CFSQP

CFSQP Version 2.5: A C Code for Solving (Large Scale) Constrained Nonlinear (Minimax) Optimization Problems, Generating Iterates Satisfying All Inequality Constraints. CFSQP is a set of C functions for the minimization of the maximum of a set of smooth objective functions (possibly a single one) subject to general smooth constraints. If the initial guess provided by the user is infeasible for some inequality constraint or some linear equality constraint, CFSQP first generates a feasible point for these constraints; subsequently the successive iterates generated by CFSQP all satisfy these constraints. Nonlinear equality constraints are turned into inequality constraints (to be satisfied by all iterates) and the maximum of the objective functions is replaced by an exact penalty function which penalizes nonlinear equality constraint violations only. When solving problems with many sequentially related constraints (or objectives), such as discretized semi- infinite programming (SIP) problems, CFSQP gives the user the option to use an algorithm that efficiently solves these problems, greatly reducing computational effort. The user has the option of either requiring that the objective function (penalty function if nonlinear equality constraints are present) decrease at each iteration after feasibility for nonlinear inequality and linear constraints has been reached (monotone line search), or requiring a decrease within at most four iterations (nonmonotone line search). He/She must provide functions that define the objective functions and constraint functions and may either provide functions to compute the respective gradients or require that CFSQP estimate them by forward finite differences. CFSQP is an implementation of two algorithms based on Sequential Quadratic Programming (SQP), modified so as to generate feasible iterates. In the first one (monotone line search), a certain Armijo type arc search is used with the property that the step of one is eventually accepted, a requirement for superlinear convergence. In the second one the same effect is achieved by means of a ”nonmonotone” search along a straight line. The merit function used in both searches is the maximum of the objective functions if there is no nonlinear equality constraints, or an exact penalty function if nonlinear equality constraints are present


References in zbMATH (referenced in 61 articles )

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  1. Pedamallu, Chandra Sekhar; Ozdamar, Linet; Ceberio, Martine: Efficient interval partitioning-local search collaboration for constraint satisfaction (2008)
  2. Pedamallu, Chandra Sekhar; Özdamar, Linet; Csendes, Tibor; Vinkó, Tamás: Efficient interval partitioning for constrained global optimization (2008)
  3. Siarry, Patrick (ed.); Michalewicz, Zbigniew (ed.): Advances in metaheuristics for hard optimization (2008)
  4. Waligóra, Grzegorz: Discrete-continuous project scheduling with discounted cash flows - a tabu search approach (2008)
  5. Johnson, Richard A.; Lu, Wenqing: Proof load designs for estimation of dependence in a bivariate Weibull model (2007)
  6. Östermark, Ralf: A flexible platform for mixed-integer non-linear programming problems (2007)
  7. Pedamallu, Chandra Sekhar; Özdamar, Linet; Csendes, Tibor: An interval partitioning approach for continuous constrained optimization (2007)
  8. Antonini, Gianluca; Martinez, Santiago Venegas; Bierlaire, Michel; Thiran, Jean Philippe: Behavioral priors for detection and tracking of pedestrians in video sequences (2006) ioport
  9. Bierlaire, Michel: A theoretical analysis of the cross-nested logit model (2006)
  10. Doltsinis, Ioannis; Kang, Zhan: Perturbation-based stochastic FE analysis and robust design of inelastic deformation processes (2006)
  11. Kearsley, Anthony J.: Algorithms for optimal signal set design (2006)
  12. Camponogara, Eduardo; Talukdar, Sarosh N.: Designing communication networks to decompose network control problems (2005)
  13. Chen, Danny Z.; Daescu, Ovidiu; Dai, Yang; Katoh, Naoki; Wu, Xiaodong; Xu, Jinhui: Efficient algorithms and implementations for optimizing the sum of linear fractional functions, with applications (2005)
  14. Doltsinis, Ioannis; Kang, Zhan; Cheng, Gengdong: Robust design of nonlinear structures using optimization methods (2005)
  15. Hao, Yongxing; Agrawal, Sunil Kumar: Formation planning and control of ugvs with trailers. (2005) ioport
  16. Ibrahimbegović, Adnan; Grešovnik, Igor; Markovič, Damijan; Melnyk, Sergiy; Rodič, Tomaž: Shape optimization of two-phase inelastic material with microstructure (2005)
  17. Jedynak, Bruno M.; Khudanpur, Sanjeev: Maximum likelihood set for estimating a probability mass function (2005)
  18. Bron, F.; Besson, J.: A yield function for anisotropic materials application to aluminum alloys. (2004)
  19. Doltsinis, Ioannis; Kang, Zhan: Robust design of structures using optimization methods (2004)
  20. Östermark, Ralf: A multipurpose parallel genetic hybrid algorithm for nonlinear nonconvex programming problems (2004)