Robust and Efficient Optimization Using a Marquardt-Levenberg Algorithm with R Package marqLevAlg

Optimization is an essential task in many computational problems. In statistical modelling for instance, in the absence of analytical solution, maximum likelihood estimators are often retrieved using iterative optimization algorithms. R software already includes a variety of optimizers from general-purpose optimization algorithms to more specific ones. Among Newton-like methods which have good convergence properties, the Marquardt-Levenberg algorithm (MLA) provides a particularly robust algorithm for solving optimization problems. Newton-like methods generally have two major limitations: (i) convergence criteria that are a little too loose, and do not ensure convergence towards a maximum, (ii) a calculation time that is often too long, which makes them unusable in complex problems. We propose in the marqLevAlg package an efficient and general implementation of a modified MLA combined with strict convergence criteria and parallel computations. Convergence to saddle points is avoided by using the relative distance to minimum/maximum criterion (RDM) in addition to the stability of the parameters and of the objective function. RDM exploits the first and second derivatives to compute the distance to a true local maximum. The independent multiple evaluations of the objective function at each iteration used for computing either first or second derivatives are called in parallel to allow a theoretical speed up to the square of the number of parameters. We show through the estimation of 7 relatively complex statistical models how parallel implementation can largely reduce computational time. We also show through the estimation of the same model using 3 different algorithms (BFGS of optim routine, an E-M, and MLA) the superior efficiency of MLA to correctly and consistently reach the maximum.