Unifying optimization algorithms to aid software system users: Optimx for R

R users can often solve optimization tasks easily using the tools in the optim function in the stats package provided by default on r installations. however,there are many other optimization and nonlinear modelling tools in r or in easily installed add-on packages. these present users with a bewildering array of choices. optimx is a wrapper to consolidate many of these choices for the optimization of functions that are mostly smooth with parameters at most bounds-constrained. we attempt to provide some diagnostic information about the function,its scaling and parameter bounds,and the solution characteristics. optimx runs a battery of methods on a given problem,thus facilitating comparative studies of optimization algorithms for the problem at hand. optimx can also be a useful pedagogical tool for demonstrating the strengths and pitfalls of different classes of optimization approaches including newton,gradient,and derivative-free methods.