Robust optimization and stochastic optimization are the two main paradigms for dealing with
the uncertainty inherent in almost all real-world optimization problems. The core principle of …

Quadratic optimization (QO) has been studied extensively in the literature due to its
applicability in many practical problems. While practical, it is known that QO problems are …

In this paper, we propose a new global optimization approach for solving nonconvex
optimization problems in which the nonconvex components are sums of products of convex …

We consider the class of disjoint bilinear programs max {x T y∣ x∈ X, y∈ Y} where X and Y
are packing polytopes. We present an O (log log m 1 log m 1 log log m 2 log m 2) …

Adjustable robust minimization problems where the objective or constraints depend in a
convex way on the adjustable variables are generally difficult to solve. In this paper, we …

We introduce a novel Reformulation-Perspectification Technique (RPT) to obtain convex
approximations of nonconvex continuous optimization problems. RPT consists of two steps …

We propose a new robust actionable prescriptive analytics framework that leverages past
data and side information to minimize a risk-based objective function under distributional …

We investigate the concept of adjustability—the difference in objective values between two
types of dynamic robust optimization formulations: one where (static) decisions are made …

We formalize the concept of Pareto Adaptive Robust Optimality (PARO) for linear two-stage
Adaptive Robust Optimization (ARO) problems, with fixed continuous recourse. A worst-case …

Environmental variables are widely recognized as a cause of differences in efficiency
measurement. However, the existing literature on data envelopment analysis (DEA) in …