Approximate linear programs (ALPs) are well-known models based on value function approximations (VFAs) to obtain policies and lower bounds on the optimal policy cost of discounted-cost Markov decision processes (MDPs). Formulating an ALP requires (i) basis functions, the linear combination of...

We study the problem of optimal trade execution in an illiquid market by minimizing the coherent dynamic risk of the implementation shortfall. The prices of the assets are modeled as a discrete-time Markov process perturbed by both temporal and permanent impacts related to the trading volume. A...

Stochastic convex optimization problems with expectation constraints (SOECs) are encountered in statistics and machine learning, business, and engineering. In data-rich environments, the SOEC objective and constraints contain expectations defined with respect to large datasets. Therefore,...

Approximate linear programs (ALPs) are well-known models for computing value function approximations (VFAs) of intractable Markov decision processes (MDPs). VFAs from ALPs have desirable theoretical properties, define an operating policy, and provide a lower bound on the optimal policy cost....

The received wisdom is that cheaper foreign inputs may replace tasks previously done by domestic labor, and cause displacement of workers at the home country. However, using the U.S. multinational enterprises data, the empirical evidence in this paper does not support the idea that the imported...