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

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  1. Waligóra, Grzegorz: Comparative analysis of some metaheuristics for discrete-continuous project scheduling with activities of identical processing rates (2016)
  2. Różycki, R.; Wȩglarz, J.: Power-aware scheduling of preemptable jobs on identical parallel processors to minimize makespan (2014)
  3. Liu, Feng-Tao; Fan, You-Hua; Yin, Jian-Hua: The use of QP-free algorithm in the limit analysis of slope stability (2011)
  4. Pedamallu, Chandra Sekhar; Ozdamar, Linet: Solving kinematics problems by efficient interval partitioning (2011)
  5. Pee, E.Y.; Royset, J.O.: On solving large-scale finite minimax problems using exponential smoothing (2011)
  6. Waligóra, Grzegorz: Heuristic approaches to discrete-continuous project scheduling problems to minimize the makespan (2011)
  7. Chung, H.; Polak, E.; Sastry, S.: On the use of outer approximations as an external active set strategy (2010)
  8. Kang, Zhan; Luo, Yangjun: Reliability-based structural optimization with probability and convex set hybrid models (2010) ioport
  9. Luo, Yangjun; Kang, Zhan; Luo, Zhen; Li, Alex: Continuum topology optimization with non-probabilistic reliability constraints based on multi-ellipsoid convex model (2010)
  10. Kang, Zhan; Luo, Yangjun: Non-probabilistic reliability-based topology optimization of geometrically nonlinear structures using convex models (2009)
  11. Östermark, Ralf: Concurrent processing of mixed-integer non-linear programming problems (2009)
  12. Rumigny, N.; Papadopoulos, P.; Polak, E.: Optimal discretization strategy for boundary element-based shape optimization problem (2009)
  13. Waligóra, Grzegorz: Tabu search for discrete-continuous scheduling problems with heuristic continuous resource allocation (2009)
  14. Hauser, Kris; Bretl, Timothy; Harada, Kensuke; Latombe, Jean-Claude: Using motion primitives in probabilistic sample-based planning for humanoid robots (2008)
  15. Östermark, Ralf: Scalability of the genetic hybrid algorithm on a parallel supercomputer (2008)
  16. Pedamallu, Chandra Sekhar; Ozdamar, Linet: Investigating a hybrid simulated annealing and local search algorithm for constrained optimization (2008)
  17. Pedamallu, Chandra Sekhar; Özdamar, Linet: Comparison of simulated annealing, interval partitioning and hybrid algorithms in constrained global optimization (2008)
  18. Pedamallu, Chandra Sekhar; Ozdamar, Linet; Ceberio, Martine: Efficient interval partitioning-local search collaboration for constraint satisfaction (2008)
  19. Pedamallu, Chandra Sekhar; Özdamar, Linet; Csendes, Tibor; Vinkó, Tamás: Efficient interval partitioning for constrained global optimization (2008)
  20. Siarry, Patrick (ed.); Michalewicz, Zbigniew (ed.): Advances in metaheuristics for hard optimization (2008)

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