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 ...

Neel Somani points out that while artificial intelligence may look like it runs on data and algorithms, its real engine is ...

Abstract. We consider sensitivity functionals and Lagrange multiplier method for solving finite dimensional convex optimization problem.An analysis based on this property is also applied for ...

Conventional quantum algorithms are not feasible for solving combinatorial optimization problems (COPs) with constraints in the operation time of quantum computers. To address this issue, researchers ...

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 ...

Bio-inspired computational methods have gained popularity recently. These methods mimic the seemingly complex behavior of ...

The original version of this story appeared in Quanta Magazine. For computer scientists, solving problems is a bit like mountaineering. First they must choose a problem to solve—akin to identifying a ...

This course examines formulation and solution of applicable optimization models, including linear, integer, nonlinear, and network problems, efficient algorithm methods, and use of computer modeling ...