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.

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  1. Ding Ma, Dominique Orban, Michael A. Saunders: A Julia implementation of Algorithm NCL for constrained optimization (2021) arXiv
  2. 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)
  3. Alimo, Ryan; Beyhaghi, Pooriya; Bewley, Thomas R.: Delaunay-based derivative-free optimization via global surrogates. III: nonconvex constraints (2020)
  4. Betts, John T.; Campbell, Stephen L.; Digirolamo, Claire: Initial guess sensitivity in computational optimal control problems (2020)
  5. Biccari, Umberto; Warma, Mahamadi; Zuazua, Enrique: Controllability of the one-dimensional fractional heat equation under positivity constraints (2020)
  6. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Sparsity in optimal randomized classification trees (2020)
  7. Bongartz, Dominik; Najman, Jaromił; Mitsos, Alexander: Deterministic global optimization of steam cycles using the IAPWS-IF97 model (2020)
  8. Burke, James V.; Curtis, Frank E.; Wang, Hao; Wang, Jiashan: Inexact sequential quadratic optimization with penalty parameter updates within the QP solver (2020)
  9. Carpentier, Pierre; Chancelier, Jean-Philippe; De Lara, Michel; Pacaud, François: Mixed spatial and temporal decompositions for large-scale multistage stochastic optimization problems (2020)
  10. Charrondière, Raphaël; Bertails-Descoubes, Florence; Neukirch, Sébastien; Romero, Victor: Numerical modeling of inextensible elastic ribbons with curvature-based elements (2020)
  11. Chen-Charpentier, Benito M.; Jackson, Mark: Direct and indirect optimal control applied to plant virus propagation with seasonality and delays (2020)
  12. 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)
  13. Dai, Yu-Hong; Liu, Xin-Wei; Sun, Jie: A primal-dual interior-point method capable of rapidly detecting infeasibility for nonlinear programs (2020)
  14. Dan, Teodora; Lodi, Andrea; Marcotte, Patrice: Joint location and pricing within a user-optimized environment (2020)
  15. Després, Bruno; Trélat, Emmanuel: Two-sided space-time (L^1) polynomial approximation of hypographs within polynomial optimal control (2020)
  16. Drezner, Tammy; Drezner, Zvi; Kalczynski, Pawel: Gradual cover competitive facility location (2020)
  17. Dutra, Dimas Abreu Archanjo: Uncertainty estimation in equality-constrained MAP and maximum likelihood estimation with applications to system identification and state estimation (2020)
  18. Eltved, Anders; Dahl, Joachim; Andersen, Martin S.: On the robustness and scalability of semidefinite relaxation for optimal power flow problems (2020)
  19. Finardi, E. C.; Lobato, R. D.; de Matos, V. L.; Sagastizábal, C.; Tomasgard, A.: Stochastic hydro-thermal unit commitment via multi-level scenario trees and bundle regularization (2020)
  20. Fischetti, Matteo; Monaci, Michele: A branch-and-cut algorithm for mixed-integer bilinear programming (2020)

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