H
Hugo Maruri-Aguilar
Researcher at Queen Mary University of London
Publications - 28
Citations - 191
Hugo Maruri-Aguilar is an academic researcher from Queen Mary University of London. The author has contributed to research in topics: Polynomial & Monomial. The author has an hindex of 7, co-authored 26 publications receiving 180 citations. Previous affiliations of Hugo Maruri-Aguilar include University of London & University of Leeds.
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Nonlinear Matroid Optimization and Experimental Design
Yael Berstein,Jon Lee,Hugo Maruri-Aguilar,Shmuel Onn,Eva Riccomagno,Robert Weismantel,Henry P. Wynn +6 more
TL;DR: In this paper, the problem of optimizing nonlinear objective functions over matroids presented by oracles or explicitly is studied, which can be interpreted as the balancing of multicriteria optimization.
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Nonlinear Matroid Optimization and Experimental Design
Yael Berstein,Jon Lee,Hugo Maruri-Aguilar,Shmuel Onn,Eva Riccomagno,Robert Weismantel,Henry P. Wynn +6 more
TL;DR: This work provides a combinatorial polynomial time algorithm for arbitrary oracle-presented matroids, that makes repeated use of matroid intersection and an algebraic algorithm for vectorialMatroids.
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An Efficient Screening Method for Computer Experiments
TL;DR: The proposed method is built upon the elementary effects (EE) method for screening and uses a threshold value to separate the inputs with linear and nonlinear effects and acts in a sequential way to keep the number of simulator runs down to a minimum.
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Bayesian Precalibration of a Large Stochastic Microsimulation Model
TL;DR: This paper proposes a fast iterative probabilistic precalibration framework and demonstrates how it can be successfully applied to a real-world traffic simulation model of a section of the M40 motorway and its surrounding area in the U.K.
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Minimal average degree aberration and the state polytope for experimental designs
TL;DR: In this article, a simple algorithm is given and bounds are derived for the criteria, which may be used to give asymptotic Nyquist-like estimability rates as model and sample sizes increase.