We propose the first combinatorial solution to one of the most classic problems in combinatorial optimization: the generalized minimum cost flow problem (flow with losses and g...
Genetic programming has been considered a promising approach for function approximation since it is possible to optimize both the functional form and the coefficients. However, it...
Partially-observable Markov decision processes (POMDPs) provide a powerful model for sequential decision-making problems with partially-observed state and are known to have (appro...
In evolutionary algorithms, the typical post-processing phase involves selection of the best-of-run individual, which becomes the final outcome of the evolutionary run. Trivial f...
We propose a convex-concave programming approach for the labeled weighted graph matching problem. The convex-concave programming formulation is obtained by rewriting the weighted ...
Mikhail Zaslavskiy, Francis Bach, Jean-Philippe Ve...