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Many important problems in contemporary machine learning involve solving highly non- convex problems in sampling, optimization, or games. The absence of convexity poses significant challenges to convergence analysis of most training algorithms, and in some ...
Recently the optimisation of end-of-life (EOL) computer remanufacturing has been highlighted since a big amount of used computers have been disposed of every year. Each part inspected after disassembling EOL computers can have various EOL options such as r ...
This paper presents an integrated approach for short-term supply chain management (SCM) at a fast moving consumer goods production plant. The problem is to determine the production quantities, to provide a detailed production schedule, to trigger the relev ...
The family of natural evolution strategies (NES) offers a principled approach to real-valued evolutionary optimization by following the natural gradient of the expected fitness. Like the well-known CMA-ES, the most competitive algorithm in the field, NES c ...
Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but ...
In this thesis, we focus on standard classes of problems in numerical optimization: unconstrained nonlinear optimization as well as systems of nonlinear equations. More precisely, we consider two types of unconstrained nonlinear optimization problems. On t ...
Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors. As the number of possible functions exponentially grows with the variables under consideration, analysts need to spend increasing amounts of ti ...
