论文标题
用于求解非凸，非平滑，有限-AM优化问题的增量准Newton算法
Incremental Quasi-Newton Algorithms for Solving Nonconvex, Nonsmooth, Finite-Sum Optimization Problems
论文作者
论文摘要
提出和测试了用于解决非凸，非平滑，有限和有限优化问题的算法。特别是，在半监督机器学习中引起的优化问题制定的背景下，提出了算法和测试。所有算法的共同特征是它们采用增量准牛顿（IQN）策略，特别是增量BFGS（IBFGS）策略。一个人将IBFGS策略直接应用于该问题，而其他人则将IBFGS策略应用于召集差重新印象，平滑近似或（强烈）局部局部近似。实验表明，所有IBFGS在实践中的表现都很好，并且全部表现优于最先进的捆绑方法。
Algorithms for solving nonconvex, nonsmooth, finite-sum optimization problems are proposed and tested. In particular, the algorithms are proposed and tested in the context of an optimization problem formulation arising in semi-supervised machine learning. The common feature of all algorithms is that they employ an incremental quasi-Newton (IQN) strategy, specifically an incremental BFGS (IBFGS) strategy. One applies an IBFGS strategy to the problem directly, whereas the others apply an IBFGS strategy to a difference-of-convex reformulation, smoothed approximation, or (strongly) convex local approximation. Experiments show that all IBFGS approaches fare well in practice, and all outperform a state-of-the-art bundle method.
