On improving relaxation methods by modified gradient techniques

MRF energy minimization and beyond via dual decomposition

This paper introduces a new rigorous theoretical framework to address discrete MRF-based optimization in computer vision. Such a framework exploits the powerful technique of Dual Decomposition. It is based on a projected subgradient scheme that attempts to solve an MRF optimization problem by first decomposing it into a set of appropriately chosen subproblems, and then combining their solutions in a principled way. In order to determine the limits of this method, we analyze the conditions that these subproblems have to satisfy and demonstrate the extreme generality and flexibility of such an approach. We thus show that by appropriately choosing what subproblems to use, one can design novel and very powerful MRF optimization algorithms. For instance, in this manner we are able to derive algorithms that: 1) generalize and extend state-of-the-art message-passing methods, 2) optimize very tight LP-relaxations to MRF optimization, and 3) take full advantage of the special structure that may exist in particular MRFs, allowing the use of efficient inference techniques such as, e.g., graph-cut-based methods. Theoretical analysis on the bounds related with the different algorithms derived from our framework and experimental results/comparisons using synthetic and real data for a variety of tasks in computer vision demonstrate the extreme potentials of our approach.

A two-phase genetic K-means algorithm for placement of radioports in cellular networks

This paper proposes a two-phase hybrid algorithm for optimal placement of radioports. The placement strategy minimizes the dynamic range of the channel, and thus reduces the peak transmit power needed by a wireless handset. The performance of the proposed algorithm is compared with that of an exhaustive search, as well as with that of a simplex algorithm. Simulation results show that the two-phase hybrid algorithm, although suboptimal, outperforms the simplex algorithm with only a modest increase in computation time. It has the added advantage of being quite insensitive to the seed solution.