We propose a Jacobi-style distributed algorithm to solve convex, quadratically constrained quadratic programs (QCQPs), which arise from broad range of applications. While small medium-sized convex QCQPs can be solved efficiently by interior-point algorithms, large-scale problems pose significant challenges traditional algorithms that are mainly designed implemented on single computing unit. The...

Linear factor models are familiar tools used in many fields. Several pioneering literatures established foundational theoretical results of the quasi-maximum likelihood estimator for high-dimensional linear models. Their based on a critical assumption: The error variance estimators uniformly bounded probability. Instead making such an assumption, we provide rigorous proof this result under some...