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 119 articles )

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  1. Bolhasani, Ehsan; Azizi, Yousef; Abdollahpour, Daryoush; Amjad, Jafar M.; Perc, Matjaž: Control of dynamics via identical time-lagged stochastic inputs (2020)
  2. Dutra, Dimas Abreu Archanjo: Uncertainty estimation in equality-constrained MAP and maximum likelihood estimation with applications to system identification and state estimation (2020)
  3. Hoffmann, Ramona; Taetz, Bertram; Miezal, Markus; Bleser, Gabriele; Leyendecker, Sigrid: On optical data-guided optimal control simulations of human motion (2020)
  4. Kuřátko, Jan; Ratschan, Stefan: Solving reachability problems by a scalable constrained optimization method (2020)
  5. Yazdaniyan, Z.; Shamsi, M.; Foroozandeh, Z.; de Pinho, Maria do Rosário: A numerical method based on the complementarity and optimal control formulations for solving a family of zero-sum pursuit-evasion differential games (2020)
  6. Zhao, Jisong; Li, Shuang: Adaptive mesh refinement method for solving optimal control problems using interpolation error analysis and improved data compression (2020)
  7. Foroozandeh, Z.; Shamsi, M.; de Pinho, M. d. R.: A mixed-binary non-linear programming approach for the numerical solution of a family of singular optimal control problems (2019)
  8. Ha, Jung-Su; Choi, Han-Lim: On periodic optimal solutions of persistent sensor planning for continuous-time linear systems (2019)
  9. Lismonde, Arthur; Sonneville, Valentin; Brüls, Olivier: A geometric optimization method for the trajectory planning of flexible manipulators (2019)
  10. Liu, Ping; Liu, Xinggao; Wang, Ping; Li, Guodong; Xiao, Long; Yan, Jie; Ren, Zhang: Control variable parameterisation with penalty approach for hypersonic vehicle reentry optimisation (2019)
  11. Miyaoka, Tiago Yuzo; Lenhart, Suzanne; Meyer, João F. C. A.: Optimal control of vaccination in a vector-Borne reaction -- diffusion model applied to Zika virus (2019)
  12. Ramezani, Mohammad Hossein; Sadati, Nasser: Hierarchical optimal control of a binary distillation column (2019)
  13. Drąg, Paweł; Styczeń, Krystyn: Process control with the variability constraints (2018)
  14. Foroozandeh, Z.; Shamsi, M.; Do Rosário De Pinho, Maria: A hybrid direct-indirect approach for solving the singular optimal control problems of finite and infinite order (2018)
  15. Huber, Andreas; Gerdts, Matthias; Bertolazzi, Enrico: Structure exploitation in an interior-point method for fully discretized, state constrained optimal control problems (2018)
  16. Jason K. Moore; Antonie van den Bogert: opty: Software for trajectory optimization and parameter identification using direct collocation (2018) not zbMATH
  17. Magnusson, Fredrik; Åkesson, Johan: Symbolic elimination in dynamic optimization based on block-triangular ordering (2018)
  18. Mahdi Ghazaei Ardakani, M.; Magnusson, Fredrik: Ball-and-finger system: modeling and optimal trajectories (2018)
  19. Mennemann, J.-F.; Marko, L.; Schmidt, J.; Kemmetmüller, W.; Kugi, A.: The spectral element method as an efficient tool for transient simulations of hydraulic systems (2018)
  20. Mulla, Ameer K.; Patil, Deepak U.; Chakraborty, Debraj: Computation of the target state and feedback controls for time optimal consensus in multi-agent systems (2018)

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