Common best proximity point theorems: Global minimization of some real-valued multi-objective functions

Let us deliberate the question of computing a solution to the problems that can be articulated as the simultaneous equations \({Sx = x}\) and \({Tx = x}\) in the framework of metric spaces. However, when the mappings in context are not necessarily self-mappings, then it may be consequential that the equations do not have a common solution. At this juncture, one contemplates to compute a common approximate solution of such a system with the least possible error. Indeed, for a common approximate solution \({x^*}\) of the equations, the real numbers \({d(x^*, Sx^*)}\) and \({d(x^*,Tx^*)}\) measure the errors due to approximation. Eventually, it is imperative that one pulls off the global minimization of the multiobjective functions \({x \rightarrow d(x, Sx)}\) and \({x \rightarrow d(x, Tx)}\). When S and T are mappings from A to B, it follows that \({d(x, Sx) \geq d(A, B)}\) and \({d(x, Tx) \geq d(A, B)}\) for every \({x \in A}\). As a result, the global minimum of the aforesaid problem shall be actualized if it is ascertained that the functions \({x \rightarrow d(x, Sx)}\) and \({x \rightarrow d(x, Tx)}\) attain the lowest possible value d(A, B). The target of this paper is to resolve the preceding multiobjective global minimization problem when S is a T-cyclic contraction or a generalized cyclic contraction, thereby enabling one to determine a common optimal approximate solution to the aforesaid simultaneous equations.