Abstract
During the last two decades, a significant growth has taken place in algorithmic and software development of local and global optimization methods for a variety of classes of nonlinear, discrete, and dynamic mathematical problems. These problems include (i) multi-quadratic programming, (ii) bilinear and biconvex, (iii) generalized geometric programming, (iv) general constrained nonlinear optimization, (v) bilevel optimization, (vi) complementarity, (vii) semidefinite programming, (viii) mixed-integer nonlinear optimization, (ix) combinatorial optimization, and (x) optimal control problems. Relative to these advances there have been very limited efforts in establishing a systematic benchmark framework for the evaluation of the algoritms and their implementations (Hock and Schittkowski (1981); Floudas and Pardalos (1990); Bongartz et al. (1995)). A well-designed experimental computational testing framework is of primary importance in identifying the merits of each algorithm and implementation.
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© 1999 Springer Science+Business Media Dordrecht
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Floudas, C.A. et al. (1999). Introduction. In: Handbook of Test Problems in Local and Global Optimization. Nonconvex Optimization and Its Applications, vol 33. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3040-1_1
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DOI: https://doi.org/10.1007/978-1-4757-3040-1_1
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-4812-0
Online ISBN: 978-1-4757-3040-1
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