HBR revives “maximizing shareholder value”—what Jack Welch called "the dumbest idea in the world"—treating customers as ...

Influence Maximization (IM) is a fundamental problem in network science with applications in viral marketing, information dissemination, cybersecurity, and epidemiology. Classical IM solvers often ...

Linear Programming has been used to solve optimization problems in banking, forestry, petroleum, and medical industries. Optimization can be completed with linear and non linear models. There are ...

Operations research professionals need the best linear programming software for Windows to solve optimization problems. Below we offer a tool that comes with all the essentials to help you perform a ...

Abstract: This paper considers a formulation of the robust adaptive beamforming (RAB) problem based on worst-case signal-to-interference-plus-noise ratio (SINR) maximization with a nonconvex ...

Mixed-Integer Linear Programming (MILP) plays an important role across a range of scientific disciplines and within areas of strategic importance to society. The MILP problems, however, suffer from ...

Abstract: This article proposes a second-order conic programming (SOCP) approach to solve distributionally robust two-stage linear programs over 1-Wasserstein balls. We start from the case with ...

Although plant proteins are often considered to have less nutritional quality because of their suboptimal amino acid (AA) content, the wide variety of their sources, both conventional and emerging, ...

This paper explores the mathematics behind optimal portfolio construction when relative utility and risk are considered together in a general sense. I derive the portfolio optimization problems when ...

In this work, a new method is presented for determining the binding constraints of a general linear maximization problem. The new method uses only objective function values at points which are ...