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Auflistung nach Schlagwort "Mathematical optimization"

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  • Zeitschriftenartikel
    Mathematical Optimization and Algorithms for Offshore Wind Farm Design: An Overview
    (Business & Information Systems Engineering: Vol. 61, No. 4, 2019) Fischetti, Martina; Pisinger, David
    Wind energy is a fast evolving field that has attracted a lot of attention and investments in the last decades. Being an increasingly competitive market, it is very important to minimize establishment costs and increase production profits already at the design phase of new wind parks. This paper is based on many years of collaboration with Vattenfall, a leading wind energy developer and wind power operator, and aims at giving an overview of the experience of using Mathematical Optimization in the field. The paper illustrates some of the practical needs defined by energy companies, showing how optimization can help the designers to increase production and reduce costs in the design of offshore parks. In particular, the study gives an overview of the individual phases of designing an offshore wind farm, and some of the optimization problems involved. Finally it goes in depth with three of the most important optimization tasks: turbine location, electrical cable routing and foundation optimization. The paper is concluded with a discussion of future challenges.
  • Zeitschriftenartikel
    Optimization frameworks for machine learning: Examples and case study
    (it - Information Technology: Vol. 62, No. 3-4, 2020) Giesen, Joachim; Laue, Sören; Mitterreiter, Matthias
    Mathematical optimization is at the algorithmic core of machine learning. Almost any known algorithm for solving mathematical optimization problems has been applied in machine learning and the machine learning community itself is actively designing and implementing new algorithms for specific problems. These implementations have to be made available to machine learning practitioners which is mostly accomplished by distributing them as standalone software. Successful well-engineered implementations are collected in machine learning toolboxes that provide a more uniform access to the different solvers. A disadvantage of the toolbox approach is a lack of flexibility as toolboxes only provide access to a fixed set of machine learning models that cannot be modified. This can be a problem for the typical machine learning workflow that iterates the process of modeling, solving and validating. If a model does not perform well on validation data, it needs to be modified. In most cases these modifications require a new solver for the entailed optimization problems. Optimization frameworks that combine a modeling language for specifying optimization problems with a solver are better suited to the iterative workflow since they allow to address large problem classes. Here, we provide examples of the use of optimization frameworks in machine learning. We also illustrate the use of one such framework in a case study that follows the typical machine learning workflow.