The robust optimization methodology is known as a popular method dealing with optimization problems with uncertain data and hard constraints. This methodology has been applied so far to various convex conic optimization problems where only their inequality constraints are subject to uncertainty....

Many optimization algorithms require gradients of the model functions, but computing accurate gradients can be computationally expensive. We study the implications of using inexact gradients in the context of the multilevel optimization algorithm <Emphasis Type="SmallCaps">MG/Opt. <Emphasis Type="SmallCaps">MG/Opt recursively uses (typically...</emphasis></emphasis>

In this paper, the problem of flow maximization in pipeline systems for transmission of natural gas is addressed. We extend previously suggested models by incorporating the variation in pipeline flow capacities with gas specific gravity and compressibility. Flow capacities are modeled as...

Demand existing at client points in the plane for several products should be met. Products have to be assembled from different components obtainable at given prices at various sources with known production capacities. The optimal design of the resulting supply chain must be determined, including...

Policy makers constantly face optimal control problems: what controls allow to achieve certain targets in, e.g., GDP growth or inflation? Conventionally this is done by applying certain linear-quadratic optimization algorithms to dynamic econometric models. Several algorithms extend this...

This paper developed a wealth allocation framework for longevity risk protection under stochastic lifetime. By combining the dynamics of wealth evolution and health evolution in stochastic multi-period discrete-time models, an optimization problem was formulated with the objective of maximizing...