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 291 articles )

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  1. do Rosário de Pinho, Maria; Nunes Nogueira, Filipa: On application of optimal control to SEIR normalized models: pros and cons (2017)
  2. Klamka, Jerzy; Maurer, Helmut; Swierniak, Andrzej: Local controllability and optimal control for a model of combined anticancer therapy with control delays (2017)
  3. Silva, Cristiana J.; Maurer, Helmut; Torres, Delfim F.M.: Optimal control of a tuberculosis model with state and control delays (2017)
  4. Allahverdi, Navid; Pozo, Alejandro; Zuazua, Enrique: Numerical aspects of large-time optimal control of Burgers equation (2016)
  5. Alt, Walter; Kaya, C.Yalçın; Schneider, Christopher: Dualization and discretization of linear-quadratic control problems with bang-bang solutions (2016)
  6. Belhachmi, Zakaria; Hecht, Frederic: An adaptive approach for the segmentation and the TV-filtering in the optic flow estimation (2016)
  7. Belyaev, Alexander: Generation of interior points and polyhedral representations of cones in $\mathbb R^N$ cut by $M$ planes sharing a common point (2016)
  8. Betts, John T.; Campbell, Stephen L.; Thompson, Karmethia C.: Solving optimal control problems with control delays using direct transcription (2016)
  9. Cannataro, Begüm Şenses; Rao, Anil V.; Davis, Timothy A.: State-defect constraint pairing graph coarsening method for Karush-Kuhn-Tucker matrices arising in orthogonal collocation methods for optimal control (2016)
  10. Chiang, Nai-Yuan; Zavala, Victor M.: An inertia-free filter line-search algorithm for large-scale nonlinear programming (2016)
  11. Dearing, P.M.; Belotti, Pietro; Smith, Andrea M.: A primal algorithm for the weighted minimum covering ball problem in $\mathbb R^n$ (2016)
  12. Domes, Ferenc; Neumaier, Arnold: Linear and parabolic relaxations for quadratic constraints (2016)
  13. Dong, Hongbo: Relaxing nonconvex quadratic functions by multiple adaptive diagonal perturbations (2016)
  14. Fliege, Jörg; Vaz, A.Ismael F.: A method for constrained multiobjective optimization based on SQP techniques (2016)
  15. Gassmann, Horand; Ma, Jun; Martin, Kipp: Communication protocols for options and results in a distributed optimization environment (2016)
  16. Grimstad, Bjarne; Sandnes, Anders: Global optimization with spline constraints: a new branch-and-bound method based on B-splines (2016)
  17. Hager, William W.; Zhang, Hongchao: Projection onto a polyhedron that exploits sparsity (2016)
  18. Harbrecht, Helmut; Loos, Florian: Optimization of current carrying multicables (2016)
  19. Iusem, Alfredo N.; Júdice, Joaquim J.; Sessa, Valentina; Sherali, Hanif D.: On the numerical solution of the quadratic eigenvalue complementarity problem (2016)
  20. Kirst, Peter; Stein, Oliver: Solving disjunctive optimization problems by generalized semi-infinite optimization techniques (2016)

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