The COCONUT Environment is a modular solver environment for nonlinear continuous global optimization problems with an open-source kernel, which can be expanded by commercial and open-source solver components (inference engines). The first test version of the environment was provided back in 2004 by the COCONUT consortium. Since then it has been under continuous improvement and stabilization, in particular, during the project P18704-N13 sponsored by FWF, the Austrian Science Foundation. The full source of the current development version including the solvers, the strategy engine and the converters is available via a subversion repository. In addition, we provide a binary compiled version of the converters and the two available global optimization solvers. For more information on obtaining the COCONUT sources and binaries please visit the download section. The application programmer’s! interface (API) is designed to make the development of the various module types independent of each other and independent of the internal model representation. It is a collection of open-source C++ classes protected by the LGPL and GPL license models, so that most of it could be used as part of commercial software (special license regulations are contained in the distribution, and they can be read here). It uses the FILIB++ library for interval computations and the matrix template library (MTL) for the internal representation of various matrix classes. The graphs are implemented using the VGTL (Vienna Graph Template Library), and the search database is based on the VDBL (Vienna DataBaseLibrary). Support for dynamic linking relieves the user from recompilation when modules are added or removed. In addition, it is designed for distributed computing, and will probably be developed further (in the years after the end of the COCONUT project) to support parallel computing as wel! l. The solution algorithm is an advanced branch-and-bound scheme which proceeds by working on the search graph, a directed acyclic graph (DAG) of search nodes, each representing an optimization problem, a model. The search nodes come in two flavors: full nodes which record the complete description of a model, and delta nodes which only contain the difference between the model represented by the node and its (then only) parent.

References in zbMATH (referenced in 31 articles , 1 standard article )

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  1. Puranik, Yash; Sahinidis, Nikolaos V.: Bounds tightening based on optimality conditions for nonconvex box-constrained optimization (2017)
  2. Araya, Ignacio; Reyes, Victor: Interval branch-and-bound algorithms for optimization and constraint satisfaction: a survey and prospects (2016)
  3. Domes, Ferenc; Neumaier, Arnold: Linear and parabolic relaxations for quadratic constraints (2016)
  4. Domes, Ferenc; Neumaier, Arnold: Constraint aggregation for rigorous global optimization (2016)
  5. Hannes Fendl, Hermann Schichl: A feasible second order bundle algorithm for nonsmooth nonconvex optimization problems with inequality constraints: II. Implementation and numerical results (2015) arXiv
  6. Neveu, Bertrand; Trombettoni, Gilles; Araya, Ignacio: Adaptive constructive interval disjunction: algorithms and experiments (2015)
  7. Ninin, Jordan; Messine, Frédéric; Hansen, Pierre: A reliable affine relaxation method for global optimization (2015)
  8. Araya, Ignacio; Trombettoni, Gilles; Neveu, Bertrand; Chabert, Gilles: Upper bounding in inner regions for global optimization under inequality constraints (2014)
  9. Kearfott, Ralph Baker; Castille, Jessie M.; Tyagi, Gaurav: Assessment of a non-adaptive deterministic global optimization algorithm for problems with low-dimensional non-convex subspaces (2014)
  10. Markót, Mihály Csaba; Schichl, Hermann: Bound constrained interval global optimization in the COCONUT environment (2014)
  11. Schichl, Hermann; Markót, Mihály Csaba; Neumaier, Arnold: Exclusion regions for optimization problems (2014)
  12. Schulze Darup, Moritz; Kastsian, Martin; Mross, Stefan; Mönnigmann, Martin: Efficient computation of spectral bounds for Hessian matrices on hyperrectangles for global optimization (2014)
  13. Fazal, Qaisra; Neumaier, Arnold: Error bounds for initial value problems by optimization (2013)
  14. Kearfott, Ralph Baker; Castille, Jessie; Tyagi, Gaurav: A general framework for convexity analysis in deterministic global optimization (2013)
  15. Pintér, János D.; Kampas, Frank J.: Benchmarking nonlinear optimization software in technical computing environments (2013)
  16. Domes, Ferenc; Neumaier, Arnold: Rigorous filtering using linear relaxations (2012)
  17. Schichl, Hermann; Markót, Mihály Csaba: Algorithmic differentiation techniques for global optimization in the COCONUT environment (2012)
  18. Domes, Ferenc; Neumaier, Arnold: Rigorous enclosures of ellipsoids and directed Cholesky factorizations (2011)
  19. Markót, Mihály Csaba; Schichl, Hermann: Comparison and automated selection of local optimization solvers for interval global optimization methods (2011)
  20. Nannicini, Giacomo; Belotti, Pietro; Lee, Jon; Linderoth, Jeff; Margot, François; Wächter, Andreas: A probing algorithm for MINLP with failure prediction by SVM (2011)

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