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. Kuhlmann, Renke; Büskens, Christof: A primal-dual augmented Lagrangian penalty-interior-point filter line search algorithm (2018)
  2. Pei, Yong Gang; Zhu, De Tong: On the global convergence of a projective trust region algorithm for nonlinear equality constrained optimization (2018)
  3. Qiu, Songqiang; Chen, Zhongwen: An interior point method for nonlinear optimization with a quasi-tangential subproblem (2018)
  4. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  5. Curtis, Frank E.; Raghunathan, Arvind U.: Solving nearly-separable quadratic optimization problems as nonsmooth equations (2017)
  6. Esteban-Bravo, Mercedes; Leszkiewicz, Agata; Vidal-Sanz, Jose M.: Exact optimal experimental designs with constraints (2017)
  7. Pang, Lili; Zhu, Detong: A line search filter-SQP method with Lagrangian function for nonlinear inequality constrained optimization (2017)
  8. Kimiaei, Morteza; Esmaeili, Hamid: A trust-region approach with novel filter adaptive radius for system of nonlinear equations (2016)
  9. Shen, Chungen; Zhang, Lei-Hong; Yang, Wei Hong: A filter active-set algorithm for ball/sphere constrained optimization problem (2016)
  10. Gould, Nicholas I. M.; Loh, Yueling; Robinson, Daniel P.: A nonmonotone filter SQP method: local convergence and numerical results (2015)
  11. Huang, Mingxia; Pu, Dingguo: A line search SQP method without a penalty or a filter (2015)
  12. Huang, Mingxia; Pu, Dingguo: Line search SQP method with a flexible step acceptance procedure (2015)
  13. Liu, Weiai; Kong, Feng; Pu, Dingguo: An infeasible QP-free method without a penalty function for nonlinear inequality constrained optimization (2015)
  14. Schmidt, Martin: An interior-point method for nonlinear optimization problems with locatable and separable nonsmoothness (2015)
  15. Pei, Yonggang; Zhu, Detong: A trust-region algorithm combining line search filter method with Lagrange merit function for nonlinear constrained optimization (2014)
  16. 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)
  17. Agarwal, Anshul; Biegler, Lorenz T.: A trust-region framework for constrained optimization using reduced order modeling (2013)
  18. Ge, Hengwu; Chen, Zhongwen: A penalty-free method with line search for nonlinear equality constrained optimization (2013)
  19. Gu, Chao: A dwindling filter inexact projected Hessian algorithm for large scale nonlinear constrained optimization (2013)
  20. Jin, Zhong: An improved nonmonotone filter trust region method for equality constrained optimization (2013)

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