The problem of observer-based robust H-infinity control is addressed for a class of linear discrete-time switched systems with time-varying norm-bounded uncertainties by using switched Lyapunov function method. None of the individual subsystems is assumed to be robustly H-infinity solvable. A novel switched Lypunov function matrix with diagonal-block form is devised to overcome the difficulties in designing switching laws. For robust H-infinity stability analysis, two linear-matrix-inequality-based sufficient conditions are derived by only using the smallest region function strategy if some parameters are preselected. Then, the robust H-infinity control synthesis is studied using a switching state feedback and an observer-based switching dynamical output feedback. All the switching laws are simultaneously constructively designed. Finally, a simulation example is given to illustrate the validity of the results.
Parameter server (PS) as the state-of-the-art distributed framework for large-scale iterative machine learning tasks has been extensively studied. However, existing PS-based systems often depend on memory implementations. With memory constraints, machine learning (ML) developers cannot train large-scale ML models in their rather small local clusters. Moreover, renting large-scale cloud servers is always economically infeasible for research teams and small companies. In this paper, we propose a disk-resident parameter server system named DRPS, which reduces the hardware requirement of large-scale machine learning tasks by storing high dimensional models on disk. To further improve the performance of DRPS, we build an efficient index structure for parameters to reduce the disk I/O cost. Based on this index structure, we propose a novel multi-objective partitioning algorithm for the parameters. Finally, a flexible workerselection parallel model of computation (WSP) is proposed to strike a right balance between the problem of inconsistent parameter versions (staleness) and that of inconsistent execution progresses (straggler). Extensive experiments on many typical machine learning applications with real and synthetic datasets validate the effectiveness of DRPS.
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