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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.
Aharon Ben-Tal, Arkadi Nemirovski
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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.
Aharon Ben-Tal, Arkadi Nemirovski
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Robust Multiobjective Optimization With Robust Consensus
IEEE Transactions on Fuzzy Systems, 2018Consider a multiobjective robust optimization problem, where a set of weighted decision makers provides their preferences a priori . The preferences are provided either in the objective space or in the decision variable space using fuzzy numbers. To solve this problem, an indicator to measure consensus, an indicator to measure the robustness of the ...
Kaustuv Nag +3 more
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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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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.
Lee H. Keel, Shankar P. Bhattacharyya
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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.
Lee H. Keel, Shankar P. Bhattacharyya
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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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Pattern Recognition, 2005
Qualitatively, a filter is said to be ''robust'' if its performance degradation is acceptable for distributions close to the one for which it is optimal, that is, the one for which it has been designed. This paper adapts the signal-processing theory of optimal robust filters to classifiers.
Edward R. Dougherty +3 more
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Qualitatively, a filter is said to be ''robust'' if its performance degradation is acceptable for distributions close to the one for which it is optimal, that is, the one for which it has been designed. This paper adapts the signal-processing theory of optimal robust filters to classifiers.
Edward R. Dougherty +3 more
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A distributional interpretation of robust optimization
2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2010Motivated by data-driven decision making and sampling problems, we investigate probabilistic interpretations of robust optimization (RO). We establish a connection between RO and distributionally robust stochastic programming (DRSP), showing that the solution to any RO problem is also a solution to a DRSP problem.
Huan Xu 0001 +2 more
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Journal of Optimization Theory and Applications, 2013
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Robust control with optimization of robustness index
2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012The paper considers a problem of robust control system synthesis based on the modified algorithm of Coefficient Diagram Method (CDM) with robustness index optimization. The proposed solution is expected to enable improvement of system robustness against parametric uncertainty.
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Robust Optimization for Clustering
2016In this paper, we investigate the robust optimization for the minimum sum-of squares clustering (MSSC) problem. Each data point is assumed to belong to a box-type uncertainty set. Following the robust optimization paradigm, we obtain a robust formulation that can be interpreted as a combination of MSSC and k-median clustering criteria.
Xuan Thanh Vo +2 more
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