Multidimensional minimization is a common procedure needed in many fields. A variety of problems in engineering, physics, chemistry, etc, are frequently reduced to ones of minimizing a function of many variables. For instance we refer to systems of non-linear equations, to variational methods, to curve fitting and to the training of neural networks. Minimizing a multidimensional function faces a lot of difficulties. There is no single method that can tackle all problems in a satisfactory way. It has been realized that one needs a strategy, combining different methods, to efficiently handle a wide spectrum of problems. The presence of constraints, even of simple ones, enhances the difficulty. Many algorithms require evaluation of the gradient and this imposes additional problems since it is not always straightforward to code it and so one resorts to approximating the derivatives using differencing, that in turn costs in computing time as well as in accuracy. Merlin is an integrated environment designed to solve optimization problems. It is devised to be easy-to-use, and implemented so as to be portable among different platforms. Another feature is that Merlin is open, i.e. a plug-in mechanism is provided so that others can easily embed their own code modules.

References in zbMATH (referenced in 14 articles , 1 standard article )

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  1. Papachristou, Charalampos: A population based confidence set inference method for SNPs that regulate quantitative phenotypes (2015)
  2. Hoda I., S.A.: Comparative study of multigrid methods and neural network methods for boundary value problems with irregular boundaries (2010)
  3. Voglis, C.; Lagaris, I.E.: Towards “Ideal multistart”. A stochastic approach for locating the minima of a continuous function inside a bounded domain (2009)
  4. Blekas, K.; Lagaris, I.E.: Newtonian clustering: an approach based on molecular dynamics and global optimization (2007)
  5. Theos, F.V.; Lagaris, I.E.; Papageorgiou, D.G.: PANMIN: sequential and parallel global optimization procedures with a variety of options for the local search strategy (2004)
  6. Gioutsos, Dimitris V.; Vayonakis, Costas E.: On nonstandard vacuua in minimal supergravity models (2003)
  7. Tsoulos, Ioannis G.; Lagaris, Isaac E.; Likas, Aristidis C.: Piecewise neural networks for function approximation, cast in a form suitable for parallel computation (2002)
  8. Likas, Aristidis: Probability density estimation using artificial neural networks (2001)
  9. Gioutsos, D.V.: An efficient renormalization group improved implementation of the MSSM effective potential (2000)
  10. Likas, Aristides; Stafylopatis, Andreas: Training the random neural network using quasi-Newton methods (2000)
  11. Likas, A.; Karras, D.A.; Lagaris, I.E.: Neural network training and simulation using a multidimensional optimization system (1998)
  12. Papageorgiou, D.G.; Demetropoulos, I.N.; Lagaris, I.E.: MERLIN-3. 0. A multidimensional optimization environment (1998)
  13. Papageorgiou, D.G.; Demetropoulos, I.N.; Lagaris, I.E.: The MERLIN control language for strategic optimization (1998)
  14. Voltairas, P.A.; Massalas, C.V.; Lagaris, I.E.: Generalized resonance modes in ferromagnetic spheres (1992)