Methods of determining, from small-variable asymptotic expansions, the characteristic exponents for variables tending to infinity are analyzed. The following methods considered: diff-log Padé summation, self-similar factor approximation, Borel and Borel–Leroy summation. Several typical problems treated. comparison results shows that all these provide close estimates large-variable exponents. re...

This paper describes two novel on-policy reinforcement learning algorithms, named QV(λ)-learning and the actor critic learning automaton (ACLA). Both algorithms learn a state value-function using TD(λ)-methods. The difference between the algorithms is that QV-learning uses the learned value function and a form of Q-learning to learn Q-values, whereas ACLA uses the value function and a learning ...

The database research community has been building methods to store, access, and update data for more than four decades. Throughout the evolution of the structures and techniques used to access data, access methods adapt to the ever changing hardware and workload requirements. Today, even small changes in the workload or the hardware lead to a redesign of access methods. The need for new designs...

Abstract. I describe a new Markov chain method for sampling from the distribution of the state sequences in a non-linear state space model, given the observation sequence. This method updates all states in the sequence simultaneously using an embedded Hidden Markov model (HMM). An update begins with the creation of a “pool” of K states at each time, by applying some Markov chain update to the c...

Most existing quasi-Newton methods for nonlinear least squares problems incorporate both linear and nonlinear information in the secant update. These methods exhibit good theoretical properties, but are not especially accurate in practice. The objective of this paper is to propose quasi-Newton methods that only update the nonlinearities. We show two advantages of such updates. First, fast conve...