Robust Principal Component Analysis (RPCA) has generated a great amount of interest for background/foreground estimation in videos. The central hypothesis in this setting is that a video’s background can be well-represented by a low-rank model. However, in the presence of complex lighting conditions this model is only accurate in localised spatio-temporal regions. Following this observation, we...

This document provides a brief proposal for “Robust Optimization & Robust Control Synthesis” reading group.

Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. This paper develops a robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply our approach to address the problem of analyzing competing biomass supply chains subject to stochastic de...

This paper is concerned with the H∞ control problem via state feedback for fast sampling discrete-time singularly perturbed systems. A new H∞ controller design method is given in terms of solutions to linear matrix inequalities (LMIs), which eliminates the regularity restrictions attached to the Riccati-based solution. A method for evaluating the upper bound of singular perturbation parameter w...

Abstract Sample average approximation (SAA) is a widely popular approach to data-driven decisionmaking under uncertainty. Under mild assumptions, SAA is both tractable and enjoys strong asymptotic performance guarantees. Similar guarantees, however, do not typically hold in finite samples. In this paper, we propose a modification of SAA, which we term Robust SAA, which retains SAA’s tractabilit...

Integrated performance and risk management is the key lever to increase shareholder value holistically. In this paper, we develop a corresponding framework for value-based performance and risk optimization in supply chains. Economic Value Added (EVA) as a prevalent metric of value-based performance is applied to mid-term sales and operations planning (S&OP). Robust optimization methods are util...

This paper investigated the problem of decentralized robust stabilization for a class of uncertain nonlinear descriptor similar large-scale composite systems with input saturation. Based on decomposing theory of large-scale systems, Lyapunov stable theory and matrix theory, a kind of decentralized robust stabilization controller is designed which ensures asymptotic stability of the closed-loop ...

A common problem in the optimization of structures is the handling of uncertainties in the parameters. If the parameters appear in the constraints, the uncertainties can lead to an infinite number of constraints. Usually the constraints have to be approximated by finite expressions to generate a computable problem. Here, using the example of the topology optimization of a truss, a method is pro...

This paper is concerned with stabilizing open loop unstable fluidized bed spray granulation with internal product classification by means of low order control methods. The process under investigation can be described by a population balance equation. In order to design a low order controller numerical approximation and model reduction methods are applied resulting in a low order design model. S...

This paper presents mathematical programming techniques for solving a class of multi-sensor scheduling problems. Robust optimization problems are formulated for both deterministic and stochastic cases using linear 0–1 programming techniques. Equivalent formulations are developed in terms of cardinality constraints. We conducted numerical case studies and analyzed the performance of optimization...