Ipopt

Ipopt (Interior Point OPTimizer, pronounced eye-pea-Opt) is a software package for large-scale ​nonlinear optimization. It is designed to find (local) solutions of mathematical optimization problems of the from min f(x), x in R^n s.t. g_L <= g(x) <= g_U, x_L <= x <= x_U, where f(x): R^n --> R is the objective function, and g(x): R^n --> R^m are the constraint functions. The vectors g_L and g_U denote the lower and upper bounds on the constraints, and the vectors x_L and x_U are the bounds on the variables x. The functions f(x) and g(x) can be nonlinear and nonconvex, but should be twice continuously differentiable. Note that equality constraints can be formulated in the above formulation by setting the corresponding components of g_L and g_U to the same value. Ipopt is part of the ​COIN-OR Initiative.


References in zbMATH (referenced in 587 articles )

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  1. Betts, John T.; Campbell, Stephen L.; Digirolamo, Claire: Initial guess sensitivity in computational optimal control problems (2020)
  2. Biccari, Umberto; Warma, Mahamadi; Zuazua, Enrique: Controllability of the one-dimensional fractional heat equation under positivity constraints (2020)
  3. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Sparsity in optimal randomized classification trees (2020)
  4. Charrondière, Raphaël; Bertails-Descoubes, Florence; Neukirch, Sébastien; Romero, Victor: Numerical modeling of inextensible elastic ribbons with curvature-based elements (2020)
  5. Chen, Rui; Qian, Xinwu; Miao, Lixin; Ukkusuri, Satish V.: Optimal charging facility location and capacity for electric vehicles considering route choice and charging time equilibrium (2020)
  6. Dan, Teodora; Lodi, Andrea; Marcotte, Patrice: Joint location and pricing within a user-optimized environment (2020)
  7. Dutra, Dimas Abreu Archanjo: Uncertainty estimation in equality-constrained MAP and maximum likelihood estimation with applications to system identification and state estimation (2020)
  8. Fischetti, Matteo; Monaci, Michele: A branch-and-cut algorithm for mixed-integer bilinear programming (2020)
  9. Gill, Philip E.; Kungurtsev, Vyacheslav; Robinson, Daniel P.: A shifted primal-dual penalty-barrier method for nonlinear optimization (2020)
  10. Gleixner, Ambros; Maher, Stephen J.; Müller, Benjamin; Pedroso, João Pedro: Price-and-verify: a new algorithm for recursive circle packing using Dantzig-Wolfe decomposition (2020)
  11. Gutekunst, Jürgen; Bock, Hans Georg; Potschka, Andreas: Economic NMPC for averaged infinite horizon problems with periodic approximations (2020)
  12. Hohmann, Marc; Warrington, Joseph; Lygeros, John: A moment and sum-of-squares extension of dual dynamic programming with application to nonlinear energy storage problems (2020)
  13. Jagtenberg, C. J.; Mason, A. J.: Improving fairness in ambulance planning by time sharing (2020)
  14. Jiang, Canghua; Guo, Zhiqiang; Li, Xin; Wang, Hai; Yu, Ming: An efficient adjoint computational method based on lifted IRK integrator and exact penalty function for optimal control problems involving continuous inequality constraints (2020)
  15. Kirches, C.; Lenders, F.; Manns, P.: Approximation properties and tight bounds for constrained mixed-integer optimal control (2020)
  16. Kronqvist, Jan; Bernal, David E.; Grossmann, Ignacio E.: Using regularization and second order information in outer approximation for convex MINLP (2020)
  17. Liu, Xin-Wei; Dai, Yu-Hong: A globally convergent primal-dual interior-point relaxation method for nonlinear programs (2020)
  18. Mazari, Idriss; Nadin, Grégoire; Privat, Yannick: Optimal location of resources maximizing the total population size in logistic models (2020)
  19. Mehlitz, Patrick: A comparison of solution approaches for the numerical treatment of or-constrained optimization problems (2020)
  20. Melo, Wendel; Fampa, Marcia; Raupp, Fernanda: An overview of MINLP algorithms and their implementation in Muriqui optimizer (2020)

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