Examples of using Optimization problems in English and their translations into German
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One example are optimization problems in high-dimensional spaces.
Formulating the design of power converters as optimization problems.
Methods for solving optimization problems[The text of the]: tutorial/ In.
Her research focus is on dealing with uncertainties in discrete optimization problems. Univ.-Prof.
Uncertainties occur in most practically relevant optimization problems: Often, not all of the important parameters are known in advance.
As a keynote speaker he held a presentation on the topic"Algorithms for Simulation-Based Optimization Problems.
Higgs field mechanism in optimization problems for soft boundaries.
The research group HEAL develops(nature-inspired) algorithms for solving complex optimization problems.
The students get to know the classification of optimization problems, modelling languages in optimization e. g.
Dynamic algorithms are required to use thataggregated data to solve supply chain optimization problems.
Using this data structure several optimization problems that are NP-complete in general can be solved efficiently or even in linear time.
Red Hat Process Automation Manager includes OptaPlanner,a tool for constructing solvers for complex optimization problems.
Many optimization problems cannot be solved by classical mathematical optimization techniques due to their complexity and the size of the solution space.
The German Science Foundation is funding theproject Preconditioned SQP Solvers for Nonlinear Optimization Problems with PDEs.
Abstract Many optimization problems cannot be solved by classical mathematicaloptimization techniques due to their complexity and the size of the solution space.
Subdifferentials are used for the description of necessary andsufficient optimality conditions for non-smooth optimization problems.
The students learn how to solve non-linear systems of equations and non-linear optimization problems with and without constraints mostly in an iterative and approximative way.
Heuristic and Evolutionary Algorithms The research group HEAL develops(nature-inspired)algorithms for solving complex optimization problems.
Robust optimization problems are often much more difficult than their deterministic analoga, even if the latter belong to a class of efficiently solvable problems such as linear programs.
In addition, we calculate material properties based on the nature of the raw materials used in production(material modeling)and solve optimization problems.
Within this project, optimality conditions for continuous dynamic optimization problems are investigated under weak regularity assumptions on the optimal controls key word: broken extremals.
The goal of this project e4-share is to lay the foundations for efficient and economically viable electric car-sharing systems by studying andsolving the optimization problems arising in their design and operations.
Theoretical insights into formally and rigorously defined optimization problems in this field will only be of limited use, since they typically do not cover all relevant aspects of graph clustering.
Other typical application areas of priority queues are algorithms for computing shortest paths in graphs(in which the edges carry a distance information), algorithms for computing maximum flows in network graphs(in which the edges carry a capacity information),coding algorithms, compression algorithms, and branch-and-bound algorithms for solving optimization problems.
Content: This module communicates the theoretical fundamentals andsolution methods for optimization problems in the service context focusing on Supply Chain Management und Health Care.
Optimization Problems in Energy Management Optimization problems in energy management are concerned with the planning of production and distribution of different energy sources(power, gas), in order to cover a given customer's demand.
The central problem is thesymbolic approximation of the occurring differential equations and optimization problems in such a way that the voltage is represented as a polynomial in the original state and the incoming signals.
Prominent research topics are optimization problems constrained by stationary as well as dynamic systems that require both discrete as well as continuous decisions, optimization problems on discrete network structures or switching systems of differential equations.
This includes the formulation of the problems as mathematical optimization problems, the analysis of various methods for optimization and the development of efficient heuristic algorithms, which can be used in real network.
Arelion GmbH in Pasching near Linz(Austria)develops and sells software that solves quantitative optimization problems in the areas of production, logistics, transportation and warehousing. At the heart of the technology is an evolutionary optimization method that is capable of swiftly solving even very complex problems to an extremely high standard, unlike other established approaches. Arelion's software is particularly suited for applications with continually changing boundary conditions typically encountered in dynamic environments. Lean implementation processes reduce project risk significantly.