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RCDS

Robust Conjugate Direction Search makes decisions via successive local approximations of the objective function to converge to an optimum. RCDS may be more efficient than Nelder-Mead but requires multiple iterations initially to establish a local model of the objective function before starting to optimize.

Advantages:

  • Low computational cost
  • Historically proven performance in the context of accelerator physics
  • Can account for measurement noise via algorithm hyperparameter
  • Can control scaling of step size

Disadvantages:

  • Local optimizer, sensitive to initial starting conditions
  • Scales poorly to higher dimensional problems
  • Cannot handle observational constraints

Parameters​

  • noise : Estimated noise level.
  • step : Step size for the optimization.