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Motivated by applications in recommendation systems and bioinformatics, we consider the problem of completing a low rank, partially observed binary matrix with graph information. We show that the corresponding problem can be set up in a positive and unlabeled data learning (referred to as PU learning in literature) framework. We make connections to convex optimization and show that existing greedy...
This paper deals with backstepping design for boundary PDE control/observer as a convex optimization problem. Both Volterra and Fredholm operators are analysed for a class of parabolic and hyperbolic PDEs. The resulting Kernel-PDEs are formulated in terms of polynomial functions, the parameters of which are optimized using Sum-of-Squares (SOS) techniques and solved via semidefinite programming. Uniqueness...
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