SOCS

Sparse Optimal Control Software (SOCS). The Sparse Optimal Control Family, developed by The Boeing Company, contains two advanced software packages, available separately or together. Sparse Optimal Control Software (SOCS) is general-purpose software for solving optimal control problems. Applications include trajectory optimization, chemical process control and machine tool path definition. Sparse Nonlinear Programming exploits state-of-the-art sparse linear algebra technology to solve very large optimization problems orders of magnitude faster than traditional methods. Applications with more than 100,000 variables and constraints can now be solved efficiently on desktop computers. The Sparse Nonlinear Programming software is available as an integral part of SOCS or as a separate package.


References in zbMATH (referenced in 88 articles )

Showing results 1 to 20 of 88.
Sorted by year (citations)

1 2 3 4 5 next

  1. Drąg, Paweł; Styczeń, Krystyn: Process control with the variability constraints (2018)
  2. Magnusson, Fredrik; Åkesson, Johan: Symbolic elimination in dynamic optimization based on block-triangular ordering (2018)
  3. Putkaradze, Vakhtang; Rogers, Stuart: Constraint control of nonholonomic mechanical systems (2018)
  4. Quirynen, Rien; Gros, Sébastien; Diehl, Moritz: Inexact Newton-type optimization with iterated sensitivities (2018)
  5. Thammawichai, Mason; Kerrigan, Eric C.: Energy-efficient real-time scheduling for two-type heterogeneous multiprocessors (2018)
  6. Bara, O.; Djouadi, S.M.; Day, J.D.; Lenhart, S.: Immune therapeutic strategies using optimal controls with $L^1$ and $L^2$ type objectives (2017)
  7. Copp, David A.; Hespanha, João P.: Simultaneous nonlinear model predictive control and state estimation (2017)
  8. Dutra, Dimas Abreu Archanjo; Teixeira, Bruno Otávio Soares; Aguirre, Luis Antonio: Joint maximum \ita posteriori state path and parameter estimation in stochastic differential equations (2017)
  9. Ge, Ming; Kerrigan, Eric C.: Noise covariance identification for nonlinear systems using expectation maximization and moving horizon estimation (2017)
  10. Kelly, Matthew: An introduction to trajectory optimization: how to do your own direct collocation (2017)
  11. Lohéac, Jér^ome; Trélat, Emmanuel; Zuazua, Enrique: Minimal controllability time for the heat equation under unilateral state or control constraints (2017)
  12. Pappalardo, Carmine M.; Guida, Domenico: Control of nonlinear vibrations using the adjoint method (2017)
  13. Quirynen, Rien; Gros, Sébastien; Houska, Boris; Diehl, Moritz: Lifted collocation integrators for direct optimal control in ACADO toolkit (2017)
  14. Scheepmaker, Gerben M.; Goverde, Rob M.P.; Kroon, Leo G.: Review of energy-efficient train control and timetabling (2017)
  15. Zanon, Mario; Boccia, Andrea; Palma, Vryan Gil S.; Parenti, Sonja; Xausa, Ilaria: Direct optimal control and model predictive control (2017)
  16. Zhu, Jiamin; Trélat, Emmanuel; Cerf, Max: Geometric optimal control and applications to aerospace (2017)
  17. Betts, John T.; Campbell, Stephen L.; Thompson, Karmethia C.: Solving optimal control problems with control delays using direct transcription (2016)
  18. Boucher, Randy; Kang, Wei; Gong, Qi: Galerkin optimal control (2016)
  19. Campbell, Stephen; Kunkel, Peter: Solving higher index DAE optimal control problems (2016)
  20. Campbell, Stephen L.; Betts, John T.: Comments on direct transcription solution of DAE constrained optimal control problems with two discretization approaches (2016)

1 2 3 4 5 next