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

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2020.10-1 by Ravi Varadhan, a year ago

Browse source code at

Authors: Ravi Varadhan[aut, cre] , Johns Hopkins University , Hans W. Borchers[aut] , ABB Corporate Research , and Vincent Bechard[aut] , HEC Montreal (Montreal University)

Documentation:   PDF Manual  

Task views: Optimization and Mathematical Programming

GPL (>= 2) license

Imported by ConsReg, DynTxRegime, cops, diffusion, garma, matie, npcs, reReg, sklarsomega, stepPenal.

Depended on by BivarP, mvord.

Suggested by ROI.plugin.optimx, SACOBRA, afex, cxr, lme4, metadat, metafor, optimx, qra.

Enhanced by Rmpfr.

See at CRAN