  Recently, Greg\'orio and Oliveira developed a proximal point scalarization
method (applied to multi-objective optimization problems) for an abstract
strict scalar representation with a variant of the logarithmic-quadratic
function of Auslender et al. as regularization. In this study, a variation of
this method is proposed, using the regularization with logarithm and
quasi-distance, which entails losing important properties, such as convexity
and differentiability. However, proceeding differently, it is shown that any
sequence \{(x^k, z^k)\} \includ R^n \times R^{m}_{++} generated by the method
satisfies: \{z^k\} is convergent and \{x^k\} is bounded and its accumulation
points are weak pareto solutions of the unconstrained multi-objective
optimization problem
