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Portfolio Selection by Robust Optimization [PDF]
تحقیقات مالی, 2014 This paper discusses the portfolio selection based on robust optimization. Since the parameters values of the portfolio optimization problem such as price of the stock, dividends, returns, etc.
Azin Abrishami, Reza Yousefi Zenouz
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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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Robustness of A-optimal designs
Linear Algebra and its Applications, 2008 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Masaro, Joe, Wong, Chi Song
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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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Case Studies in Thermal Engineering, 2020 As they deliver dispatchable renewable energy, biomass power plants are expected to play a key role in the stability of the future electricity grids dominated by intermittent renewables.
J. Blondeau +5 more
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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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Optimal robust expensive optimization is tractable [PDF]
Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 2009 Following a number of recent papers investigating the possibility of optimal comparison-based optimization algorithms for a given distribution of probability on fitness functions, we (i) discuss the comparison-based constraints (ii) choose a setting in which theoretical tight bounds are known (iii) develop a careful implementation using billiard ...
Rolet, Philippe +2 more
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Robust-to-Dynamics Optimization
Mathematics of Operations ResearchA 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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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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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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