Sparse methods for supervised learning aim at finding good linear predictors from as few variables as possible, i.e., with small cardinality of their supports. This combinatorial ...
In this paper, we propose a new shape decomposition method, called convex shape decomposition. We formalize the convex decomposition problem as an integer linear programming probl...
This paper considers constraint propagation methods for continuous constraint satisfaction problems consisting of linear and quadratic constraints. All methods can be applied after...
Most graph layout algorithms treat nodes as points. The problem of node overlap removal is to adjust the layout generated by such methods so that nodes of non-zero width and height...
Abstract— In this paper, we consider a discrete-time stochastic system, where sensor measurements are sent over a network to the controller. The design objective is a non-classic...