SCOPE: Scalable Composite Optimization for Learning on Spark

Many machine learning models, such as logistic regression~(LR) and support vector machine~(SVM), can be formulated as composite optimization problems. Recently, many distributed stochastic optimization~(DSO) methods have been proposed to solve the large-scale composite optimization problems, which have shown better performance than traditional batch methods. However, most of these DSO methods are not scalable enough. In this paper, we propose a novel DSO method, called \underline{s}calable \underline{c}omposite \underline{op}timization for l\underline{e}arning~({SCOPE}), and implement it on the fault-tolerant distributed platform \mbox{Spark}. SCOPE is both computation-efficient and communication-efficient. Theoretical analysis shows that SCOPE is convergent with linear convergence rate when the objective function is convex. Furthermore, empirical results on real datasets show that SCOPE can outperform other state-of-the-art distributed learning methods on Spark, including both batch learning methods and DSO methods

Grey Critical Chain Project Scheduling Technique and Its Application

Based on the idea of Grey System and interval number coefficient notation, a Grey Critical Chain scheduling approach is studied. According to Grey system theory, the time of project or task completion can be considered as the object that extension is definite but intension is uncertain, which is coincident with the character of the project management. The Grey Critical Chain Scheduling Technique mainly aims at the single project time management, but the management idea can also be applied to the other knowledge areas of the project management. In this Technique, we improve the selection method of the buffer time in the Critical Chain, in order to obtain reasonable Feeding Buffer time and Project Buffer time. In this paper, we will use an example to discuss the Grey Critical Chain Scheduling Technique, compare Grey Critical Chain with Program Evaluation and Review Technique, Critical Chain and Fuzzy Critical Chain, analyze the advantages, disadvantages and applicable scope of their own. Key words: Critical Chain, Grey System, Interval Number, Schedule Management, Project Management Résumé: Sur la base de l’idée de Système Gris et la notation du coefficient de nombre d’intervalle, l’approche de pragrammtion d’une Chaîne Critique Grise est étudiée. Selon la théorie du Système Gris, le temps du projet ou de la tâche peut être considéré comme l’objet dont l’extention est définitive mais l’intention est incertaine, qui est conforme au caractère du management de projet. La Technique de Programmation de la Chaîne Critique Grise vise essentiellement le management du temps du projet simple, mais l’idée de management peut aussi être appliquée dans d’autres domaines du management de projet. Avec cette technique, nous améliorons la méthode de sélection du temps d’amortissement dans la Chaîne Critique afin d’obtenir le temps d’amortissement de l’alimentation raisonnable et le temps d’amortissement de projet. Dans l’article présent, nous allons utiliser un exemple pour discuter la Technique de Programmation de la Chaîne Critique Grise, comparer la Chaîne Critique Grise avec l’Evaluation du Programme et la Technique de révision, la Chaîne Critique et la Chaîne Critique Floue, et analyser leurs avantages, désavantages et champ d’application. Mots-Clés: Chaîne Critique, Système Gris, nombre d’intervalle, management de programme, management de proje

Energy-efficient multihop cooperative MISO transmission with optimal hop distance in wireless ad hoc networks

In this paper, we investigate the hop distance optimization problem in ad hoc networks where cooperative multiinput- single-output (MISO) is adopted to improve the energy efficiency of the network. We first establish the energy model of multihop cooperative MISO transmission. Based on the model, the energy consumption per bit of the network with high node density is minimized numerically by finding an optimal hop distance, and, to get the global minimum energy consumption, both hop distance and the number of cooperating nodes around each relay node for multihop transmission are jointly optimized. We also compare the performance between multihop cooperative MISO transmission and single-input-single-output (SISO) transmission, under the same network condition (high node density). We show that cooperative MISO transmission could be energyinefficient compared with SISO transmission when the path-loss exponent becomes high. We then extend our investigation to the networks with varied node densities and show the effectiveness of the joint optimization method in this scenario using simulation results. It is shown that the optimal results depend on network conditions such as node density and path-loss exponent, and the simulation results are closely matched to those obtained using the numerical models for high node density cases

Improving energy efficiency in a wireless sensor network by combining cooperative MIMO with data aggregation

In wireless sensor networks where nodes are powered by batteries, it is critical to prolong the network lifetime by minimizing the energy consumption of each node. In this paper, the cooperative multiple-input-multiple-output (MIMO) and data-aggregation techniques are jointly adopted to reduce the energy consumption per bit in wireless sensor networks by reducing the amount of data for transmission and better using network resources through cooperative communication. For this purpose, we derive a new energy model that considers the correlation between data generated by nodes and the distance between them for a cluster-based sensor network by employing the combined techniques. Using this model, the effect of the cluster size on the average energy consumption per node can be analyzed. It is shown that the energy efficiency of the network can significantly be enhanced in cooperative MIMO systems with data aggregation, compared with either cooperative MIMO systems without data aggregation or data-aggregation systems without cooperative MIMO, if sensor nodes are properly clusterized. Both centralized and distributed data-aggregation schemes for the cooperating nodes to exchange and compress their data are also proposed and appraised, which lead to diverse impacts of data correlation on the energy performance of the integrated cooperative MIMO and data-aggregation systems