The current paper verifies the asynchronous Hsub.∞ control and optimization problem for flight vehicles with a time-varying delay. The nonlinear dynamic model and Jacobian linearization establish the ...flight vehicle’s switched model. An asynchronous Hsub.∞ tracking controller is designed, considering the existing asynchronous switching between the controllers and corresponding subsystems. In order to promote transient efficiency, the tracking controller comprises the model-based part and the learning-based part. The model-based part guarantees stability and prescribed efficiency, and the learning-based part compensates for undesirable uncertainties. The multiple Lyapunov function (MLF) and mode-dependent average dwell time (MDADT) methods are utilized to guarantee stability and the specified attenuation efficiency. The existing conditions and the solutions of model-based sub-controllers are represented by linear matrix inequalities (LMIs). The deep Q learning (DQL) algorithm provides the learning-based part. Different from the conventional method, the controller parameters are scheduled online. Therefore, robustness, stability, and dynamic efficiency can be met simultaneously. A numerical example illustrates the efficiency and advantage of the presented approach.
In this paper we show that the Minimum Spanning Tree problem (MST) can be solved deterministically in O(1) rounds of the Congested Clique model.
In the Congested Clique model there are n players that ...perform computation in synchronous rounds. Each round consist of a phase of local computation and a phase of communication, in which each pair of players is allowed to exchange O(logn) bit messages. The studies of this model began with the MST problem: in the paper by Lotker, Pavlov, Patt-Shamir, and Peleg SPAA’03, SICOMP’05 that defines the Congested Clique model the authors give a deterministic O(loglogn) round algorithm that improved over a trivial O(logn) round adaptation of Borůvka’s algorithm.
There was a sequence of gradual improvements to this result: an O(logloglogn) round algorithm by Hegeman, Pandurangan, Pemmaraju, Sardeshmukh, and Scquizzato PODC’15, an O(log* n) round algorithm by Ghaffari and Parter, PODC’16 and an O(1) round algorithm by Jurdziński and Nowicki, SODA’18, but all those algorithms were randomized. Therefore, the question about the existence of any deterministic o(loglogn) round algorithms for the Minimum Spanning Tree problem remains open since the seminal paper by Lotker, Pavlov, Patt-Shamir, and Peleg SPAA’03, SICOMP’05.
Our result resolves this question and establishes that O(1) rounds is enough to solve the MST problem in the Congested Clique model, even if we are not allowed to use any randomness. Furthermore, the amount of communication needed by the algorithm makes it applicable to a variant of the MPC model using machines with local memory of size O(n).
The aim of this study is to obtain the lower and upper bounds for the distance-based Wiener index, Hyper-Wiener index
and Harary index of the Fibonacci-sum graph. Firstly, the shortest distance of ...the vertices in the Fibonacci-sum graph is characterized.
Afterwards, the numbers of decision vertices and leaves are obtained by applying the BFS algorithm to the Fibonacci-sum graph.
The aim of this study is to obtain the lower and upper bounds for the distance-based Wiener index, Hyper-Wiener index
and Harary index of the Fibonacci-sum graph. Firstly, the shortest distance of the vertices in the Fibonacci-sum graph is characterized.
Afterwards, the numbers of decision vertices and leaves are obtained by applying the BFS algorithm to the Fibonacci-sum graph.
Distributed jobs typically facing competition for multiple resources in modern data centers, especially for network. Without effective network scheduling, this competition can cause low efficiency of ...the data center. Previous work on network scheduling has been focused on reducing flow completion time or improving per-flow fairness. Yet, its effect on improving jobs’ performance is limited by the unawareness of relationships between communication and computation. In this paper, we focus on the problem of scheduling network resources for multiple jobs, with the specific objective to reduce the job completion time (JCT), which also makes the datacenter more efficient. With an in-depth investigation of communication and computation, we identify an opportunity for accelerating jobs in a way that occupies less bandwidth for DAG-based complicated modern jobs. Accordingly, this paper proposes JIT, a job-aware network scheduler that leverages the computational graph to accelerate jobs effectively. To cater to the goal of JIT, we first develop a mathematical model and formulate the scheduling problem as an integer linear programming (ILP) problem. We further prove that it has an equivalent linear programming (LP) problem through rigorous theoretical analysis in order to solve this ILP problem efficiently. Some reasonable simplifications are also adopted to reduce the solving time of JIT to only 1 second. The proposed JIT is simulated and compared against some state-of-the-art designs, and the simulation results demonstrate that JIT can achieve an acceleration of up to 1.55 × , which successfully improves the efficiency of the data center.
In this paper, we deal with the Hsub.2/Hsub.∞ control problem in the infinite horizon for discrete-time mean-field stochastic systems with (x,u,v)-dependent noise. First of all, a stochastic-bounded ...real lemma, which is the core of Hsub.∞ analysis, is derived. Secondly, a sufficient condition in terms of the solution of coupled difference Riccati equations (CDREs) is obtained for solving the Hsub.2/Hsub.∞ control problem above. In addition, an iterative algorithm for solving CDREs is proposed and a numerical example is given for verification of the feasibility of the developed results.