One approach to modeling and solving for economic equilibria relies on mathematical programming. These models solve for competitive equilibria. However, policy analysis often requires measuring the impacts of government price regulations that differ from the competitive equilibrium. In this paper we provide a unified framework for computing market equilibrium in mathematical programming models ...

Existing modeling languages lack the expressiveness or efficiency to support many modern and successful machine learning (ML) models such as structured prediction or matrix factorization. We present WOLFE, a probabilistic programming language that enables practitioners to develop such models. Most ML approaches can be formulated in terms of scalar objectives or scoring functions (such as distri...

This paper presents a review of advances that have taken place in the mathematical programming approach to process design and synthesis. A review is first presented on the algorithms that are available for solving MINLP problems, and its most recent variant, Generalized Disjunctive Programming models. The formulation of superstructures, models and solution strategies is also discussed for the e...

Mathematical programming is one of the areas to which fuq set theoq has been applied extensively. Primarily based on Bellman and Zadeh’s model of decision in fuzzy environments, models have been suggested which allow flexibility in constraints and nonlinear programming. Data envelopment analysis (DEA) is a method of evaluating relative efJiciencies for a group of similar units based on an efici...