[Arnold E. and Puta H. (1994): An SQP-type solution method for constrained discrete-time optimal control problems. In: Computational Optimal Control (R. Bulirsch and D. Kraft, Eds.), Birkhäuser Verlag, Basel, Switzerland, pp. 127-136.10.1007/978-3-0348-8497-6_11]Search in Google Scholar
[Arnold E., Tatjewski P. and Wołochowicz P. (1994): Two methods for large-scale nonlinear optimization and their comparison on a case study of hydropower optimization.Journal of Optimization Theory and Applications, Vol. 81, No. 2, pp. 221-248.10.1007/BF02191662]Search in Google Scholar
[Benson H. Y., Shanno D. F. and Vanderbei R. J. (2001): Interior-point methods for nonconvex nonlinear programming: Filter methods and merit functions. Technical Report ORFE-00-06, Operations Research and Financial Engineering, Princeton University. Available at http://www.princeton.edu/~rvdb/tex/loqo4/loqo4_4.pdf]Search in Google Scholar
[Benson H. Y., Shanno D. F. and Vanderbei R. J. (2002): A comparative study of large-scale nonlinear optimization algorithms. Technical Report ORFE-01-04, Operations Research and Financial Engineering, Princeton University. Available at http://www.princeton.edu/~rvdb/tex/loqo5/loqo5_5.pdf]Search in Google Scholar
[Bertsekas D. P. (1982): Projected Newton methods for optimization problems with simple constraints.SIAM Journal on Control and Optimization, Vol. 20, No. 2, pp. 221-246.10.1137/0320018]Search in Google Scholar
[Błaszczyk J., Karbowski A. and Malinowski K. (2002a): Object library of algorithms for unconstrained dynamic optimization problems. Proceedings of the 14-th National Conference on Automatic Control (KKA), Vol. I, Zielona Góra, Poland, pp. 451-456.]Search in Google Scholar
[Błaszczyk J., Karbowski A. and Malinowski K. (2002b): Object library of algorithms for dynamic optimization problems without constraints or with simple bounds on control. Proceedings of the 8th IEEE International Conference on Methods and Models in Automation and Robotics, Vol. 1, Szczecin, Poland, pp. 257-262.]Search in Google Scholar
[Błaszczyk J., Karbowski A. and Malinowski K. (2003): Object library of algorithms for dynamic optimization problems with general constraints. Proceedings of the 9th IEEE International Conference on Methods and Models in Automation and Robotics, Vol. 1, Międzyzdroje, Poland, pp. 271-276.]Search in Google Scholar
[Bryson A. E. (1998): Dynamic Optimization. Menlo Park CA: Addison-Wesley, p. 550.]Search in Google Scholar
[Byrd R. H., Hribar M. E. and J. Nocedal (1999): An interior point algorithm for large scale nonlinear programming.SIAM Journal on Optimization, Vol. 9, No. 4, pp. 877-900.]Search in Google Scholar
[Byrd R. H., Gilbert J. Ch. and Nocedal J. (2000): A trust region method based on interior point techniques for nonlinear programming.Mathematical Programming, Vol. 89, pp. 149-185.10.1007/PL00011391]Search in Google Scholar
[Chamberlain R. M., Powell M. J. D., Lemarechal C. and Pedersen H. C. (1982): The watchdog technique for forcing convergence in algorithms for constrained optimization.Mathematical Programming Study, Vol. 16, pp. 1-17.10.1007/BFb0120945]Search in Google Scholar
[Dolan E. D. and Moré J. J. (2002): Benchmarking optimization software with performance profiles.Mathematical Programming, Vol. 91, No. 2, pp. 201-213.10.1007/s101070100263]Search in Google Scholar
[Fiacco A. V. and McCormick G. P. (1968): Nonlinear Programming: Sequential Unconstrained Minimization Techniques. John Wiley and Sons, New York/London.]Search in Google Scholar
[Findeisen W., Szymanowski J. and Wierzbicki A. (1980): Theory and Computational Methods of Optimization. Polish Scientific Publishers, Warsaw (in Polish).]Search in Google Scholar
[Fletcher R. (1987): Practical Methods of Optimization. John Wiley and Sons, New York, NY, USA.]Search in Google Scholar
[Fletcher R. (1995): An optimal positive definite update for sparse Hessian matrices.SIAM Journal on Optimization, Vol. 5, No. 1, pp. 192-218.10.1137/0805010]Search in Google Scholar
