The first nontrivial lower bound of the worst-case approximation ratio for the maxcut problem was achieved via the dual Cheeger problem, whose optimal value is referred to as the dual Cheeger constant h+, and later improved through its modification \(\widehat{h}^{+}\) . However, the dual Cheeger problem and its modification themselves are relatively unexplored, especially the lack of effective approximate algorithms. To this end, we first derive equivalent spectral formulations of h+ and \(\widehat{h}^{+}\) within the framework of the nonlinear spectral theory of signless 1-Laplacian, present their interactions with the Laplacian matrix and 1-Laplacians, and then use them to develop an inverse power algorithm that leverages the local linearity of the objective functions involved. We prove that the inverse power algorithm monotonically converges to a ternary-valued eigenvector, and provide the approximate values of h+ and \(\widehat{h}^{+}\) on the G-set for the first time. The recursive spectral cut algorithm for the maxcut problem can be enhanced by integrating it into the inverse power algorithms, leading to significantly improved approximate values on the G-set. Finally, we show that the lower bound of the worst-case approximation ratio for the maxcut problem within the recursive spectral cut framework cannot be improved beyond 0.769.
