TOMP

Algorithm 733: TOMP - Fortran modules for optimal control calculations. A great number of analysis and synthesis problems of modern processes can be written as state and control constrained optimal control problems governed by ordinary differential equations with multipoint boundary values. As the software tools for following this attractive approach are still missing or can be used only by experts, the structure and usage of an easy-to-use software package is described which efficiently solves the given problem. Among its features are user-orientation, applicability on personal computers and mainframes, and robustness with respect to model changes and inaccurate starting values. It has been tested on a number of complex engineering tasks, including aerospace and robotic trajectory planning. (Source: http://dl.acm.org/)

This software is also peer reviewed by journal TOMS.


References in zbMATH (referenced in 32 articles , 1 standard article )

Showing results 1 to 20 of 32.
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  1. Höppner, Sebastiaan; Baesens, Bart; Verbeke, Wouter; Verdonck, Tim: Instance-dependent cost-sensitive learning for detecting transfer fraud (2022)
  2. Azzolini, Damiano; Riguzzi, Fabrizio: Optimizing probabilities in probabilistic logic programs (2021)
  3. Burgard, Jan Pablo; Krause, Joscha; Schmaus, Simon: Estimation of regional transition probabilities for spatial dynamic microsimulations from survey data lacking in regional detail (2021)
  4. Schweidtmann, Artur M.; Bongartz, Dominik; Grothe, Daniel; Kerkenhoff, Tim; Lin, Xiaopeng; Najman, Jaromił; Mitsos, Alexander: Deterministic global optimization with Gaussian processes embedded (2021)
  5. Singh, Sukhminder; Pflug, Lukas; Stingl, Michael: Material optimization to enhance delamination resistance of composite structures using viscous regularization (2021)
  6. Wang, Aijuan; Liu, Wanping; Li, Tiehu; Huang, Tingwen: Privacy-preserving weighted average consensus and optimal attacking strategy for multi-agent networks (2021)
  7. Calzavara, Stefano; Lucchese, Claudio; Tolomei, Gabriele; Abebe, Seyum Assefa; Orlando, Salvatore: \textscTreant: training evasion-aware decision trees (2020)
  8. Chan Beom Park: YAM2: Yet another library for the M2 variables using sequential quadratic programming (2020) arXiv
  9. Blond, Maxence; Simon, Daniel; Creuze, Vincent; Tempier, Olivier: Optimal thrusters steering for dynamically reconfigurable underwater vehicles (2019)
  10. Meshcheryakov Georgy, Igolkina Anna: semopy: A Python package for Structural Equation Modeling (2019) arXiv
  11. Chiquete, Carlos; Short, Mark; Meyer, Chad D.; Quirk, James J.: Calibration of the pseudo-reaction-zone model for detonation wave propagation (2018)
  12. Endres, Stefan C.; Sandrock, Carl; Focke, Walter W.: A simplicial homology algorithm for Lipschitz optimisation (2018)
  13. Bašić, Josip; Degiuli, Nastia; Ban, Dario: Assessment of d’Alembert’s paradox in panel methods by tangency correction (2017)
  14. Bongartz, Dominik; Mitsos, Alexander: Deterministic global optimization of process flowsheets in a reduced space using McCormick relaxations (2017)
  15. Dunning, Peter D.; Ovtchinnikov, Evgueni; Scott, Jennifer; Kim, H. Alicia: Level-set topology optimization with many linear buckling constraints using an efficient and robust eigensolver (2016)
  16. Aragón, Alejandro M.; Molinari, Jean-François: A hierarchical detection framework for computational contact mechanics (2014)
  17. Fabien, Brian C.: Parallel indirect solution of optimal control problems (2014)
  18. Lantoine, Gregory; Russell, Ryan P.: A hybrid differential dynamic programming algorithm for constrained optimal control problems. I: Theory (2012)
  19. Fabien, Brian C.: \textttdsoa: the implementation of a dynamic system optimization algorithm (2010)
  20. Morzhin, O. V.; Tyatyushkin, A. I.: On optimization of position control in attainability tube in a model problem (2010)

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