Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such pr
Robust design—that is, managing design uncertainties such as model uncertainty or parametric uncertainty—is the often unpleasant issue crucial in much multidisciplinary optimal design work. Recently,
Robust Optimization is a method to improve robustness using low-cost variations of a single, conceptual design. The benefits of Robust Optimization include faster product development cycles; faster la
This book introduces methods of robust optimization in multivariate adaptive regression splines (MARS) and Conic MARS in order to handle uncertainty and non-linearity. The proposed techniques are impl
In the automotive industry, a Control Engineer must design a unique control law that is then tested and validated on a single prototype with a level of reliability high enough to to meet a number of c
Praise for Robust Portfolio Optimization and Management "In the half century since Harry Markowitz introduced his elegant theory for selecting portfolios, investors and scholars have extended and ref
Recovering the low-rank structure of a linear subspace using a small set of corrupted examples has recently been made feasible through substantial advances in the area of matrix completion and nuclear
Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time
The efficient numerical solution of PDE constrained optimization problems plays an important role in many engineering and science applications. The development of robust and efficient numerical algori
