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 105 articles )

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  1. Ha, Jung-Su; Choi, Han-Lim: On periodic optimal solutions of persistent sensor planning for continuous-time linear systems (2019)
  2. Ramezani, Mohammad Hossein; Sadati, Nasser: Hierarchical optimal control of a binary distillation column (2019)
  3. Drąg, Paweł; Styczeń, Krystyn: Process control with the variability constraints (2018)
  4. 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)
  5. Huber, Andreas; Gerdts, Matthias; Bertolazzi, Enrico: Structure exploitation in an interior-point method for fully discretized, state constrained optimal control problems (2018)
  6. Jason K. Moore; Antonie van den Bogert: opty: Software for trajectory optimization and parameter identification using direct collocation (2018) not zbMATH
  7. Magnusson, Fredrik; Åkesson, Johan: Symbolic elimination in dynamic optimization based on block-triangular ordering (2018)
  8. Mahdi Ghazaei Ardakani, M.; Magnusson, Fredrik: Ball-and-finger system: modeling and optimal trajectories (2018)
  9. 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)
  10. Nicholson, Bethany L.; Wan, Wei; Kameswaran, Shivakumar; Biegler, Lorenz T.: Parallel cyclic reduction strategies for linear systems that arise in dynamic optimization problems (2018)
  11. Nicholson, Bethany; Siirola, John D.; Watson, Jean-Paul; Zavala, Victor M.; Biegler, Lorenz T.: \textttpyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations (2018)
  12. Nikooeinejad, Z.; Delavarkhalafi, A.; Heydari, M.: Application of shifted Jacobi pseudospectral method for solving (in)finite-horizon min-max optimal control problems with uncertainty (2018)
  13. Olivares, Alberto; Staffetti, Ernesto: Switching time-optimal control of spacecraft equipped with reaction wheels and gas jet thrusters (2018)
  14. Putkaradze, Vakhtang; Rogers, Stuart: Constraint control of nonholonomic mechanical systems (2018)
  15. Quirynen, Rien; Gros, Sébastien; Diehl, Moritz: Inexact Newton-type optimization with iterated sensitivities (2018)
  16. Römer, Ulrich; Narayanamurthi, Mahesh; Sandu, Adrian: Solving parameter estimation problems with discrete adjoint exponential integrators (2018)
  17. Semmler, Willi; Maurer, Helmut; Bonen, Anthony: An extended integrated assessment model for mitigation and adaptation policies on climate change (2018)
  18. Thammawichai, Mason; Kerrigan, Eric C.: Energy-efficient real-time scheduling for two-type heterogeneous multiprocessors (2018)
  19. 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)
  20. Copp, David A.; Hespanha, João P.: Simultaneous nonlinear model predictive control and state estimation (2017)

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