Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Rolfes, Jan"'
In this paper we present a new semidefinite programming hierarchy for covering problems in compact metric spaces. Over the last years, these kind of hierarchies were developed primarily for geometric packing and for energy minimization problems; they
Externí odkaz:
http://arxiv.org/abs/2312.11267
Autor:
Kreuz, Sebastian, Zubiri, Benjamin Apeleo, Englisch, Silvan, Kang, Sung-Gyu, Ramachandramoorthy, Rajaprakash, Spiecker, Erdmann, Liers, Frauke, Rolfes, Jan
Compressed sensing is an image reconstruction technique to achieve high-quality results from limited amount of data. In order to achieve this, it utilizes prior knowledge about the samples that shall be reconstructed. Focusing on image reconstruction
Externí odkaz:
http://arxiv.org/abs/2312.05592
The presented work addresses two-stage stochastic programs (2SPs), a broadly applicable model to capture optimization problems subject to uncertain parameters with adjustable decision variables. In case the adjustable or second-stage variables contai
Externí odkaz:
http://arxiv.org/abs/2305.06785
Finding point configurations, that yield the maximum polarization (Chebyshev constant) is gaining interest in the field of geometric optimization. In the present article, we study the problem of unconstrained maximum polarization on compact sets. In
Externí odkaz:
http://arxiv.org/abs/2303.10101
In the present article we propose a mixed-integer approximation of adjustable-robust optimization (ARO) problems, that have both, continuous and discrete variables on the lowest level. As these trilevel problems are notoriously hard to solve, we rest
Externí odkaz:
http://arxiv.org/abs/2302.13962
In this work, we present algorithmically tractable safe approximations of distributionally robust optimization (DRO) problems. The considered ambiguity sets can exploit information on moments as well as confidence sets. Typically, reformulation appro
Externí odkaz:
http://arxiv.org/abs/2301.11185
Pareto efficiency for robust linear programs was introduced by Iancu and Trichakis in [9]. We generalize their approach and theoretical results to robust optimization problems in Euclidean spaces with affine uncertainty. Additionally, we demonstrate
Externí odkaz:
http://arxiv.org/abs/2109.03208
This work studies equilibrium problems under uncertainty where firms maximize their profits in a robust way when selling their output. Robust optimization plays an increasingly important role when best guaranteed objective values are to be determined
Externí odkaz:
http://arxiv.org/abs/2108.09139
Linear complementarity problems are a powerful tool for modeling many practically relevant situations such as market equilibria. They also connect many sub-areas of mathematics like game theory, optimization, and matrix theory. Despite their close re
Externí odkaz:
http://arxiv.org/abs/2008.05784
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