A penalty PALM method for sparse portfolio selection problems

In this paper, we propose a penalty proximal alternating linearized minimization method for the large-scale sparse portfolio problems in which a sequence of penalty subproblems are solved by utilizing the proximal alternating linearized minimization framework and sparse projection techniques. For exploiting the structure of the problems and reducing the computation complexity, each penalty subproblem is solved by alternately solving two projection problems. The global convergence of the method to a Karush-Kuhn-Tucker point or a local minimizer of the problem can be proved under the characteristic of the problem. The computational results with practical problems demonstrate that our method can find the suboptimal solutions of the problems efficiently and is competitive with some other local solution methods.     

Frequency conditions for stable networked controllers with time-delay

The paper analyses a networked control system consisting of a linear time-invariant system with a static feedback nonlinearity, subject to large delays in the feedback loop. This model is valid for example in wireless data flow control, where a saturation occurs since the flow is one-directional. The present paper extends previous results by proving necessity, in case the loop gain is uniformly less than one. The results are validated and illustrated in a simulation study.