GOMethod
GOMethod
Section Calculation Modes::Geometry Optimization
Type integer
Default fire
Method by which the minimization is performed. For more information see the
GSL documentation.
Options:
- steep:
Simple steepest descent.
- steep_native:
(Experimental) Non-gsl implementation of steepest descent.
- cg_fr:
Fletcher-Reeves conjugate-gradient algorithm. The conjugate-gradient algorithm proceeds as a succession of line minimizations. The sequence of search directions is used to build up an approximation to the curvature of the function in the neighborhood of the minimum.
- cg_pr:
Polak-Ribiere conjugate-gradient algorithm.
- cg_bfgs:
Vector Broyden-Fletcher-Goldfarb-Shanno (BFGS) conjugate-gradient algorithm. It is a quasi-Newton method which builds up an approximation to the second derivatives of the function f using the difference between successive gradient vectors. By combining the first and second derivatives, the algorithm is able to take Newton-type steps towards the function minimum, assuming quadratic behavior in that region.
- cg_bfgs2:
The bfgs2 version of this minimizer is the most efficient version available, and is a faithful implementation of the line minimization scheme described in Fletcher, Practical Methods of Optimization, Algorithms 2.6.2 and 2.6.4.
- simplex:
This is experimental, and in fact, not recommended unless you just want to fool around. It is the Nead-Melder simplex algorithm, as implemented in the GNU Scientific Library (GSL). It does not make use of the gradients (i.e., the forces) which makes it less efficient than other schemes. It is included here for completeness, since it is free.
- fire:
The FIRE algorithm. See also GOFireMass and GOFireIntegrator. Ref: E. Bitzek, P. Koskinen, F. Gahler, M. Moseler, and P. Gumbsch, Phys. Rev. Lett. 97, 170201 (2006).