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 647 articles )

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  1. Al Sayed, Abdelkader; Bogosel, Beniamin; Henrot, Antoine; Nacry, Florent: Maximization of the Steklov eigenvalues with a diameter constraint (2021)
  2. Bollhöfer, Matthias; Schenk, Olaf; Verbosio, Fabio: A high performance level-block approximate LU factorization preconditioner algorithm (2021)
  3. Dandurand, Brian C.; Kim, Kibaek; Leyffer, Sven: A bilevel approach for identifying the worst contingencies for nonconvex alternating current power systems (2021)
  4. Ding Ma, Dominique Orban, Michael A. Saunders: A Julia implementation of Algorithm NCL for constrained optimization (2021) arXiv
  5. Erfani, Shervan; Babolian, Esmail; Javadi, Shahnam: New fractional pseudospectral methods with accurate convergence rates for fractional differential equations (2021)
  6. Haeser, Gabriel; Hinder, Oliver; Ye, Yinyu: On the behavior of Lagrange multipliers in convex and nonconvex infeasible interior point methods (2021)
  7. Harwood, Stuart M.: Analysis of the alternating direction method of multipliers for nonconvex problems (2021)
  8. Liu, Yanchao: A note on solving DiDi’s driver-order matching problem (2021)
  9. Lohéac, Jérôme; Trélat, Emmanuel; Zuazua, Enrique: Nonnegative control of finite-dimensional linear systems (2021)
  10. Manns, Paul; Kirches, Christian; Lenders, Felix: Approximation properties of sum-up rounding in the presence of vanishing constraints (2021)
  11. Mazari, Idriss; Ruiz-Balet, Domènec: A fragmentation phenomenon for a nonenergetic optimal control problem: optimization of the total population size in logistic diffusive models (2021)
  12. Agamawi, Yunus M.; Rao, Anil V.: CGPOPS: a C++ software for solving multiple-phase optimal control problems using adaptive Gaussian quadrature collocation and sparse nonlinear programming (2020)
  13. Alimo, Ryan; Beyhaghi, Pooriya; Bewley, Thomas R.: Delaunay-based derivative-free optimization via global surrogates. III: nonconvex constraints (2020)
  14. Betts, John T.; Campbell, Stephen L.; Digirolamo, Claire: Initial guess sensitivity in computational optimal control problems (2020)
  15. Biccari, Umberto; Warma, Mahamadi; Zuazua, Enrique: Controllability of the one-dimensional fractional heat equation under positivity constraints (2020)
  16. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Sparsity in optimal randomized classification trees (2020)
  17. Bongartz, Dominik; Najman, Jaromił; Mitsos, Alexander: Deterministic global optimization of steam cycles using the IAPWS-IF97 model (2020)
  18. Burke, James V.; Curtis, Frank E.; Wang, Hao; Wang, Jiashan: Inexact sequential quadratic optimization with penalty parameter updates within the QP solver (2020)
  19. Carpentier, Pierre; Chancelier, Jean-Philippe; De Lara, Michel; Pacaud, François: Mixed spatial and temporal decompositions for large-scale multistage stochastic optimization problems (2020)
  20. Casanellas, Glòria; Castro, Jordi: Using interior point solvers for optimizing progressive lens models with spherical coordinates (2020)

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