We consider the problem of online pricing with offline data, where the decision maker has in hand pre-existed offline data and then takes online sequential actions to maximize expected cumulative revenue as well as learning the optimal pricing. We focus on a simple and specific problem setting...

We build a new unified modeling and analysis framework for a broad class of online matching problems. The proposed unified framework encompasses a number of classical online matching problems and accommodates three practical features: reusable resources, network resources and decaying rewards....

We propose gradient-based simulation-optimization algorithms to optimize systems that have complicated stochastic structure. The presence of complicated stochastic structure, such as the involvement of infinite-dimensional continuous-time stochastic processes, may cause the exact simulation of...

We study the problem of simulating a class of nonstationary spatio-temporal Poisson processes. The Poisson intensity function is non-stationary and piecewise linear in both the time dimension and the spatial location dimensions. We propose an exact simulation algorithm based on the inversion...

We propose a new approach to modeling informed order flows. Our approach generalizes arbitrage pricing to incorporate both information and demand effects, giving rise to a new quadratic factor model of price impacts. Our approach offers two fresh insights into the economics of informed trading....