The asymptotic behavior of stochastic gradient algorithms is studied. Relying on some results of differential geometry (Lojasiewicz gradient inequality), the almost sure pointconve...
In this paper, we introduce a new genetic representation -- a splicing/decomposable (S/D) binary encoding, which was proposed based on some theoretical guidance and existing recom...
Vision systems are increasingly being deployed to perform complex surveillance tasks. While improved algorithms are being developed to perform these tasks, it is also important tha...
We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we...
Abstract--This work studies the near-optimality versus the complexity of distributed configuration management for wireless networks. We first develop a global probabilistic graphic...