Derivative-Free Optimization

Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems.


dfoptim NEWS

Changes in version 2018.2-1 (2018-4-01) o Set oshrink=1 to enable "restarting" of Nelder-Mead due to stagnation (thanks to Simon Wessing)

Changes in version 2017.12-1 (2017-12-20) o fixed a bug in the code, which impacts the "restarting" of Nelder-Mead due to stagnation (thanks to Simon Wessing)

Changes in version 2016.7-1 (2011-07-08)

o Used a slightly modified code for hjk() and hjkb()

Changes in version 2011.8-1 (2011-08-12)

o Bounds constrained Hooke-Jeeves hjkb()

Changes in version 2011.7-2 (2011-07-26)

o Bounds constrained Nelder-Mead nmkb().

Changes in version 2011.7-1

o Hooke-Jeeves minimization routine hjk().

Changes in version 2011.5-1

o Fixed minor bug in the re-definition of objective function inside
  for maximization.

Initial version

o Nelder-Mead minimization routine nmk().

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


2018.2-1 by Ravi Varadhan, a year ago

Browse source code at

Authors: Ravi Varadhan , Johns Hopkins University , and Hans W. Borchers , ABB Corporate Research.

Documentation:   PDF Manual  

Task views: Optimization and Mathematical Programming

GPL (>= 2) license

Imported by DynTxRegime, diffusion, matie, stepPenal.

Depended on by BivarP, mvord.

Suggested by ROI.plugin.optimx, SACOBRA, afex, lme4, metafor, optimx.

See at CRAN