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Preprocessing Imprecise Points for the Pareto Front [PDF]
, 2022 In the preprocessing model for uncertain data we are given a set of regions R which model the uncertainty associated with an unknown set of points P. In this model there are two phases: a preprocessing phase, in which we have access only to R, followed by a reconstruction phase, in which we have access to points in P at a certain retrieval cost C per ...
Ivor van der Hoog +3 more
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BMC Bioinformatics, 2022 Background Single-cell RNA sequencing (scRNA-seq) technology has contributed significantly to diverse research areas in biology, from cancer to development.
Hui Li +3 more
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Two-Sided Pareto Front Approximations [PDF]
Journal of Optimization Theory and Applications, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ignacy Kaliszewski, Janusz Miroforidis
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Multiobjective portfolio optimization via Pareto front evolution
Complex & Intelligent Systems, 2022 Portfolio optimization is about building an investment decision on a set of candidate assets with finite capital. Generally, investors should devise rational compromise to return and risk for their investments.
Yi Chen, Ai-Min Zhou
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Mathematical and Computational Applications, 2022 Multi-objective evolutionary algorithms (MOEAs) have been successfully applied for the numerical treatment of multi-objective optimization problems (MOP) during the last three decades.
Carlos Ignacio Hernández Castellanos +1 more
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Pareto front approximation through a multi-objective augmented Lagrangian method
EURO Journal on Computational Optimization, 2021 In this manuscript, we consider smooth multi-objective optimization problems with convex constraints. We propose an extension of a multi-objective augmented Lagrangian Method from recent literature.
G. Cocchi +2 more
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Multi-objective new product development by complete Pareto front and ripple-spreading algorithm [PDF]
, 2014 Given several different new product development projects and limited resources, this paper is concerned with the optimal allocation of resources among the projects.
Hu, Xiao-Bing +4 more
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Automatic Discovery of Privacy–Utility Pareto Fronts [PDF]
Proceedings on Privacy Enhancing Technologies, 2020 Abstract Differential privacy is a mathematical framework for privacy-preserving data analysis. Changing the hyperparameters of a differentially private algorithm allows one to trade off privacy and utility in a principled way. Quantifying this trade-off in advance is essential to decision-makers tasked with deciding how much privacy can
Brendan Avent +4 more
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