Auxiliary gradient-based sampling algorithms

Beamspace direction finding based on the conjugate gradient and the auxiliary vector filtering algorithms

Motivated by the performance of the direction finding algorithms based on the auxiliary vector filtering (AVF) method and the conjugate gradient (CG) method as well as the advantages of operating in beamspace (BS), we develop two novel direction finding algorithms for uniform linear arrays (ULAs) in the beamspace domain, which we refer to as the BS AVF and the BS CG methods. The recently propos...

Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares

In modern data analysis, random sampling is an efficient and widely-used strategy to overcome the computational difficulties brought by large sample size. In previous studies, researchers conducted random sampling which is according to the input data but independent on the response variable, however the response variable may also be informative for sampling. In this paper we propose an adaptive...

Exact Sampling Using Auxiliary Variables

Direct Gradient-Based Reinforcement Learning: I. Gradient Estimation Algorithms

Abstract Despite their many empirical successes, approximate value-function based approaches to reinforcement learning suffer from a paucity of theoretical guarantees on the performance of the policy generated by the value-function. In this paper we pursue an alternative approach: first compute the gradient of the average reward with respect to the parameters controlling the state transitions i...

Gradient-based genetic algorithms in image registration

Two modifications of Genetic algorithm (GA) are proposed that employ gradient analysis of the fitness function and are integrated with the main genetic procedure. Combination of the relative weighted error factor and adaptive size of the mutation pool accelerates convergence of the iterative process and indicates when the global optimum solution is found. Local gradient correction of the initia...