544 research outputs found
Evaluation of advanced optimisation methods for estimating Mixed Logit models
The performances of different simulation-based estimation techniques for mixed logit modeling are evaluated. A quasi-Monte Carlo method (modified Latin hypercube sampling) is compared with a Monte Carlo algorithm with dynamic accuracy. The classic Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm line-search approach and trust region methods, which have proved to be extremely powerful in nonlinear programming, are also compared. Numerical tests are performed on two real data sets: stated preference data for parking type collected in the United Kingdom, and revealed preference data for mode choice collected as part of a German travel diary survey. Several criteria are used to evaluate the approximation quality of the log likelihood function and the accuracy of the results and the associated estimation runtime. Results suggest that the trust region approach outperforms the BFGS approach and that Monte Carlo methods remain competitive with quasi-Monte Carlo methods in high-dimensional problems, especially when an adaptive optimization algorithm is used
Optimizing Low-Discrepancy Sequences with an Evolutionary Algorithm
International audienceMany elds rely on some stochastic sampling of a given com- plex space. Low-discrepancy sequences are methods aim- ing at producing samples with better space-lling properties than uniformly distributed random numbers, hence allow- ing a more ecient sampling of that space. State-of-the-art methods like nearly orthogonal Latin hypercubes and scram- bled Halton sequences are congured by permutations of in- ternal parameters, where permutations are commonly done randomly. This paper proposes the use of evolutionary al- gorithms to evolve these permutations, in order to optimize a discrepancy measure. Results show that an evolution- ary method is able to generate low-discrepancy sequences of signicantly better space-lling properties compared to sequences congured with purely random permutations
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
Cyber-physical systems (CPS), such as automotive systems, are starting to
include sophisticated machine learning (ML) components. Their correctness,
therefore, depends on properties of the inner ML modules. While learning
algorithms aim to generalize from examples, they are only as good as the
examples provided, and recent efforts have shown that they can produce
inconsistent output under small adversarial perturbations. This raises the
question: can the output from learning components can lead to a failure of the
entire CPS? In this work, we address this question by formulating it as a
problem of falsifying signal temporal logic (STL) specifications for CPS with
ML components. We propose a compositional falsification framework where a
temporal logic falsifier and a machine learning analyzer cooperate with the aim
of finding falsifying executions of the considered model. The efficacy of the
proposed technique is shown on an automatic emergency braking system model with
a perception component based on deep neural networks
Calibration of stochastic volatility models using quasi-evolutionary algorithms
V tĂ©to práci se zabĂ˝váme kalibracĂ modelĹŻ stochastickĂ© volatility pomocĂ kvazi-evoluÄŤnĂch algoritmĹŻ. V Ăşvodu nastĂnĂme problĂ©m evoluÄŤnĂch algoritmĹŻ a typy inicializaÄŤnĂch populacĂ, kterĂ© majĂ velkĂ˝ vliv na vĂ˝sledek algoritmu. V práci popisujeme jednotlivĂ© kroky genetickĂ©ho algoritmu, stanovĂ˝me si testovacĂ funkce a zabĂ˝váme se modely stochastickĂ© volatility. SoučástĂ práce je i modifikace genetickĂ©ho algoritmu v programu Matlab. Zde porovnáváme pouĹľitĂ náhodnĂ© a quasi náhodnĂ© inicializaÄŤnĂ populace pĹ™i kalibraci na reálná trĹľnĂ data.ObhájenoIn this thesis, we focus on calibration of stochastic volatility models using quasi-evolutionary algorithms. First we introduce evolutionary algorithms and types of initialization of a new population, that has an important impact on the algorithm. In methodology, we describe each step of the genetic algorithm, set the test functions and focus on stochastic volatility models. An implementation part of this thesis is also a modification of genetic algorithm in software Matlab. We compare quasi random and random initial population on real market data calibration problem
Weak multipliers for generalized van der Corput sequences
Generalized van der Corput sequences are onedimensional, infinite sequences in the unit interval. They are generated from permutations in integer base b and are the building blocks of the multi-dimensional Halton sequences. Motivated by recent progress of Atanassov on the uniform distribution behavior of Halton sequences, we study, among others, permutations of the form P(i) = ai (mod b) for coprime integers a and b. We show that multipliers a that either divide b - 1 or b + 1 generate van der Corput sequences with weak distribution properties. We give explicit lower bounds for the asymptotic distribution behavior of these sequences and relate them to sequences generated from the identity permutation in smaller bases, which are, due to Faure, the weakest distributed generalized van der Corput sequences.Les suites de Van der Corput généralisées sont dessuites unidimensionnelles et infinies dans l’intervalle de l’unité.Elles sont générées par permutations des entiers de la basebetsont les éléments constitutifs des suites multi-dimensionnelles deHalton. Suites aux progrès récents d’Atanassov concernant le com-portement de distribution uniforme des suites de Halton nous nousintéressons aux permutations de la formuleP(i) =ai(modb)pour les entiers premiers entre euxaetb. Dans cet article nousidentifions des multiplicateursagénérant des suites de Van derCorput ayant une mauvaise distribution. Nous donnons les bornesinférieures explicites pour cette distribution asymptotique asso-ciée à ces suites et relions ces dernières aux suites générées parpermutation d’identité, qui sont, selon Faure, les moins bien dis-tribuées des suites généralisées de Van der Corput dans une basedonnée
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