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 338 articles )

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  1. Aftalion, Amandine: How to run 100 meters (2017)
  2. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  3. Baharev, Ali; Domes, Ferenc; Neumaier, Arnold: A robust approach for finding all well-separated solutions of sparse systems of nonlinear equations (2017)
  4. Belotti, Pietro; Berthold, Timo: Three ideas for a feasibility pump for nonconvex MINLP (2017)
  5. Brander, David; Gravesen, Jens; Nørbjerg, Toke Bjerge: Approximation by planar elastic curves (2017)
  6. Burachik, R.S.; Kaya, C.Y.; Rizvi, M.M.: A new scalarization technique and new algorithms to generate Pareto fronts (2017)
  7. Curtis, Frank E.; Gould, Nicholas I.M.; Robinson, Daniel P.; Toint, Philippe L.: An interior-point trust-funnel algorithm for nonlinear optimization (2017)
  8. Curtis, Frank E.; Raghunathan, Arvind U.: Solving nearly-separable quadratic optimization problems as nonsmooth equations (2017)
  9. D’Ambrosio, Claudia; Vu, Ky; Lavor, Carlile; Liberti, Leo; Maculan, Nelson: New error measures and methods for realizing protein graphs from distance data (2017)
  10. Danaila, Ionut; Kaplanski, Felix; Sazhin, Sergei S.: A model for confined vortex rings with elliptical-core vorticity distribution (2017)
  11. Domes, Ferenc; Goldsztejn, Alexandre: A branch and bound algorithm for quantified quadratic programming (2017)
  12. do Rosário de Pinho, Maria; Nunes Nogueira, Filipa: On application of optimal control to SEIR normalized models: pros and cons (2017)
  13. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  14. Dutra, Dimas Abreu Archanjo; Teixeira, Bruno Otávio Soares; Aguirre, Luis Antonio: Joint maximum \ita posteriori state path and parameter estimation in stochastic differential equations (2017)
  15. Fernández, F.J.; Alvarez-Vázquez, L.J.; Martínez, A.; Vázquez-Méndez, M.E.: A 3D optimal control problem related to the urban heat islands (2017)
  16. Gleixner, Ambros M.; Berthold, Timo; Müller, Benjamin; Weltge, Stefan: Three enhancements for optimization-based bound tightening (2017)
  17. Göttlich, Simone; Potschka, Andreas; Ziegler, Ute: Partial outer convexification for traffic light optimization in road networks (2017)
  18. Hart, William E.; Laird, Carl D.; Watson, Jean-Paul; Woodruff, David L.; Hackebeil, Gabriel A.; Nicholson, Bethany L.; Siirola, John D.: Pyomo -- optimization modeling in Python (2017)
  19. Huang, Kuo-Ling; Mehrotra, Sanjay: Solution of monotone complementarity and general convex programming problems using a modified potential reduction interior point method (2017)
  20. Klamka, Jerzy; Maurer, Helmut; Swierniak, Andrzej: Local controllability and optimal control for a model of combined anticancer therapy with control delays (2017)

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