We investigate risk-averse stochastic optimization problems with a risk-shaping constraint in the form of a stochastic-order relation. Both univariate and multivariate orders are considered. We extend ...

We might be witnessing the start of a new computing era where AI, cloud and quantum begin to converge in ways that redefine ...

A framework based on advanced AI techniques can solve complex, computationally intensive problems faster and in a more more scalable way than state-of-the-art methods, according to a new study. A ...

The paper intends to give a survey on some important aspects of optimization problems with infinitely many constraints. We consider the structure of the problem, optimality conditions, Newton methods ...

where \(\mathsf{G}(\cdot)\) is some convex operator and \(\mathcal{F}\) is as set of feasible input distributions. Examples of such an optimization problem include finding capacity in information ...

There’s a lot of excitement around exascale-class supercomputing and the possibilities of quantum computing, but there’s an emerging alternative advanced computing paradigm that transcends the limits ...

Cambridge, MA – Managing a power grid is like trying to solve an enormous puzzle. Grid operators must ensure the proper amount of power is flowing to the right areas at the exact time when it is ...

The advantage of partial outer convexification, which was first used in the field of optimal control with ordinary differential equations, is that the problem can be split into a nonlinear dynamic ...

Traffic modeling has been of interest to mathematicians since the 1950s. Research in the area has only grown as road traffic control presents an ever-increasing problem. Generally, models for traffic ...

Over the course of my 25-year career in the mathematical optimization software industry, I’ve lost count of how many times I’ve been asked this question: “Can you tell me what mathematical ...