Results 41 to 50 of about 1,105,335 (289)
Shortfall-Based Wasserstein Distributionally Robust Optimization
Mathematics, 2023 In this paper, we study a distributionally robust optimization (DRO) problem with affine decision rules. In particular, we construct an ambiguity set based on a new family of Wasserstein metrics, shortfall–Wasserstein metrics, which apply normalized ...
Ruoxuan Li, Wenhua Lv, Tiantian Mao
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Distributionally robust optimization
Acta NumericaDistributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncertain. A key component of any DRO model is its ambiguity set, that is, a family of probability distributions consistent with any available structural or statistical ...
Daniel Kuhn 0001 +2 more
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Hybrid Rocket Engine Design Optimization at Politecnico di Torino: A Review
Aerospace, 2021 Optimization of Hybrid Rocket Engines at Politecnico di Torino began in the 1990s. A comprehensive review of the related research activities carried out in the last three decades is here presented.
Lorenzo Casalino +2 more
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Twenty years of continuous multiobjective optimization in the twenty-first century
EURO Journal on Computational Optimization, 2021 The survey highlights some of the research topics which have attracted attention in the last two decades within the area of mathematical optimization of multiple objective functions.
Gabriele Eichfelder
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Theory and Applications of Robust Optimization [PDF]
, 2010 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 ...
Banerjee O. +10 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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Robust optimization ? methodology and applications [PDF]
Mathematical Programming, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Aharon Ben-Tal, Arkadi Nemirovski
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Bayesian Distributionally Robust Optimization
SIAM Journal on Optimization, 2023 We introduce a new framework, Bayesian Distributionally Robust Optimization (Bayesian-DRO), for data-driven stochastic optimization where the underlying distribution is unknown. Bayesian-DRO contrasts with most of the existing DRO approaches in the use of Bayesian estimation of the unknown distribution.
Alexander Shapiro 0001 +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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Pediatric Blood &Cancer, EarlyView.ABSTRACT Objective To evaluate selumetinib exposure using therapeutic drug monitoring (TDM) in pediatric patients with neurofibromatosis type 1 (NF1) and plexiform neurofibromas (PN), assess interpatient pharmacokinetic variability, and explore the relationship between drug exposure, clinical response, and adverse effects.
Janka Kovács +8 more
wiley +1 more source