Results 41 to 50 of about 376,109 (314)

Distributionally robust optimization

Acta Numerica
Distributionally 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, Soroosh Shafiee, Wolfram Wiesemann   +2 more
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Robust-to-Dynamics Optimization

Mathematics of Operations Research
A robust-to-dynamics optimization (RDO) problem is an optimization problem specified by two pieces of input: (i) a mathematical program (an objective function [Formula: see text] and a feasible set [Formula: see text]) and (ii) a dynamical system (a map [Formula: see text]).
Amir Ali Ahmadi, Oktay Günlük
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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, Enlu Zhou, Yifan Lin   +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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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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Reducing Conservatism in Robust Optimization [PDF]

INFORMS Journal on Computing, 2020
Summary: Although robust optimization is a powerful technique in dealing with uncertainty in optimization, its solutions can be too conservative. More specifically, it can lead to an objective value much worse than the nominal solution or even to infeasibility of the robust problem.
Ernst Roos, Dick den Hertog
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Spatiotemporal and quantitative analyses of phosphoinositides – fluorescent probe—and mass spectrometry‐based approaches

FEBS Letters, EarlyView.
Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho, Shin Morioka, Junko Sasaki, Takehiko Sasaki   +3 more
wiley   +1 more source

Robust Covariance Matrix Adaptation Evolution Strategy: Optimal Design of Magnetic Devices Considering Material Variation

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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Mean robust optimization

Mathematical Programming
Robust 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), C. Becker (Cole), BPG van Parys (Bart), B. Stellato (Bartolomeo)   +3 more
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