Results 11 to 20 of about 1,105,335 (289)
Robust balanced optimization [PDF]
EURO Journal on Computational Optimization, 2018 An instance of a balanced optimization problem with vector costs consists of a ground set X, a cost-vector for every element of X, and a system of feasible subsets over X.
AnnetteM.C. Ficker +2 more
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Multipolar robust optimization [PDF]
EURO Journal on Computational Optimization, 2018 We consider linear programs involving uncertain parameters and propose a new tractable robust counterpart which contains and generalizes several other models including the existing Affinely Adjustable Robust Counterpart and the Fully Adjustable Robust ...
Walid Ben-Ameur +3 more
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Robust-to-Dynamics Optimization
Mathematics of Operations Research, 2018 A robust-to-dynamics optimization (RDO) problem is an optimization problem specified by two pieces of input: (i) a mathematical program (an objective function $f:\mathbb{R}^n\rightarrow\mathbb{R}$ and a feasible set $\Omega\subseteq\mathbb{R}^n$), and ...
Ahmadi, Amir Ali, Gunluk, Oktay
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Robust optimization through neuroevolution.
PLoS ONE, 2019 We propose a method for evolving neural network controllers robust with respect to variations of the environmental conditions (i.e. that can operate effectively in new conditions immediately, without the need to adapt to variations). The method specifies
Paolo Pagliuca, Stefano Nolfi
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Data-Driven Robust Optimization [PDF]
Mathematical Programming, 2014 The last decade witnessed an explosion in the availability of data for operations research applications. Motivated by this growing availability, we propose a novel schema for utilizing data to design uncertainty sets for robust optimization using ...
Bertsimas, Dimitris +2 more
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A robust robust optimization result [PDF]
Operations Research Letters, 2012 We study the loss in objective value when an inaccurate objective is optimized instead of the true one, and show that "on average" this loss is very small, for an arbitrary compact feasible region.
Martina Gancarova, Michael J. Todd
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Optimization and Optimizers for Adversarial Robustness
CoRR, 2023 Empirical robustness evaluation (RE) of deep learning models against adversarial perturbations entails solving nontrivial constrained optimization problems. Existing numerical algorithms that are commonly used to solve them in practice predominantly rely on projected gradient, and mostly handle perturbations modeled by the $\ell_1$, $\ell_2$ and $\ell_\
Hengyue Liang +5 more
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Frontiers in Energy Research, 2020 Ammonia/hydrogen-fueled combustion represents a very promising solution for the future energy scenario. This study aims to shed light and understand the behavior of ammonia/hydrogen blends under flameless conditions.
Marco Ferrarotti +9 more
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Evaluation of Modeling Approaches for MILD Combustion Systems With Internal Recirculation
Frontiers in Mechanical Engineering, 2020 Numerical simulations employing two different modeling approaches are performed and validated against experimental results from a moderate or intense low-oxygen dilution (MILD) system with internal recirculation. The flamelet-generated manifold (FGM) and
Ruggero Amaduzzi +7 more
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Frameworks and Results in Distributionally Robust Optimization
Open Journal of Mathematical Optimization, 2022 The concepts of risk aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. The statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these ...
Rahimian, Hamed, Mehrotra, Sanjay
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