Results 41 to 50 of about 3,492,516 (198)
Robustness in the Optimization of Risk Measures [PDF]
SSRN Electronic Journal, 2018 We study issues of robustness in the context of Quantitative Risk Management and Optimization. We develop a general methodology for determining whether a given risk-measurement-related optimization problem is robust, which we call “robustness against optimization.” The new notion is studied for various classes of risk measures and expected utility and
Paul Embrechts +2 more
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Development of an experiment-based robust design paradigm for multiple quality characteristics using physical programming [PDF]
, 2007 The well-known quality improvement methodology, robust design, is a powerful and cost-effective technique for building quality into the design of products and processes.
Antony, Jiju
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Mathematical ProgrammingRobust optimization is a tractable and expressive technique for decision-making under uncertainty, but it can lead to overly conservative decisions when pessimistic assumptions are made on the uncertain parameters. Wasserstein distributionally robust optimization can reduce conservatism by being data-driven, but it often leads to very large problems ...
I. Wang (Irina) +3 more
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Causality and Robust Optimization
CoRR, 2020 A decision-maker must consider cofounding bias when attempting to apply machine learning prediction, and, while feature selection is widely recognized as important process in data-analysis, it could cause cofounding bias. A causal Bayesian network is a standard tool for describing causal relationships, and if relationships are known, then adjustment ...
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Determining a Robust, Pareto Optimal Geometry for a Welded Joint [PDF]
, 2014 Multi-criteria optimization problems are known to give rise to a set of Pareto optimal solutions where one solution cannot be regarded as being superior to another.
Radhi, Hazim E. +3 more
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A bicriteria approach to robust optimization [PDF]
Computers & Operations Research, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
André B. Chassein, Marc Goerigk
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Optimality of Robust Online Learning
Foundations of Computational Mathematics, 2023 In this paper, we study an online learning algorithm with a robust loss function $\mathcal{L}_σ$ for regression over a reproducing kernel Hilbert space (RKHS). The loss function $\mathcal{L}_σ$ involving a scaling parameter $σ>0$ can cover a wide range of commonly used robust losses.
Zheng-Chu Guo +2 more
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IEEE Access, 2023 Uncertainties caused by material variation can significantly impair the characteristics of devices. Therefore, it is important to design devices whose performance is not significantly damaged even when material variations occur. Robust optimization seeks
Akito Maruo, Hajime Igarashi
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A robust optimization method based on previous optimization knowledge
Zhongguo Jianchuan Yanjiu, 2022 ObjectivesAs solving the robust optimization (RO) problem with interval uncertainty is unduly time-consuming when the nested differential evolution algorithm is directly used, a new RO design method is proposed.MethodsIn the proposed method, individuals'
Guochen LYU +3 more
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Robust Topology Optimization for Compliant Mechanisms Considering Uncertainty of Applied Loads
Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2008 In this study, a robust topology optimization method is proposed for compliant mechanisms, where the effect that variation of the input load direction has on the output displacement is considered.
Nozomu KOGISO +4 more
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