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. de Pinho, Maria do Rosário; Maurer, Helmut; Zidani, Hasnaa: Optimal control of normalized SIMR models with vaccination and treatment (2018)
  2. Rodrigues, Filipe; Silva, Cristiana J.; Torres, Delfim F.M.; Maurer, Helmut: Optimal control of a delayed HIV model (2018)
  3. Thäter, Markus; Chudej, Kurt; Pesch, Hans Josef: Optimal vaccination strategies for an SEIR model of infectious diseases with logistic growth (2018)
  4. Aftalion, Amandine: How to run 100 meters (2017)
  5. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  6. Baharev, Ali; Domes, Ferenc; Neumaier, Arnold: A robust approach for finding all well-separated solutions of sparse systems of nonlinear equations (2017)
  7. Bara, O.; Djouadi, S.M.; Day, J.D.; Lenhart, S.: Immune therapeutic strategies using optimal controls with $L^1$ and $L^2$ type objectives (2017)
  8. Belotti, Pietro; Berthold, Timo: Three ideas for a feasibility pump for nonconvex MINLP (2017)
  9. Bongartz, Dominik; Mitsos, Alexander: Deterministic global optimization of process flowsheets in a reduced space using McCormick relaxations (2017)
  10. Brander, David; Gravesen, Jens; Nørbjerg, Toke Bjerge: Approximation by planar elastic curves (2017)
  11. Burachik, R.S.; Kaya, C.Y.; Rizvi, M.M.: A new scalarization technique and new algorithms to generate Pareto fronts (2017)
  12. Chen, Chen; Atamtürk, Alper; Oren, Shmuel S.: A spatial branch-and-cut method for nonconvex QCQP with bounded complex variables (2017)
  13. Chen, Yunmei; Lan, Guanghui; Ouyang, Yuyuan: Accelerated schemes for a class of variational inequalities (2017)
  14. Curtis, Frank E.; Gould, Nicholas I.M.; Robinson, Daniel P.; Toint, Philippe L.: An interior-point trust-funnel algorithm for nonlinear optimization (2017)
  15. Curtis, Frank E.; Raghunathan, Arvind U.: Solving nearly-separable quadratic optimization problems as nonsmooth equations (2017)
  16. D’Ambrosio, Claudia; Vu, Ky; Lavor, Carlile; Liberti, Leo; Maculan, Nelson: New error measures and methods for realizing protein graphs from distance data (2017)
  17. Danaila, Ionut; Kaplanski, Felix; Sazhin, Sergei S.: A model for confined vortex rings with elliptical-core vorticity distribution (2017)
  18. Domes, Ferenc; Goldsztejn, Alexandre: A branch and bound algorithm for quantified quadratic programming (2017)
  19. do Rosário de Pinho, Maria; Nunes Nogueira, Filipa: On application of optimal control to SEIR normalized models: pros and cons (2017)
  20. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)

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