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 787 articles )

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  1. Abgrall, Rémi; Öffner, Philipp; Ranocha, Hendrik: Reinterpretation and extension of entropy correction terms for residual distribution and discontinuous Galerkin schemes: application to structure preserving discretization (2022)
  2. Andreani, R.; Haeser, G.; Schuverdt, M. L.; Secchin, L. D.; Silva, P. J. S.: On scaled stopping criteria for a safeguarded augmented Lagrangian method with theoretical guarantees (2022)
  3. Arrigo, Adriano; Ordoudis, Christos; Kazempour, Jalal; De Grève, Zacharie; Toubeau, Jean-François; Vallée, François: Wasserstein distributionally robust chance-constrained optimization for energy and reserve dispatch: an exact and physically-bounded formulation (2022)
  4. Baayen, Jorn H.; Postek, Krzysztof: Hidden invariant convexity for global and conic-intersection optimality guarantees in discrete-time optimal control (2022)
  5. Bansal, Saurabh; Nagarajan, Mahesh: Technical note -- A Monge sequence-based approach to characterize the competitive newsvendor problem (2022)
  6. Belli, Edoardo: Smoothly adaptively centered ridge estimator (2022)
  7. Brust, Johannes J.; Marcia, Roummel F.; Petra, Cosmin G.; Saunders, Michael A.: Large-scale optimization with linear equality constraints using reduced compact representation (2022)
  8. Calafiore, Giuseppe C.; Novara, Carlo; Possieri, Corrado: Control analysis and design via randomised coordinate polynomial minimisation (2022)
  9. Chen, Qi; Johnson, Emma S.; Bernal, David E.; Valentin, Romeo; Kale, Sunjeev; Bates, Johnny; Siirola, John D.; Grossmann, Ignacio E.: Pyomo.GDP: an ecosystem for logic based modeling and optimization development (2022)
  10. Deng, Haoyang; Ohtsuka, Toshiyuki: ParNMPC -- a parallel optimisation toolkit for real-time nonlinear model predictive control (2022)
  11. Du, Hongwang; Jiang, Qinwen; Xiong, Wei: Unified static equilibrium modeling and analysis of elastic rods with large deformations for complex constraints (2022)
  12. Eftekhari, Aryan; Scheidegger, Simon: High-dimensional dynamic stochastic model representation (2022)
  13. Fairbrother, Jamie; Turner, Amanda; Wallace, Stein W.: Problem-driven scenario generation: an analytical approach for stochastic programs with tail risk measure (2022)
  14. Gade, Jan-Lucas; Thore, Carl-Johan; Stålhand, Jonas: Identification of mechanical properties of arteries with certification of global optimality (2022)
  15. Gao, Pu; Kamiński, Bogumił; MacRury, Calum; Prałat, Paweł: Hamilton cycles in the semi-random graph process (2022)
  16. Gorissen, Bram L.: Interior point methods can exploit structure of convex piecewise linear functions with application in radiation therapy (2022)
  17. Hou, Liangshao; Qian, Xun; Liao, Li-Zhi; Sun, Jie: An interior point parameterized central path following algorithm for linearly constrained convex programming (2022)
  18. Hurst, Todd; Rehbock, Volker: Optimizing micro-algae production in a raceway pond with variable depth (2022)
  19. Júdice, Joaquim; Sessa, Valentina; Fukushima, Masao: Solution of fractional quadratic programs on the simplex and application to the eigenvalue complementarity problem (2022)
  20. Kaya, C. Yalçın: Observer path planning for maximum information (2022)

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