One of the most flexible parts of OpenMx is the way that the objective functions can be defined. An objective function for optimization results in a scalar number that is minimized. Examples of predefined objective functions include maximum likelihood (mxMLObjective) and full information maximum likelihood (mxFIMLObjective). However, other objective functions can be specified using the mxAlgebraObjective which allows one to specify a formula in the same way as an MxAlgebra is specified with the caveat that the result of the formula must be a 1 × 1 matrix. This allows the possibility of creating objective functions that perform specific optimizations such as variants of least squares or even various Bayesian optimizations.
