912 resultados para expenditure constraint


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El punto de vista de muchas otras aplicaciones que modifican las reglas de computación. En segundo lugar, y una vez generalizado el concepto de independencia, es necesario realizar un estudio exhaustivo de la efectividad de las herramientas de análisis en la tarea de la paralelizacion automática. Los resultados obtenidos de dicha evaluación permiten asegurar de forma empírica que la utilización de analizadores globales en la tarea de la paralelizacion automática es vital para la consecución de una paralelizarían efectiva. Por último, a la luz de los buenos resultados obtenidos sobre la efectividad de los analizadores de flujo globales basados en la interpretación abstracta, se presenta la generalización de las herramientas de análisis al contexto de los lenguajes lógicos restricciones y planificación dinámica.

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Fuel poverty can be defined as ‘the inability to afford adequate warmth in the home’ and it is the result of the combination of three factors: low household income, lack of energy efficiency and high energy bills. Within this context, the present research is aimed at characterizing, for the first time, the housing stock of fuel-poor households in the Autonomous Region of Madrid. Fuel poverty incidence was established and households were divided into six different groups according to their relative position regarding fuel and monetary poverty. The housing stock of each group is characterized and those households most in need are identified. These results enable energy retrofitting priorities to be established, focusing on the needs of the different household groups and accounting for their housing stock characteristics. This allows Spanish energy retrofitting policies to be assessed for their capability of tackling fuel poverty and makes it possible to suggest some improvements.

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Linear vector semi-infinite optimization deals with the simultaneous minimization of finitely many linear scalar functions subject to infinitely many linear constraints. This paper provides characterizations of the weakly efficient, efficient, properly efficient and strongly efficient points in terms of cones involving the data and Karush–Kuhn–Tucker conditions. The latter characterizations rely on different local and global constraint qualifications. The global constraint qualifications are illustrated on a collection of selected applications.

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The multiobjective optimization model studied in this paper deals with simultaneous minimization of finitely many linear functions subject to an arbitrary number of uncertain linear constraints. We first provide a radius of robust feasibility guaranteeing the feasibility of the robust counterpart under affine data parametrization. We then establish dual characterizations of robust solutions of our model that are immunized against data uncertainty by way of characterizing corresponding solutions of robust counterpart of the model. Consequently, we present robust duality theorems relating the value of the robust model with the corresponding value of its dual problem.

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Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods.

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Convex vector (or multi-objective) semi-infinite optimization deals with the simultaneous minimization of finitely many convex scalar functions subject to infinitely many convex constraints. This paper provides characterizations of the weakly efficient, efficient and properly efficient points in terms of cones involving the data and Karush–Kuhn–Tucker conditions. The latter characterizations rely on different local and global constraint qualifications. The results in this paper generalize those obtained by the same authors on linear vector semi-infinite optimization problems.