A globally convergent primal-dual interior-point filter method for nonlinear programming The paper proposes an algorithm which uses the filter technique of Fletcher and Leyffer to globalize the primal-dual interior-point method for nonlinear optimization, avoiding the use of merit functions and the updating of penalty parameters. This algorithm decomposes the primal-dual step obtained from the perturbed first-order necessary conditions into a normal and a tangential step, whose sizes are controlled by a trust-region type parameter. Each entry in the filter is a pair of coordinates: one resulting from feasibility and centrality, and associated with the normal step, the other resulting from optimality and related with the tangential step.

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  1. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  2. Curtis, Frank E.; Raghunathan, Arvind U.: Solving nearly-separable quadratic optimization problems as nonsmooth equations (2017)
  3. Esteban-Bravo, Mercedes; Leszkiewicz, Agata; Vidal-Sanz, Jose M.: Exact optimal experimental designs with constraints (2017)
  4. Pang, Lili; Zhu, Detong: A line search filter-SQP method with Lagrangian function for nonlinear inequality constrained optimization (2017)
  5. Kimiaei, Morteza; Esmaeili, Hamid: A trust-region approach with novel filter adaptive radius for system of nonlinear equations (2016)
  6. Shen, Chungen; Zhang, Lei-Hong; Yang, Wei Hong: A filter active-set algorithm for ball/sphere constrained optimization problem (2016)
  7. Gould, Nicholas I.M.; Loh, Yueling; Robinson, Daniel P.: A nonmonotone filter SQP method: local convergence and numerical results (2015)
  8. Huang, Mingxia; Pu, Dingguo: Line search SQP method with a flexible step acceptance procedure (2015)
  9. Huang, Mingxia; Pu, Dingguo: A line search SQP method without a penalty or a filter (2015)
  10. Liu, Weiai; Kong, Feng; Pu, Dingguo: An infeasible QP-free method without a penalty function for nonlinear inequality constrained optimization (2015)
  11. Schmidt, Martin: An interior-point method for nonlinear optimization problems with locatable and separable nonsmoothness (2015)
  12. Pei, Yonggang; Zhu, Detong: A trust-region algorithm combining line search filter method with Lagrange merit function for nonlinear constrained optimization (2014)
  13. Rocha, Ana Maria A.C.; Costa, M.Fernanda P.; Fernandes, Edite M.G.P.: A filter-based artificial fish swarm algorithm for constrained global optimization: theoretical and practical issues (2014)
  14. Agarwal, Anshul; Biegler, Lorenz T.: A trust-region framework for constrained optimization using reduced order modeling (2013)
  15. Gu, Chao: A dwindling filter inexact projected Hessian algorithm for large scale nonlinear constrained optimization (2013)
  16. Jin, Zhong: An improved nonmonotone filter trust region method for equality constrained optimization (2013)
  17. Periçaro, Gislaine A.; Ribeiro, Ademir A.; Karas, Elizabeth W.: Global convergence of a general filter algorithm based on an efficiency condition of the step (2013)
  18. Su, Ke; Liu, Wei; Lu, Xiaoli: Global convergence of a new nonmonotone filter method for equality constrained optimization (2013)
  19. Cai, Li; Zhu, Detong: A line search filter inexact SQP method for nonlinear equality constrained optimization (2012)
  20. Fatemi, M.; Mahdavi-Amiri, N.: A filter trust-region algorithm for unconstrained optimization with strong global convergence properties (2012)

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