LGO

The program system LGO serves to solve global optimization problems under very mild -- continuity or Lipschitz-continuity -- structural assumptions. LGO is embedded into a menu-driven user interface which effectively assists the application development process. Implementation details, and several application areas are also highlighted.

Keywords for this software

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References in zbMATH (referenced in 96 articles , 1 standard article )

Showing results 1 to 20 of 96.
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  1. Gergel, Victor; Kozinov, Evgeniy; Barkalov, Konstantin: Computationally efficient approach for solving lexicographic multicriteria optimization problems (2021)
  2. Lera, Daniela; Posypkin, Mikhail; Sergeyev, Yaroslav D.: Space-filling curves for numerical approximation and visualization of solutions to systems of nonlinear inequalities with applications in robotics (2021)
  3. Sergeyev, Yaroslav D.; Nasso, Maria Chiara; Mukhametzhanov, Marat S.; Kvasov, Dmitri E.: Novel local tuning techniques for speeding up one-dimensional algorithms in expensive global optimization using Lipschitz derivatives (2021)
  4. Stripinis, Linas; Paulavičius, Remigijus: A new \textttDIRECT-GLh algorithm for global optimization with hidden constraints (2021)
  5. Bajaj, Ishan; Faruque Hasan, M. M.: Deterministic global derivative-free optimization of black-box problems with bounded Hessian (2020)
  6. Kvasov, Dmitri E.; Mukhametzhanov, Marat S.; Nasso, Maria Chiara; Sergeyev, Yaroslav D.: On acceleration of derivative-free univariate Lipschitz global optimization methods (2020)
  7. Strongin, R. G.; Gergel, V. P.; Barkalov, K. A.: Adaptive global optimization based on a block-recursive dimensionality reduction scheme (2020)
  8. Strongin, Roman; Barkalov, Konstantin; Bevzuk, Semen: Acceleration of global search by implementing dual estimates for Lipschitz constant (2020)
  9. Kampas, Frank J.; Castillo, Ignacio; Pintér, János D.: Optimized ellipse packings in regular polygons (2019)
  10. Najman, Jaromił; Mitsos, Alexander: On tightness and anchoring of McCormick and other relaxations (2019)
  11. Rahal, Mohamed; Abdelkader, Ziadi; Rachid, Ellaia: Generating (\alpha)-dense curves in non-convex sets to solve a class of non-smooth constrained global optimization (2019)
  12. Schmidt, Martin; Sirvent, Mathias; Wollner, Winnifried: A decomposition method for MINLPs with Lipschitz continuous nonlinearities (2019)
  13. Al-Dujaili, Abdullah; Suresh, S.: Multi-objective simultaneous optimistic optimization (2018)
  14. Barkalov, Konstantin; Strongin, Roman: Solving a set of global optimization problems by the parallel technique with uniform convergence (2018)
  15. Castagnotto, Alessandro; Lohmann, Boris: A new framework for (\mathcalH_2)-optimal model reduction (2018)
  16. Endres, Stefan C.; Sandrock, Carl; Focke, Walter W.: A simplicial homology algorithm for Lipschitz optimisation (2018)
  17. Evtushenko, Yuri; Posypkin, Mikhail; Rybak, Larisa; Turkin, Andrei: Approximating a solution set of nonlinear inequalities (2018)
  18. Gergel, Victor; Barkalov, Konstantin; Sysoyev, Alexander: Globalizer: a novel supercomputer software system for solving time-consuming global optimization problems (2018)
  19. Gergel, Victor; Kozinov, Evgeny: Efficient multicriterial optimization based on intensive reuse of search information (2018)
  20. Gimbutas, Albertas; Žilinskas, Antanas: An algorithm of simplicial Lipschitz optimization with the bi-criteria selection of simplices for the bi-section (2018)

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