  In this paper we propose a set of guidelines to select a solver for the
solution of nonlinear programming problems. With this in mind, we present a
comparison of the convergence performances of commonly used solvers for both
unconstrained and constrained nonlinear programming problems. The comparison
involves accuracy, convergence rate, and convergence speed. Because of its
popularity among research teams in academia and industry, MATLAB is used as
common implementation platform for the solvers. Our study includes solvers
which are either freely available, or require a license, or are fully described
in literature. In addition, we differentiate solvers if they allow the
selection of different optimal search methods. As result, we examine the
performances of 23 algorithms to solve 60 benchmark problems. To enrich our
analysis, we will describe how, and to what extent, convergence speed and
accuracy can be improved by changing the inner settings of each solver.
