963 resultados para Stochastic Approximation Algorithms
Resumo:
The purpose of this article is to characterize dynamic optimal harvesting trajectories that maximize discounted utility assuming an age-structured population model, in the same line as Tahvonen (2009). The main novelty of our study is that uses as an age-structured population model the standard stochastic cohort framework applied in Virtual Population Analysis for fish stock assessment. This allows us to compare optimal harvesting in a discounted economic context with standard reference points used by fisheries agencies for long term management plans (e.g. Fmsy). Our main findings are the following. First, optimal steady state is characterized and sufficient conditions that guarantees its existence and uniqueness for the general case of n cohorts are shown. It is also proved that the optimal steady state coincides with the traditional target Fmsy when the utility function to be maximized is the yield and the discount rate is zero. Second, an algorithm to calculate the optimal path that easily drives the resource to the steady state is developed. And third, the algorithm is applied to the Northern Stock of hake. Results show that management plans based exclusively on traditional reference targets as Fmsy may drive fishery economic results far from the optimal.
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This paper studies the behavior of the implied volatility function (smile) when the true distribution of the underlying asset is consistent with the stochastic volatility model proposed by Heston (1993). The main result of the paper is to extend previous results applicable to the smile as a whole to alternative degrees of moneyness. The conditions under which the implied volatility function changes whenever there is a change in the parameters associated with Hestons stochastic volatility model for a given degree of moneyness are given.
Resumo:
In this article, we analyze how to evaluate fishery resource management under “ecological uncertainty”. In this context, an efficient policy consists of applying a different exploitation rule depending on the state of the resource and we could say that the stock is always in transition, jumping from one steady state to another. First, we propose a method for calibrating the growth path of the resource such that observed dynamics of resource and captures are matched. Second, we apply the calibration procedure proposed in two different fishing grounds: the European Anchovy (Division VIII) and the Southern Stock of Hake. Our results show that the role played by uncertainty is essential for the conclusions. For European Anchovy fishery (Division VIII) we find, in contrast with Del Valle et al. (2001), that this is not an overexploited fishing ground. However, we show that the Southern Stock of Hake is in a dangerous situation. In both cases our results are in accordance with ICES advice.
Resumo:
In this paper we introduce four scenario Cluster based Lagrangian Decomposition (CLD) procedures for obtaining strong lower bounds to the (optimal) solution value of two-stage stochastic mixed 0-1 problems. At each iteration of the Lagrangian based procedures, the traditional aim consists of obtaining the solution value of the corresponding Lagrangian dual via solving scenario submodels once the nonanticipativity constraints have been dualized. Instead of considering a splitting variable representation over the set of scenarios, we propose to decompose the model into a set of scenario clusters. We compare the computational performance of the four Lagrange multiplier updating procedures, namely the Subgradient Method, the Volume Algorithm, the Progressive Hedging Algorithm and the Dynamic Constrained Cutting Plane scheme for different numbers of scenario clusters and different dimensions of the original problem. Our computational experience shows that the CLD bound and its computational effort depend on the number of scenario clusters to consider. In any case, our results show that the CLD procedures outperform the traditional LD scheme for single scenarios both in the quality of the bounds and computational effort. All the procedures have been implemented in a C++ experimental code. A broad computational experience is reported on a test of randomly generated instances by using the MIP solvers COIN-OR and CPLEX for the auxiliary mixed 0-1 cluster submodels, this last solver within the open source engine COIN-OR. We also give computational evidence of the model tightening effect that the preprocessing techniques, cut generation and appending and parallel computing tools have in stochastic integer optimization. Finally, we have observed that the plain use of both solvers does not provide the optimal solution of the instances included in the testbed with which we have experimented but for two toy instances in affordable elapsed time. On the other hand the proposed procedures provide strong lower bounds (or the same solution value) in a considerably shorter elapsed time for the quasi-optimal solution obtained by other means for the original stochastic problem.
Resumo:
[ES]La fibrilación ventricular (VF) es el primer ritmo registrado en el 40\,\% de las muertes súbitas por paro cardiorrespiratorio extrahospitalario (PCRE). El único tratamiento eficaz para la FV es la desfibrilación mediante una descarga eléctrica. Fuera del hospital, la descarga se administra mediante un desfibrilador externo automático (DEA), que previamente analiza el electrocardiograma (ECG) del paciente y comprueba si presenta un ritmo desfibrilable. La supervivencia en un caso de PCRE depende fundamentalmente de dos factores: la desfibrilación temprana y la resucitación cardiopulmonar (RCP) temprana, que prolonga la FV y por lo tanto la oportunidad de desfibrilación. Para un correcto análisis del ritmo cardiaco es necesario interrumpir la RCP, ya que, debido a las compresiones torácicas, la RCP introduce artefactos en el ECG. Desafortunadamente, la interrupción de la RCP afecta negativamente al éxito en la desfibrilación. En 2003 se aprobó el uso del DEA en pacientes entre 1 y 8 años. Los DEA, que originalmente se diseñaron para pacientes adultos, deben discriminar de forma precisa las arritmias pediátricas para que su uso en niños sea seguro. Varios DEAs se han adaptado para uso pediátrico, bien demostrando la precisión de los algoritmos para adultos con arritmias pediátricas, o bien mediante algoritmos específicos para arritmias pediátricas. Esta tesis presenta un nuevo algoritmo DEA diseñado conjuntamente para pacientes adultos y pediátricos. El algoritmo se ha probado exhaustivamente en bases de datos acordes a los requisitos de la American Heart Association (AHA), y en registros de resucitación con y sin artefacto RCP. El trabajo comenzó con una larga fase experimental en la que se recopilaron y clasificaron retrospectivamente un total de 1090 ritmos pediátricos. Además, se revisó una base de arritmias de adultos y se añadieron 928 nuevos ritmos de adultos. La base de datos final contiene 2782 registros, 1270 se usaron para diseñar el algoritmo y 1512 para validarlo. A continuación, se diseñó un nuevo algoritmo DEA compuesto de cuatro subalgoritmos. Estos subalgoritmos están basados en un conjunto de nuevos parámetros para la detección de arritmias, calculados en diversos dominios de la señal, como el tiempo, la frecuencia, la pendiente o la función de autocorrelación. El algoritmo cumple las exigencias de la AHA para la detección de ritmos desfibrilables y no-desfibrilables tanto en pacientes adultos como en pediátricos. El trabajo concluyó con el análisis del comportamiento del algoritmo con episodios reales de resucitación. En los ritmos que no contenían artefacto RCP se cumplieron las exigencias de la AHA. Posteriormente, se estudió la precisión del algoritmo durante las compresiones torácicas, antes y después de filtrar el artefacto RCP. Para suprimir el artefacto se utilizó un nuevo método desarrollado a lo largo de la tesis. Los ritmos desfibrilables se detectaron de forma precisa tras el filtrado, los no-desfibrilables sin embargo no.
Resumo:
373 p. : il., gráf., fot., tablas