Sequential minimal optimization (SMO) is one popular algorithm for training support vector machine (SVM), but it still requires a large amount of computation time for solving large size problems. This paper proposes one parallel implementation of SMO for training SVM. The parallel SMO is developed using message passing interface (MPI). Specifically, the parallel SMO first partitions the entire training data set into smaller subsets and then simultaneously runs multiple CPU processors to deal with each of the partitioned data sets. Experiments show that there is great speedup on the adult data set and the Mixing National Institute of Standard and Technology (MNIST) data set when many processors are used. There are also satisfactory results on the Web data set.

In this article, stable local solutions with global characteristics are developed for the weighted joint torque optimization in redundant manipulator. Conditions for these symmetric weight matrices are found that can guarantee that the local optimization of joint torque weighted by these matrices will have the same stability and optimality as the local optimization for the inertia inverse weighted joint torque. The symbolic results of the weighted projection operator of Jacobian matrix are also derived. It is also proposed that the weight matrix plays an important role in avoiding instabilities by relating the null space of the Jacobian matrix to what the authors termed an effective null space of J.>

Cox's proportional hazards regression model is a useful statistical tool for the analysis of 'survival data' from longitudinal studies. This multivariate method compares the 'survival experience' between two or more exposure groups while allowing for simultaneous adjustment of confounding due to one or more covariates. In addition to the summary regression statistics, further insight on the exposure--response relationship can be gained by visually examining the covariates-adjusted survival curves in the respective comparison groups. Covariates-adjusted survival curves are usually computed by the 'average covariate method'. This method is, however, subject to potential drawbacks. A method that avoids these drawbacks is to estimate adjusted survival curves by the corrected group prognostic curves approach. We have written a computer program to construct survival curves by the latter method. The program is coded in the Interactive Matrix Language of SAS.