Nonlinear transformations for the simplification of unconstrained nonlinear optimization problems

Formalization decisions in mathematical programming could significantly influence the complexity of the problem, and so the efficiency of the applied solver methods. This widely accepted statement induced investigations for the reformulation of optimization problems in the hope of getting easier to solve problem forms, e.g. in integer programming. These transformations usually go hand in hand with relaxation of some constraints and with the increase in the number of the variables. However, the quick evolution and the widespread use of computer algebra systems in the last few years motivated us to use symbolic computation techniques also in the field of global optimization. We are interested in potential simplifications generated by symbolic transformations in global optimization, and especially in automatic mechanisms producing equivalent expressions that possibly decrease the dimension of the problem. As it was pointed out by Csendes and Rapcsák (J Glob Optim 3(2):213–221, 1993), it is possible in some cases to simplify the unconstrained nonlinear objective function by nonlinear coordinate transformations. That means mostly symbolic replacement of redundant subexpressions expecting less computation, while the simplified task remains equivalent to the original in the sense that a conversion between the solutions of the two forms is possible. We present a proper implementation of the referred theoretical algorithm in a modern symbolic programming environment, and testing on some examples both from the original publications and from the set of standard global optimization test problems to illustrate the capabilities of the method.