Stochastic Problems
Mostrando 1-12 de 78 artigos, teses e dissertações.
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1. Lessons and perspectives for applications of stochastic models in biological and cancer research
The effects of randomness, an unavoidable feature of intracellular environments, are observed at higher hierarchical levels of living matter organization, such as cells, tissues, and organisms. Additionally, the many compounds interacting as a well-orchestrated network of reactions increase the difficulties of assessing these systems using only experiments.
Clinics. Publicado em: 21/09/2018
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2. STOCHASTIC KNAPSACK PROBLEM: APPLICATION TO TRANSPORTATION PROBLEMS
ABSTRACT In this paper, we study the stochastic knapsack problem with expectation constraint. We solve the relaxed version of this problem using a stochastic gradient algorithm in order to provide upper bounds for a branch-and-bound framework. Two approaches to estimate the needed gradients are studied, one based on Integration by Parts and one using Finite
Pesqui. Oper.. Publicado em: 2017-09
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3. STOCHASTIC GRADIENT METHODS FOR UNCONSTRAINED OPTIMIZATION
This papers presents an overview of gradient based methods for minimization of noisy functions. It is assumed that the objective functions is either given with error terms of stochastic nature or given as the mathematical expectation. Such problems arise in the context of simulation based optimization. The focus of this presentation is on the gradient based
Pesqui. Oper.. Publicado em: 2014-12
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4. Stochastic multi-scale analysis of homogenised properties considering uncertainties in cellular solid microstructures using a first-order perturbation
Randomness in the microstructure due to variations in microscopic properties and geometrical information is used to predict the stochastically homogenised properties of cellular media. Two stochastic problems at the micro-scale level that commonly occur due to fabrication inaccuracies, degradation mechanisms or natural heterogeneity were analysed using a sto
Lat. Am. j. solids struct.. Publicado em: 2014-10
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5. Decomposition approach for generation and transmission expansion planning with implicit multipliers evaluation
In an electric power systems planning framework, decomposition techniques are usually applied to separate investment and operation subproblems to take benefits from the use of independent solution algorithms. Real power systems planning problems can be rather complex and their detailed representation often leads to greater effort to solve the operation subpr
Pesqui. Oper.. Publicado em: 08/11/2013
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6. Proper orthogonal decomposition for model reduction of a vibroimpact system
The application that inspires this work is the percussion drilling. This problem has impacts and presents uncertainties. In this first analysis the focus is on the construction of an efficient reduced-order model to deal with the nonlinear dynamics due to the impacts. It is important to have an efficient reduced-order model to perform the stochastic analysis
J. Braz. Soc. Mech. Sci. & Eng.. Publicado em: 2012-09
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7. Selecting the system most likely to be the best in the presence of an infinite number of alternatives
Simulation Optimization (SO) belongs to a broader class of problems called Stochastic Optimization. Most of the proposed SO methodologies in the literature aim to optimize the expected value of the performance measure. This thesis focus is on another class of problems: Multinomial Selection Procedures (MSPs). These procedures select the best alternative, whe
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 02/12/2011
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8. Solution of porous media inverse drying problems using a combination of stochastic and deterministic methods
In the present work the inverse problem of simultaneous heat and mass transfer modeled by Luikov equations is studied using a hybrid combination of the Levenberg-Marquardt (LM), Simulated Annealing (SA) and Artificial Neural Network (ANN) methods. The direct and inverse problems are described, formulated and solved. After the use of an experiment design tech
Journal of the Brazilian Society of Mechanical Sciences and Engineering. Publicado em: 2011-12
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9. Comparing stochastic optimization methods to solve the medium-term operation planning problem
The Medium-Term Operation Planning (MTOP) of hydrothermal systems aims to define the generation for each power plant, minimizing the expected operating cost over the planning horizon. Mathematically, this task can be characterized as a linear, stochastic, large-scale problem which requires the application of suitable optimization tools. To solve this problem
Computational & Applied Mathematics. Publicado em: 2011
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10. Stochastic Newton-like methods for computing equilibria in general equilibrium models
Calculating an equilibrium point in general equilibrium models in many cases reduces to solving a nonlinear system of equations. Taking model parameter values as random variables with a known distribution increases the level of information provided by the model but makes computation of equilibrium points even more challenging. We propose a computationally ef
Computational & Applied Mathematics. Publicado em: 2011
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11. Application of stochastic volatility models to air pollution data of two big cities: Mexico City and São Paulo / Aplicação de modelos de volatilidade estocástica em dados de poluição do ar de duas grandes cidades: Cidade do México e São Paulo
Recent studies related to environmental has been considered in all world due to increasing levels of pollution and of natural resources destruction especially, in the last years. The largest cities in the world are the ones been mostly affected by pollution and in this work we consider the analysis of air pollution data of two important cities: Mexico City a
Publicado em: 2010
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12. Genetic programming: crossover operators, building blocks and semantic emergence / Programação genética: operadores de crossover, blocos construtivos e emergência semântica
Evolutionary algorithms are heuristic methods used to find solutions to optimization problems. These methods use stochastic search mechanisms inspired by Natural Selection Theory. Genetic Algorithms and Genetic Programming are two of the most popular evolutionary algorithms. These techniques make intensive use of crossover operators, a mechanism responsible
Publicado em: 2010