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Statistics & Risk Modeling, 1993
Summary: We consider the problem of discrete-time causal filtering for scalar systems in the presence of data outliers. We model the outliers as an extension to time-series of Huber's \(\varepsilon\)-contamination model [\textit{P. J. Huber}, Ann. Math. Statist. 35, 73-101 (1964; Zbl 0136.398); ibid.
Birmiwal, Kailash, Shen, Jun
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Summary: We consider the problem of discrete-time causal filtering for scalar systems in the presence of data outliers. We model the outliers as an extension to time-series of Huber's \(\varepsilon\)-contamination model [\textit{P. J. Huber}, Ann. Math. Statist. 35, 73-101 (1964; Zbl 0136.398); ibid.
Birmiwal, Kailash, Shen, Jun
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Mathematics of Operations Research, 1998
We study convex optimization problems for which the data is not specified exactly and it is only known to belong to a given uncertainty set U, yet the constraints must hold for all possible values of the data from U. The ensuing optimization problem is called robust optimization. In this paper we lay the foundation of robust convex optimization.
Ben-Tal, A., Nemirovski, A.
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We study convex optimization problems for which the data is not specified exactly and it is only known to belong to a given uncertainty set U, yet the constraints must hold for all possible values of the data from U. The ensuing optimization problem is called robust optimization. In this paper we lay the foundation of robust convex optimization.
Ben-Tal, A., Nemirovski, A.
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Metrika, 2002
We address the problem of estimating risk-minimizing portfolios from a sample of historical returns, when the underlying distribution that generates returns exhibits departures from the standard Gaussian assumption. Specifically, we examine how the underlying estimation problem is influenced by marginal heavy tails, as modeled by the univariate Student-
G. J. Lauprete +2 more
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We address the problem of estimating risk-minimizing portfolios from a sample of historical returns, when the underlying distribution that generates returns exhibits departures from the standard Gaussian assumption. Specifically, we examine how the underlying estimation problem is influenced by marginal heavy tails, as modeled by the univariate Student-
G. J. Lauprete +2 more
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Digital Optimal Robust Control
Physical Review Letters, 2023The lack of ability to determine and implement accurately quantum optimal control is a strong limitation to the development of quantum technologies. We propose a digital procedure based on a series of pulses where their amplitudes and (static) phases are designed from an optimal continuous-time protocol for given type and degree of robustness ...
Harutyunyan, Meri +3 more
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Journal of Optimization Theory and Applications, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Proceedings of the 1997 American Control Conference (Cat. No.97CH36041), 1997
We show by examples that optimum and robust controllers, designed by using the H/sub 2/, H/sub /spl infin//, l/sup 1/ and /spl mu/ formulations, can produce extremely fragile controllers, in the sense that vanishingly small perturbations of the coefficients of the designed controller destabilize the closed loop control system.
Keel, L. H., Bhattacharyya, S. P.
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We show by examples that optimum and robust controllers, designed by using the H/sub 2/, H/sub /spl infin//, l/sup 1/ and /spl mu/ formulations, can produce extremely fragile controllers, in the sense that vanishingly small perturbations of the coefficients of the designed controller destabilize the closed loop control system.
Keel, L. H., Bhattacharyya, S. P.
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Robust dual-response optimization
IIE Transactions, 2015This article presents a robust optimization reformulation of the dual-response problem developed in response surface methodology. The dual-response approach fits separate models for the mean and the variance and analyzes these two models in a mathematical optimization setting.
Yanıkoğlu, İhsan +2 more
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Robustness, Optimization, and Architectures
European Journal of Control, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chandra, Fiona A. +3 more
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Almost Robust Discrete Optimization
European Journal of Operational Research, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Opher Baron +3 more
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2017
This master’s thesis studies optimization problems handling data influenced by un- certainties as they appear in various ’real life’ applications. The transformation of a general optimization problem into the according robust optimization problem by developing the robust counterpart is of special interest.
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This master’s thesis studies optimization problems handling data influenced by un- certainties as they appear in various ’real life’ applications. The transformation of a general optimization problem into the according robust optimization problem by developing the robust counterpart is of special interest.
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