dominance constraints to hold with respect to all …

We consider a distributionally robust second-order stochastic dominance constrained
optimization problem, where the true distribution of the uncertain parameters is ambiguous …

In this article, we address the online resource allocation problem in service queuing systems
under uncertainty. In particular, the optimal control policy is derived by using the real-time …

We propose a new randomized algorithm for solving convex optimization problems that
have a large number of constraints (with high probability). Existing methods like interior …