The authors discuss an interesting technique to visualize various minimization methods. Their technique is very useful to better understand convergence -- whether a minimization method converges at all, how fast it converges, to which minimum it converges based on initial values, etc. The visualization technique introduced is applicable to visualizing the behavior of minimization methods for functions in $n$ variables.\parThe paper should be of interest for anyone concerned with multivariate optimization, convergence, and, in particular, visualization of mathematical algorithms.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Özdamar, Linet; Demirhan, Melek: Experiments with new stochastic global optimization search techniques (2000)
- Vrahatis, M. N.; Androulakis, G. S.; Lambrinos, J. N.; Magoulas, G. D.: A class of gradient unconstrained minimization algorithms with adaptive stepsize (2000)
- Androulakis, G. S.; Magoulas, G. D.; Vrahatis, M. N.: Geometry of learning: Visualizing the performance of neural network supervised training methods (1997)
- Androulakis, G. S.; Vrahatis, M. N.: OPTAC: A portable software package for analyzing and comparing optimization methods by visualization (1996)