[Fletcher R. and Leyffer S. (2002): Nonlinear programming without a penalty function.Mathematical Programming, Vol. 91, No. 2, pp. 239-269.10.1007/s101070100244]Search in Google Scholar
[Fletcher R., Leyffer S. and Toint Ph. L. (2006): A brief history of filter methods. Technical Report ANL/MCS-P1372-0906, Mathematics and Computer Science Division, Argonne National Laboratory. Available at http://www.optimization-online.org/DB_FILE/2006/10/1489.pdf]Search in Google Scholar
[Franke R. (1994): Anwendung von Interior-Point-Methoden zur Lösung zeitdiskreter Optimalsteuerungsprobleme. M.S. thesis, Techniche Universität Ilmenau, Fakultät für Informatik und Automatsierung, Institut für Automatisierungsund Systemtechnik Fachgebiet Dynamik und Simulation ökologischer Systeme, Ilmenau, Germany, (in German).]Search in Google Scholar
[Franke R. (1998): OMUSES -A tool for the optimization of multistage systems and HQP - A solver for sparse nonlinear optimization. Version 1.5. Department of Automation and Systems Engineering, Technical University of Ilmenau, Germany. Available at ftp://ftp.systemtechnik.tu-ilmenau.de/pub/reports/omuses.ps.gz]Search in Google Scholar
[Franke R. and Arnold E. (1997): Applying new numerical algorithms to the solution of discrete-time optimal control problems. In: Computer-Intensive Methods in Control and Signal Processing: The Curse of Dimensionality, (Warwick K. and Kárný M., Eds.), Birkhäuser Verlag, Basel, Switzerland, pp. 105-118.10.1007/978-1-4612-1996-5_6]Search in Google Scholar
[The solver Omuses/HQP for structured large-scale constrained optimization: Algorithm, implementation, and example application. Proceedings of the 6-th SIAM Conference on Optimization, Atlanta.]Search in Google Scholar
[Goldfarb D. and Idnani A. (1983): A numerically stable dual method for solving strictly convex quadratic programs.Mathematical Programming, Vol. 27, No. 1, pp. 1-33.10.1007/BF02591962]Search in Google Scholar
[Gondzio J. (1994): Multiple centrality corrections in a primal-dual method for linear programming. Technical Report. 20, Department of Management Studies, University of Geneva, Geneva, Switzerland. Available at http://www.maths.ed.ac.uk/~gondzio/software/correctors.ps]Search in Google Scholar
[Griewank A., Juedes D., Mitev H., Utke J., Vogel O. and Walther A. (1999): ADOL-C: A package for the automatic differentiation of algorithms written in C/C++, Version 1.8.2, March 1999. Available at http://www.math.tu-dresden.de/~adol-c/]Search in Google Scholar
[Karmarkar N. (1984): A new polynomial-time algorithm for linear programming.Combinatorica, Vol. 4, No. 4, pp. 373-395.10.1007/BF02579150]Search in Google Scholar
[Luus R. (2000): Iterative Dynamic Programming. CRC Press, Inc., Boca Raton, FL, USA.]Search in Google Scholar
[Mehrotra S. (1992): On the implementation of a primal-dual interior point method.SIAM Journal on Optimization, Vol. 2, No. 4, pp. 575-601.10.1137/0802028]Search in Google Scholar
[Misc J.-P. (2003): Large scale nonconvex optimization.SIAM's SIAG/OPT Newsletter Views-and-News, Vol. 14, No. 1, pp. 1-25. Available at http://fewcal.uvt.nl/sturm/siagopt/vn14_1.pdf]Search in Google Scholar
[Morales J. L., Nocedal J., Waltz R. A., Liu G. and Goux J.-P. (2001): Assessing the potential of interior methods for nonlinear optimization. Technical Report OTC 2001/4, Optimization Technology Center of Northwestern University. Available at http://www.ece.northwestern.edu/~morales/PSfiles/assess.ps]Search in Google Scholar
[Nocedal J. and Wright S. J. (1999): Numerical Optimization. Berlin: Springer-Verlag.10.1007/b98874]Search in Google Scholar
[De O. Pantoja J. F.A. (1988): Differential dynamic programming and Newton's method.International Journal of Control, Vol. 47, No. 5, pp. 1539-1553.10.1080/00207178808906114]Search in Google Scholar
[Powell M. J. D. (1978): A fast algorithm for nonlinearly constrained optimization calculations. In: Numerical Analysis, Dundee (G. A. Watson, Ed.), Dundee: Springer-Verlag, pp. 144-157.10.1007/BFb0067703]Search in Google Scholar
[Powell M. J. D. (1985): On the quadratic programming algorithm of Goldfarb and Idnani.Mathematical Programming Study, Vol. 25, pp. 46-61.10.1007/BFb0121074]Search in Google Scholar
