We consider inverse iteration-based eigensolvers, which require at each step solving an “inner” linear system. We assume that this linear system is solved by some (preconditioned) Krylov subspace method. In this framework, several approaches are possible, which differ by the linear system to be solved and/or the way the preconditioner is used. This includes methods such as inexact shift-and-inv...

We propose a Newton-like iteration that evolves on the set of fixed dimensional subspaces of Rn and converges locally cubically to the invariant subspaces of a symmetric matrix. This iteration is compared in terms of numerical cost and global behavior with three other methods that display the same property of cubic convergence. Moreover, we consider heuristics that greatly improve the global be...

As an important approach to solving complex sequential decision problems, reinforcement learning (RL) has been widely studied in the community of artificial intelligence and machine learning. However, the generalization ability of RL is still an open problem and it is difficult for existing RL algorithms to solve Markov decision problems (MDPs) with both continuous state and action spaces. In t...

Dividend policy is one of most important managerial decisions affecting the firm value. Although there are many studies regarding decision-making problems, such as credit policy decisions through bankruptcy prediction and credit scoring, there is no research, to our knowledge, about dividend prediction or dividend policy forecasting using machine learning approaches in spite of the significance...