Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems

The proximal gradient algorithm has been popularly used for convex optimization. Recently, it has also been extended for nonconvex problems, and the current state-of-the-art is the nonmonotone accelerated proximal gradient algorithm. However, it typically requires two exact proximal steps in each iteration, and can be inefficient when the proximal step is expensive. In this paper, we propose an efficient proximal gradient algorithm that requires only one inexact (and thus less expensive) proximal step in each iteration. Convergence to a critical point %of the nonconvex problem is still guaranteed and has a $O(1/k)$ convergence rate, which is the best rate for nonconvex problems with first-order methods. Experiments on a number of problems demonstrate that the proposed algorithm has comparable performance as the state-of-the-art, but is much faster

Energy Density Functional analysis of shape evolution in N=28 isotones

The structure of low-energy collective states in proton-deficient N=28 isotones is analyzed using structure models based on the relativistic energy density functional DD-PC1. The relativistic Hartree-Bogoliubov model for triaxial nuclei is used to calculate binding energy maps in the $\beta$-$\gamma$ plane. The evolution of neutron and proton single-particle levels with quadrupole deformation, and the occurrence of gaps around the Fermi surface, provide a simple microscopic interpretation of the onset of deformation and shape coexistence. Starting from self-consistent constrained energy surfaces calculated with the functional DD-PC1, a collective Hamiltonian for quadrupole vibrations and rotations is employed in the analysis of excitation spectra and transition rates of $^{46}$Ar, $^{44}$S, and $^{42}$Si. The results are compared to available data, and previous studies based either on the mean-field approach or large-scale shell-model calculations. The present study is particularly focused on $^{44}$S, for which data have recently been reported that indicate pronounced shape coexistence.Comment: 31 pages, 11 figures. arXiv admin note: text overlap with arXiv:1102.419

On the effects of seeding strategies: a case for search-based multi-objective service composition

Service composition aims to search a composition plan of candidate services that produces the optimal results with respect to multiple and possibly conflicting Quality-Of-Service (QoS) attributes, e.g., latency, throughput and cost. This leads to a multi-objective optimization problem for which evolutionary algorithm is a promising solution. In this paper, we investigate different ways of injecting knowledge about the problem into the Multi-Objective Evolutionary Algorithm (MOEA) by seeding. Specifcally, we propose four alternative seeding strategies to strengthen the quality of the initial population for the MOEA to start working with. By using the real-world WS-DREAM dataset, we conduced experimental evaluations based on 9 different work flows of service composition problems and several metrics. The results confirm the effectiveness and efficiency of those seeding strategies. We also observed that, unlike the discoveries for other problem domains, the implication of the number of seeds on the service composition problems is minimal, for which we investigated and discussed the possible reasons

FEMOSAA: Feature guided and knEe driven Multi-Objective optimization for Self-Adaptive softwAre

Self-Adaptive Software (SAS) can reconfigure itself to adapt to the changing environment at runtime, aiming to continually optimize conflicted nonfunctional objectives (e.g., response time, energy consumption, throughput, cost, etc.). In this article, we present Feature-guided and knEe-driven Multi-Objective optimization for Self-Adaptive softwAre (FEMOSAA), a novel framework that automatically synergizes the feature model and Multi-Objective Evolutionary Algorithm (MOEA) to optimize SAS at runtime. FEMOSAA operates in two phases: at design time, FEMOSAA automatically transposes the engineers’ design of SAS, expressed as a feature model, to fit the MOEA, creating new chromosome representation and reproduction operators. At runtime, FEMOSAA utilizes the feature model as domain knowledge to guide the search and further extend the MOEA, providing a larger chance for finding better solutions. In addition, we have designed a new method to search for the knee solutions, which can achieve a balanced tradeoff. We comprehensively evaluated FEMOSAA on two running SAS: One is a highly complex SAS with various adaptable real-world software under the realistic workload trace; another is a service-oriented SAS that can be dynamically composed from services. In particular, we compared the effectiveness and overhead of FEMOSAA against four of its variants and three other search-based frameworks for SAS under various scenarios, including three commonly applied MOEAs, two workload patterns, and diverse conflicting quality objectives. The results reveal the effectiveness of FEMOSAA and its superiority over the others with high statistical significance and nontrivial effect sizes

Return-Map Cryptanalysis Revisited

As a powerful cryptanalysis tool, the method of return-map attacks can be used to extract secret messages masked by chaos in secure communication schemes. Recently, a simple defensive mechanism was presented to enhance the security of chaotic parameter modulation schemes against return-map attacks. Two techniques are combined in the proposed defensive mechanism: multistep parameter modulation and alternative driving of two different transmitter variables. This paper re-studies the security of this proposed defensive mechanism against return-map attacks, and points out that the security was much over-estimated in the original publication for both ciphertext-only attack and known/chosen-plaintext attacks. It is found that a deterministic relationship exists between the shape of the return map and the modulated parameter, and that such a relationship can be used to dramatically enhance return-map attacks thereby making them quite easy to break the defensive mechanism.Comment: 11 pages, 7 figure

A review of the alumina recovery from coal fly ash, with a focus in China

Coal fly ash, an industrial by-product, is derived from coal combustion in thermal power plants. It is one of the most complex and abundant of anthropogenic materials and its improper disposal has become an environmental concern and resulted in a waste of recoverable resources. Coal fly ash is rich in alumina making it a potential substitute for bauxite. With the diminishing reserves of bauxite resources as well as the increasing demand for alumina, recovering alumina from fly ash has attracted extensive attentions. The present review first describes the alumina recovery history and technologies, and then focuses on the recovery status in China. Finally, the current status of fly ash recycling and directions for future research are considered