Taylor and Francis Ltd.
+2
1029-4937
Bi-Monthly
1055-6788
1992
4402080520500
United Kingdom
English
YES
Google Scholar
ulbrich@mathematik.tu-darmstadt.de
Optimization Methods and Software publishes refereed papers on the latest developments in the theory and realization of optimization methods, with particular emphasis on the interface between software development and algorithm design. Theory, implementation and performance evaluation of algorithms and computer codes for linear, nonlinear, discrete, stochastic optimization and optimal control. This includes in particular conic, semi-definite, mixed integer, network, non-smooth, multi-objective and global optimization by deterministic or nondeterministic algorithms. Algorithms and software for complementarity, variational inequalities and equilibrium problems, and also for solving inverse problems, systems of nonlinear equations and the numerical study of parameter dependent operators. Various aspects of efficient and user-friendly implementations: e.g. automatic differentiation, massively parallel optimization, distributed computing, on-line algorithms, error sensitivity and validity analysis, problem scaling, stopping criteria and symbolic numeric interfaces. Theoretical studies with clear potential for applications and successful applications of specially adapted optimization methods and software to fields like engineering, machine learning, data mining, economics, finance, biology, or medicine. These submissions should not consist solely of the straightforward use of standard optimization techniques.
This research investigates the parallel-batching scheduling problems with a time-dependent learning effect where the job processing time is a decreasing function of its starting time. Both t...
We propose a novel algorithm portfolio model that incorporates time series forecasting techniques to predict online the performance of its constituent algorithms. The predictions are used to...
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