LIBOPT

The Libopt environment is both a methodology and a set of tools that can be used for testing, comparing, and profiling solvers on problems belonging to various collections. These collections can be heterogeneous in the sense that their problems can have common features that differ from one collection to the other. Libopt brings a unified view on this composite world by offering, for example, the possibility to run any solver on any problem compatible with it, using the same Unix/Linux command. The environment also provides tools for comparing the results obtained by solvers on a specified set of problems. Most of the scripts going with the Libopt environment have been written in Perl

Keywords for this software

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References in zbMATH (referenced in 9 articles )

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  1. Carpentier, P.; Chancelier, J. -Ph.; Leclère, V.; Pacaud, F.: Stochastic decomposition applied to large-scale hydro valleys management (2018)
  2. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
  3. Souopgui, Innocent; Ngodock, Hans E.; Vidard, Arthur; Le Dimet, François-Xavier: Incremental projection approach of regularization for inverse problems (2016)
  4. Souopgui, Innocent; Wieland, Scott A.; Yousuff Hussaini, M.; Vasilyev, Oleg V.: Space-time adaptive approach to variational data assimilation using wavelets (2016)
  5. Domes, Ferenc; Fuchs, Martin; Schichl, Hermann; Neumaier, Arnold: The optimization test environment (2014)
  6. Apostolopoulou, M. S.; Sotiropoulos, D. G.; Botsaris, C. A.; Pintelas, P.: A practical method for solving large-scale TRS (2011)
  7. Apostolopoulou, M. S.; Sotiropoulos, D. G.; Botsaris, C. A.: A curvilinear method based on minimal-memory BFGS updates (2010)
  8. Livieris, I. E.; Pintelas, P.: Performance evaluation of descent CG methods for neural network training (2010)
  9. Gilbert, Jean Charles; Jonsson, Xavier: LIBOPT - an environment for testing solvers on heterogeneous collections of problems - version 1.0 (2007) ioport