TL;DR: In this paper, a quadrangle of risk is developed to reconcile risk measures and expected utility, thereby reconciling those two approaches to optimisation under uncertainty, and a direct correspondence can be identified between measures of error and measures of regret.
TL;DR: In this paper, the authors present suggestions for appropriate use of state-of-the-art optimizers and guidelines for careful formulation, both of which can vastly improve performance of mixed integer programs.
TL;DR: Suggestions for diagnosing and removing performance problems in state-of-the-art linear programming solvers, and guidelines for careful model formulation, both of which can vastly improve performance are presented.