A kernel path algorithm for general parametric quadratic programming problem

Bin Gu, Ziran Xiong, Shuyang Yu, Guansheng Zheng

Abstract: Highlights•We study a general PQP problem that can be instantiated into many learning problems.•Based on the general PQP problem, we provide a unified and robust kernel path implementation (i.e. GKP) for an extensive number of PQP problems, many of which still do not have kernel path algorithms.•We analyze the iterative complexity and computational complexity of GKP.•We conduct experiments on various datasets, these results not only confirm the identity between GKP and several exiting specific kernel path algorithms (SKP), but also show that our GKP is superior to SKP in terms of generality and robustness.