This work focuses on the basic stochastic decomposition (SD) algorithm of Higle and Sen [J.L. Higle, S. Sen, Stochastic Decomposition, Kluwer Academic Publishers, 1996] for two-st...
Abstract--We propose an approach to accurately detecting twodimensional (2-D) shapes. The cross section of the shape boundary is modeled as a step function. We first derive a one-d...
Kernel functions have become an extremely popular tool in machine learning, with an attractive theory as well. This theory views a kernel as implicitly mapping data points into a ...
We describe a combination of runtime information and static analysis for checking properties of complex and configurable systems. The basic idea of our approach is to 1) let the p...
Most existing work uses dual decomposition and subgradient methods to solve Network Utility Maximization (NUM) problems in a distributed manner, which suffer from slow rate of con...