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 488 articles )

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  1. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv
  2. Gugat, Martin; Hante, Falk M.: On the turnpike phenomenon for optimal boundary control problems with hyperbolic systems (2019)
  3. Ledzewicz, Urszula; Maurer, Helmut; Schättler, Heinz: Optimal combined radio- and anti-angiogenic cancer therapy (2019)
  4. Liuzzi, G.; Locatelli, M.; Piccialli, Veronica: A new branch-and-bound algorithm for standard quadratic programming problems (2019)
  5. Privat, Yannick; Trélat, Emmanuel; Zuazua, Enrique: Spectral shape optimization for the Neumann traces of the Dirichlet-Laplacian eigenfunctions (2019)
  6. Singham, D. I.: Sample average approximation for the continuous type principal-agent problem (2019)
  7. Wang, Tong; Lima, Ricardo M.; Giraldi, Loïc; Knio, Omar M.: Trajectory planning for autonomous underwater vehicles in the presence of obstacles and a nonlinear flow field using mixed integer nonlinear programming (2019)
  8. Youett, Jonathan; Sander, Oliver; Kornhuber, Ralf: A globally convergent filter-trust-region method for large deformation contact problems (2019)
  9. Yu, Qinxiao; Fang, Kan; Zhu, Ning; Ma, Shoufeng: A matheuristic approach to the orienteering problem with service time dependent profits (2019)
  10. Amaioua, Nadir; Audet, Charles; Conn, Andrew R.; Le Digabel, Sébastien: Efficient solution of quadratically constrained quadratic subproblems within the mesh adaptive direct search algorithm (2018)
  11. Arreckx, Sylvain; Orban, Dominique: A regularized factorization-free method for equality-constrained optimization (2018)
  12. Berthold, Timo: A computational study of primal heuristics inside an MI(NL)P solver (2018)
  13. Bertsimas, Dimitris; Gupta, Vishal; Kallus, Nathan: Robust sample average approximation (2018)
  14. Cabrera G., Guillermo; Ehrgott, Matthias; Mason, Andrew J.; Raith, Andrea: A matheuristic approach to solve the multiobjective beam angle optimization problem in intensity-modulated radiation therapy (2018)
  15. Cafieri, Sonia; Cellier, Loïc; Messine, Frédéric; Omheni, Riadh: Combination of optimal control approaches for aircraft conflict avoidance via velocity regulation (2018)
  16. Cafieri, Sonia; D’Ambrosio, Claudia: Feasibility pump for aircraft deconfliction with speed regulation (2018)
  17. Cai, W.; Singham, D. I.: A principal-agent problem with heterogeneous demand distributions for a carbon capture and storage system (2018)
  18. Carrizosa, Emilio; Guerrero, Vanesa; Romero Morales, Dolores: On mathematical optimization for the visualization of frequencies and adjacencies as rectangular maps (2018)
  19. Charkhgard, Hadi; Savelsbergh, Martin; Talebian, Masoud: A linear programming based algorithm to solve a class of optimization problems with a multi-linear objective function and affine constraints (2018)
  20. Conti, Sergio; Rumpf, Martin; Schultz, Rüdiger; Tölkes, Sascha: Stochastic dominance constraints in elastic shape optimization (2018)

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