Ranking as an important task in information systems has many applications, such as document/webpage retrieval, collaborative filtering and advertising. The last decade has witnessed a growing interest in the study of learning to rank as a means to leverage training information in a system. In this paper, we propose a new learning to rank method, i.e. BLM-Rank, which uses a linear function to score samples and models the pairwise preference of samples relying on their scores under a Bayesian framework. A stochastic gradient approach is adopted to maximize the posterior probability in BLM-Rank. For industrial practice, we have also implemented the proposed algorithm on Graphic Processing Unit (GPU). Experimental results on LETOR have demonstrated that the proposed BLM-Rank method outperforms the state-of-the-art methods, including RankSVM-Struct, RankBoost, AdaRank-NDCG, AdaRank-MAP and ListNet. Moreover, the results have shown that the GPU implementation of the BLM-Rank method is ten-to-eleven times faster than its CPU counterpart in the training phase, and one-to-four times faster in the testing phase.

Small loop gain and low crossover frequency result in poor dynamic performance of a single-loop output voltage controlled boost converter in continuous conduction mode. Multi-loop current control can improve the dynamic performance, however, the cost, size and weight of the circuit will also be increased. Sensorless multi-loop control solves the problems, however, the difficulty of the closed-loop characteristics evaluation will be severely aggravated, because there are more parameters in the loops, meanwhile, different from the single-loop, the relationships between the loop gains and closed-loop characteristics including audio susceptibility and output impedance are generally indirect for the multi-loop. Therefore, in this paper, a novel robust H∞ synthesis approach in the time-domain is proposed to design a sensorless controller for boost converters, which need not solve any algebraic Riccati equation or linear matrix inequalities, and most importantly, provides an approach to parameterizing the controller by an adjustable parameter. The adjustable parameter behaves like a ‘knob’ on the dynamic performance, consequently, which makes the closed-loop characteristics evaluation straightforward. A boost converter is used to verify the proposed synthesis approach. Simulations show the great convenience of the closed-loop characteristics evaluation. Practical experiments confirm the simulations.