Existing studies of the stationary points of the constant modulus adaptive algorithm, typically, assume a channel equalization setting and several restrictive conditions on the source sequence, the communications channel and the noise environment. Often, results are presented in terms of the combined channel and equalizer impulse response parameters, the source vector and the channel convolution matrix. As such, these results are not easily applicable in other areas. Modeling after Wiener filtering theory, this paper derives new expressions for the basic equations of the constant modulus (CM) optimization method. In particular, new formulas for the index function, its gradient vector and its Hessian matrix are obtained. These formulas involve the unknown parameters and the statistics of the sample sequence only. Consequently, they serve to convert the CM minimization problem into that of solving a system of cubic equations in the parameter vector. This new formulation of the CM optimization problem leads to a characterization of the critical points that is not exclusive to channel equalization. It also makes it possible to derive new equations for the minimum value of the cost function, the conditions for perfect signal recovery, and a tighter lower bound for the approach. Computer simulation examples are also presented to illustrate our findings.