One of the most difficult challenges in face recognition is the large variation in pose. One approach to handle this problem is to use a 2D-Warping algorithm in a nearest-neighbor classifier. The 2D-Warping algorithm optimizes an energy function that captures the cost of matching pixels between two images while respecting the 2D dependencies defined by local pixel neighborhoods. Optimizing this energy function is an NP-complete problem and is therefore approached with algorithms that aim to approximate the optimal solution. In this paper we compare two algorithms that do this without discarding any 2D dependencies and we study the effect of the quality of the approximate solutions on the classification performance. Additionally, we propose a new algorithm that is capable of finding better solutions and obtaining better energies than the other methods. The experimental evaluation on the CMU-MultiPIE database shows that the proposed algorithm also achieves state-of-the-art recognition accuracies.