[Salahi M., Peng J. and Terlaky T. (2005): On Mehrotratype predictor-corrector algorithms. Technical report, Advanced Optimization Lab, Department of Computing and Software, McMaster University, Hamilton, Ontario, Canada. Available at http://www.optimization-online.org/DB_FILE/2005/03/1104.pdf]Search in Google Scholar
[Schittkowski K. (1980): Nonlinear Programming Codes: Information, Tests, Performance. Berlin: Springer-Verlag.10.1007/978-3-642-46424-9]Search in Google Scholar
[Schittkowski K. (1983): On the convergence of a sequential quadratic programming method with an augmented Lagrangian line search function. Mathematishe Operations Forschung und Statistik, Ser. Optimization, Vol. 14, No. 2, pp. 197-216.10.1080/02331938308842847]Search in Google Scholar
[Schwartz A. and Polak E. (1997): Family of projected descent methods for optimization problems with simple bounds.Journal of Optimization Theory and Applications, Vol. 92, No. 1, pp. 1-31.10.1023/A:1022690711754]Search in Google Scholar
[Tenny M. J., Wright S. J. and Rawlings J. B. (2002): Nonlinear model predictive control via feasibility-perturbed sequential quadratic programming. Technical Report TWMCC-2002-02, Texas-Wisconsin Modeling and Control Consortium. Available at http://jbrwww.che.wisc.edu/jbr-group/tech-reports/twmcc-2002-02.pdf]Search in Google Scholar
[Tits A. L., Wächter A., Bakhtiari S., Urban T. J. and Lawrence C.T. (2002): A primal-dual interior-point method for nonlinear programming with strong global and local convergence properties. Technical Report TR 2002-29, Institute for Systems Research, University of Maryland. Available at http://www.ee.umd.edu/~andre/pdiprev.ps]Search in Google Scholar
[Ulbrich M., Ulbrich S. and Vicente L. N. (2004): A globally convergent primal-dual interior-point filter method for nonlinear programming.Mathematical Programming, Vol. 100, No. 2, pp. 379-410.10.1007/s10107-003-0477-4]Search in Google Scholar
[Vanderbei R. J. and Shanno D. F. (1997): An interior-point algorithm for non-convex nonlinear programming. Technical Report SOR-97-21, Statistics and Operations Research, Princeton University. Available at http://www.sor.princeton.edu/~rvdb/ps/nonlin.ps.gz]Search in Google Scholar
[Wächter A. (2002): An Interior Point Algorithm for Large-Scale Nonlinear Optimization with Applications in Process Engineering. Ph.D. dissertation, Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. Available at http://www.research.ibm.com/people/a/andreasw/papers/thesis.pdf]Search in Google Scholar
[Wächter A. and Biegler L. T. (2000): Failure of global convergence for a class of interior point methods for nonlinear programming.Mathematical Programming, Vol. 88, No. 3, pp. 565-574.10.1007/PL00011386]Search in Google Scholar
[Wächter A. and Biegler L. T. (2005): Line search filter methods for nonlinear programming: Motivation and global convergence.SIAM Journal on Optimization, Vol. 16, No. 1, pp. 1-31.10.1137/S1052623403426556]Search in Google Scholar
[Wächter A. and Biegler L. T. (2006): On the implementation of a primal-dual interior-point filter line-search algorithm for large-scale nonlinear programming.Mathematical Programming, Vol. 106, No. 1, pp. 25-57.10.1007/s10107-004-0559-y]Search in Google Scholar
[Waltz R. A. and Plantenga T. (2006): KNITRO 5.0 User's Manual. Available at http://www.ziena.com/docs/knitroman.pdf]Search in Google Scholar
[Wierzbicki A. (1984): Models and Sensitivity of Control Systems. Elsevier, Amsterdam.]Search in Google Scholar
[Wright S. J. (1993): Interior point methods for optimal control of discrete time systems.Journal of Optimization Theory and Applications, Vol. 77, No. 1, pp. 161-187.10.1007/BF00940784]Search in Google Scholar
[Wright S. J. (1997): Primal-Dual Interior-Point Methods. SIAM, Philadelphia, PA.10.1137/1.9781611971453]Search in Google Scholar
[Yakowitz S. and Rutherford B. (1984): Computational aspects of discrete-time optimal control.Applied Mathematics and Computation, Vol. 15, No. 1, pp. 29-45.10.1016/0096-3003(84)90051-1]Search in Google Scholar