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We consider robust feedback control of time-varying, linear discrete-time systems operating over a finite horizon. For such systems, we consider the problem of designing robust causal controllers that minimize the expected value of a convex quadratic cost ...
Institute of Electrical and Electronics Engineers2011
Stochastic programming and robust optimization are disciplines concerned with optimal decision-making under uncertainty over time. Traditional models and solution algorithms have been tailored to problems where the order in which the uncertainties unfold i ...
Robust portfolio optimization aims to maximize the worst-case portfolio return given that the asset returns are allowed to vary within a prescribed uncertainty set. If the uncertainty set is not too large, the resulting portfolio performs well under normal ...
We consider robust feedback control of time-varying, linear discrete-time systems operating over a finite horizon. For such systems, we consider the problem of designing robust causal controllers that minimize the expected value of a convex quadratic cost ...
Institute of Electrical and Electronics Engineers2011
Fast model predictive control on embedded sys- tems has been successfully applied to plants with microsecond sampling times employing a precomputed state-to-input map. However, the complexity of this so-called explicit MPC can be prohibitive even for low-d ...
A current trend in robotics is to define robot motions so that they can be easily adopted to situations beyond those for which the motion was originally designed. In this work, we show how the challenging task of playing minigolf can be efficiently tackled ...
Robust dynamic optimization problems involving adaptive decisions are computationally intractable in general. Tractable upper bounding approximations can be obtained by requiring the adaptive decisions to be representable as linear decision rules (LDRs). I ...
Heat removal and power density distribution delivery have become two major reliability concerns in 3D stacked technology. In this paper, we propose a thermal-driven 3D floorplanner. Our contributions include: (1) a novel multi-objective formulation to cons ...
Linear stochastic programming provides a flexible toolbox for analyzing real-life decision situations, but it can become computationally cumbersome when recourse decisions are involved. The latter are usually modeled as decision rules, i.e., functions of t ...
