Keywords
constrained optimization
mathematical programming
nonlinear programming
optimization
Optimización no lineal
optimización con restricciones
programación matemática
programación no lineal
optimización
How to Cite
Abstract
Our goal is to build a software able to solve problems in non linear optimization. A central point is selection of the algorithm, which is based on Augmented Lagrangian Method combined with quasi-Newton methods (BFGS, L-BFGS). The article expains how software is structured, the steps of its construction and the mode of use; furthermore, results ansd numerical tests are analyzed. OPTIMIZA 3.0 was built on Borland Delphi 3.0 and runs on Windows, its capacity on the number of variables and constraints are ontly limited ont he memory machine to be used.
References
Luenberger, G. (1984) Programación Lineal y No Lineal. Addison-Wesley Iberoamericana, México.
Nocedal, J. (1992) “Theory of algorithms for unconstrained optimization”, Acta Numerica 1: 199–222.
Nocedal, J.; Liu Dong, C. (1989) “On the limited memory BFGS method for large-scale optimization”, Mathematical Programming 45: 503–528.
Hock, W.; Schittkokski, K. (1981) Test Examples for Nonlinear Programming Codes. Lecture Notes in Economics and Mathematical Systems187, Springer, Berlin.
Buckley; Lenir, A. (1983) “QN-like variable storage conjugate gradients”, Mathematical Programming 27: 103–119.
Nocedal, J. (1980) “Updating quasi-Newton matrices with limited storage”, Mathematical Comput. 35: 773–782.