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 )

Showing results 41 to 60 of 61.
Sorted by year (citations)
  1. Lynch, Kevin M.: Optimal control of the thrusted skate (2003)
  2. Stanciulescu, C.; Fortemps, Ph.; Installé, M.; Wertz, V.: Multiobjective fuzzy linear programming problems with fuzzy decision variables. (2003)
  3. Józefowska, Joanna; Mika, Marek; Różycki, Rafał; Waligóra, Grzegorz; Węglarz, Jan: A heuristic approach to allocating the continuous resource in discrete--continuous scheduling problems to minimize the makespan (2002)
  4. Józefowska, Joanna; Waligóra, Grzegorz; Węglarz, Jan: Tabu list management methods for a discrete-continuous scheduling problem (2002)
  5. McCloud, Michael L.; Varanasi, Mahesh K.: Modulation and coding for noncoherent communications (2002)
  6. Howse, James W.; Ticknor, Lawrence O.; Muske, Kenneth R.: Least squares estimation techniques for position tracking of radioactive sources (2001)
  7. Lawrence, Craig T.; Tits, André L.: A computationally efficient feasible sequential quadratic programming algorithm (2001)
  8. Contreras, Javier; Losi, Arturo; Russo, Mario; Wu, Felix F.: DistOpt: A software framework for modeling and evaluating optimization problem solutions in distributed environments (2000)
  9. Makowski, Marek: Modeling paradigms applied to the analysis of European air quality (2000)
  10. Stacey, Chris H. E.; Eyers, Tony; Anido, Gary J.: A concave link elimination (CLE) procedure and lower bound for concave topology, capacity and flow assignment network design problems (2000)
  11. Tolani, Deepak; Goswami, Ambarish; Badler, Norman I.: Real-time inverse kinematics techniques for anthropomorphic limbs (2000)
  12. Pytlak, Radosław: Numerical methods for optimal control problems with state constraints (1999)
  13. Pytlak, R.; Vinter, R. B.: Feasible direction algorithm for optimal control problems with state and control constraints: Implementation (1999)
  14. Aubert, P.; Rousselet, B.: Sensitivity computation and shape optimization for a nonlinear arch model with limit-points instabilities (1998)
  15. Gelsey, Andrew; Schwabacher, Mark; Smith, Don: Using modeling knowledge to guide design space search (1998)
  16. Józefowska, Joanna; Mika, Marek; Różycki, Rafal; Waligóra, Grzegorz; Węglarz, Jan: Local search metaheuristics for discrete-continuous scheduling problems (1998)
  17. Lawrence, Craig T.; Tits, André L.: Feasible sequential quadratic programming for finely discretized problems from SIP (1998)
  18. Huang, Jian; Rossini, A. J.: Sieve estimation for the proportional-odds failure-time regression model with interval censoring (1997)
  19. Wang, S.; Teo, K. L.; Lee, H. W. J.; Caccetta, L.: Solving 0-1 programming problems by a penalty approach. (1997)
  20. Aubert, Pierre: Thickness optimization of a geometrically nonlinear arch at a limit point (1996)