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 547 articles )

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  1. Fischetti, Matteo; Monaci, Michele: A branch-and-cut algorithm for mixed-integer bilinear programming (2020)
  2. 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)
  3. 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)
  4. Jagtenberg, C. J.; Mason, A. J.: Improving fairness in ambulance planning by time sharing (2020)
  5. Mazari, Idriss; Nadin, Grégoire; Privat, Yannick: Optimal location of resources maximizing the total population size in logistic models (2020)
  6. Michel, Volker; Schneider, Naomi: A first approach to learning a best basis for gravitational field modelling (2020)
  7. Steinboeck, Andreas; Guay, Martin; Kugi, Andreas: A design technique for fast sampled-data nonlinear model predictive control with convergence and stability results (2020)
  8. Yazdaniyan, Z.; Shamsi, M.; Foroozandeh, Z.; de Pinho, Maria do Rosário: A numerical method based on the complementarity and optimal control formulations for solving a family of zero-sum pursuit-evasion differential games (2020)
  9. Zanelli, A.; Domahidi, A.; Jerez, J.; Morari, M.: FORCES NLP: an efficient implementation of interior-point methods for multistage nonlinear nonconvex programs (2020)
  10. Almeida, Luis; Privat, Yannick; Strugarek, Martin; Vauchelet, Nicolas: Optimal releases for population replacement strategies: application to Wolbachia (2019)
  11. Altherr, Lena C.; Leise, Philipp; Pfetsch, Marc E.; Schmitt, Andreas: Resilient layout, design and operation of energy-efficient water distribution networks for high-rise buildings using MINLP (2019)
  12. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  13. Armand, Paul; Tran, Ngoc Nguyen: Rapid infeasibility detection in a mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2019)
  14. Baharev, Ali; Neumaier, Arnold; Schichl, Hermann: A manifold-based approach to sparse global constraint satisfaction problems (2019)
  15. Benham, Graham P.; Boucher, J. P.; Labbé, R.; Benzaquen, M.; Clanet, C.: Wave drag on asymmetric bodies (2019)
  16. Benita, Francisco; Mehlitz, Patrick: Solving optimal control problems with terminal complementarity constraints via Scholtes’ relaxation scheme (2019)
  17. D’Ambrosio, Claudia; Lee, Jon; Liberti, Leo; Ovsjanikov, Maks: Extrapolating curvature lines in rough concept sketches using mixed-integer nonlinear optimization (2019)
  18. Dokken, Jørgen S.; Funke, Simon W.; Johansson, August; Schmidt, Stephan: Shape optimization using the finite element method on multiple meshes with Nitsche coupling (2019)
  19. Dong, Hongbo: On integer and MPCC representability of affine sparsity (2019)
  20. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv

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