This paper proposes a robust method to estimate the inverse covariance under noisy measurements. The method is based on the estimation of each column in the inverse covariance matrix independently via robust regression, which enables parallelization. Different from previous linear programming based methods that cannot guarantee a positive semi-definite covariance matrix, our method adjusts the ...

For the whole class of linear term rewriting systems and for each integer k, we define k-bounded rewriting as a restriction of the usual notion of rewriting. We show that the k-bounded uniform termination, the k-bounded termination, the inverse k-bounded uniform, and the inverse kbounded problems are decidable. The k-bounded class (BO(k)) is, by definition, the set of linear systems for which e...

Abstract We have proposed a block sparse approximate inverse with cutoff (BSAIC) preconditioner for relatively dense matrices. The BSAIC preconditioner is effective for semi-sparse matrices which have relatively large number of nonzero elements. This method reduces the computational cost for generating the preconditioning matrix, and overcomes the performance bottlenecks of SAI using the blocke...

Aimed at solving the misplaced body trajectory problem caused by the rolling of semi-round rigid feet when a robot is walking, a legged kinematic trajectory correction methodology based on the Least Squares Support Vector Machine (LS-SVM) is proposed. The concept of ideal foothold is put forward for the three-dimensional kinematic model modification of a robot leg, and the deviation value betwe...

This paper proposes a method to accommodate multiple tasks for redundancy utilization, which is based on a specific weighted pseudo-inverse. The proposed method also has task priority imposition property as those conventional task priority based method. In order to deal with general situations of task specification, the so-called semi-definitely weighted pseudoinverse is devised.