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Theory and Applications of Robust Optimization
In this paper we survey the primary research, both theoretical and applied,
in the area of Robust Optimization (RO). Our focus is on the computational
attractiveness of RO approaches, as well as the modeling power and broad
applicability of the methodology. In addition to surveying prominent
theoretical results of RO, we also present some recent results linking RO to
adaptable models for multi-stage decision-making problems. Finally, we
highlight applications of RO across a wide spectrum of domains, including
finance, statistics, learning, and various areas of engineering.Comment: 50 page
A Posteriori Probabilistic Bounds of Convex Scenario Programs with Validation Tests
Scenario programs have established themselves as efficient tools towards
decision-making under uncertainty. To assess the quality of scenario-based
solutions a posteriori, validation tests based on Bernoulli trials have been
widely adopted in practice. However, to reach a theoretically reliable
judgement of risk, one typically needs to collect massive validation samples.
In this work, we propose new a posteriori bounds for convex scenario programs
with validation tests, which are dependent on both realizations of support
constraints and performance on out-of-sample validation data. The proposed
bounds enjoy wide generality in that many existing theoretical results can be
incorporated as particular cases. To facilitate practical use, a systematic
approach for parameterizing a posteriori probability bounds is also developed,
which is shown to possess a variety of desirable properties allowing for easy
implementations and clear interpretations. By synthesizing comprehensive
information about support constraints and validation tests, improved risk
evaluation can be achieved for randomized solutions in comparison with existing
a posteriori bounds. Case studies on controller design of aircraft lateral
motion are presented to validate the effectiveness of the proposed a posteriori
bounds
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