Fast Parallel Randomized QR with Column Pivoting Algorithms for Reliable Low-rank Matrix Approximations

Factorizing large matrices by QR with column pivoting (QRCP) is substantially more expensive than QR without pivoting, owing to communication costs required for pivoting decisions. In contrast, randomized QRCP (RQRCP) algorithms have proven themselves empirically to be highly competitive with high-performance implementations of QR in processing time, on uniprocessor and shared memory machines, and as reliable as QRCP in pivot quality. We show that RQRCP algorithms can be as reliable as QRCP with failure probabilities exponentially decaying in oversampling size. We also analyze efficiency differences among different RQRCP algorithms. More importantly, we develop distributed memory implementations of RQRCP that are significantly better than QRCP implementations in ScaLAPACK. As a further development, we introduce the concept of and develop algorithms for computing spectrum-revealing QR factorizations for low-rank matrix approximations, and demonstrate their effectiveness against leading low-rank approximation methods in both theoretical and numerical reliability and efficiency.Comment: 11 pages, 14 figures, accepted by 2017 IEEE 24th International Conference on High Performance Computing (HiPC), awarded the best paper priz

The Role of Entrepreneurial Passion and Creativity in Entrepreneurial Intention: A Hierarchical Analysis of the Moderating Effect of Entrepreneurial Support Programs

Based on the MOA (Motivation - Opportunity - Ability) model framework, a hierarchical model is built and the application of the self-efficacy theory is discussed in relation entrepreneurial passion, creativity and entrepreneurial intention in Chinese university graduate students. The results of questionnaire surveys from 1057 students and 238 members of the faculty show that entrepreneurial passion and creativity positively influence entrepreneurial intention, and that entrepreneurial self-efficacy partly mediates the above relationship. The questionnaire results also reveal that entrepreneurial support programs positively moderate the relationship between entrepreneurial passion and entrepreneurial self-efficacy but negatively moderate the relationship between creativity and entrepreneurial self-efficacy. Based on empirical research, this paper provides a way forward to improving entrepreneurial intention for graduate students. Keywords: Entrepreneurial intention, Entrepreneurial passion, Creativity, Entrepreneurial support programs, Entrepreneurial self-efficac

Low-Rank Matrix Approximations with Flip-Flop Spectrum-Revealing QR Factorization

We present Flip-Flop Spectrum-Revealing QR (Flip-Flop SRQR) factorization, a significantly faster and more reliable variant of the QLP factorization of Stewart, for low-rank matrix approximations. Flip-Flop SRQR uses SRQR factorization to initialize a partial column pivoted QR factorization and then compute a partial LQ factorization. As observed by Stewart in his original QLP work, Flip-Flop SRQR tracks the exact singular values with "considerable fidelity". We develop singular value lower bounds and residual error upper bounds for Flip-Flop SRQR factorization. In situations where singular values of the input matrix decay relatively quickly, the low-rank approximation computed by SRQR is guaranteed to be as accurate as truncated SVD. We also perform a complexity analysis to show that for the same accuracy, Flip-Flop SRQR is faster than randomized subspace iteration for approximating the SVD, the standard method used in Matlab tensor toolbox. We also compare Flip-Flop SRQR with alternatives on two applications, tensor approximation and nuclear norm minimization, to demonstrate its efficiency and effectiveness

A Case Study on Foamy Oil Characteristics of the Orinoco Belt, Venezuela

With a current recovery of less than 11%, the Orinoco Belt in Venezuela still contains potentially more than 1.3 trillion barrels of reserves of “three highs, one low” oil at a depth of 100 to 1500 m. 5 joint projects and one project of Petroleos de Venezuela SA are making plans to improve oil recovery in the area. So it is important for them to have a thorough knowledge of foamy oil characteristics. This reservoir has a peculiar behavior called as a foamy phenomenon. In order to characterize the properties of the foamy oil, this paper discussed unconventional test methodology and the detailed suite of laboratory procedures including PVT and pressure depletion tests used to examine the Orinoco heavy oil. The results showed substantial differences in characteristics of foamy oil and conventional oil studied, not only in terms of PVT behavior but also in terms of the production performance during pressure depletion tests. The foamy oil compressibility was between 10-120×10-4 mPa-1, which was obviously higher than that of conventional oil. Differential liberation experiments of the oil, with obvious high formation volume factor, stable GOR, and low density showed a strong tendency to foam below the bubble point. Other notable observations were that more efficient oil recovery was achieved at high depletion rates while less free gas was produced.Key words: Foamy oil; Unconventional tests; The Orinoco Belt; PVT; Pressure depletion test

Battery Protective Electric Vehicle Charging Management in Renewable Energy System

The adoption of grid-connected electric vehicles (GEVs) brings a bright prospect for promoting renewable energy. An efficient vehicle-to-grid (V2G) scheduling scheme that can deal with renewable energy volatility and protect vehicle batteries from fast aging is indispensable to enable this benefit. This article develops a novel V2G scheduling method for consuming local renewable energy in microgrids by using a mixed learning framework. It is the first attempt to integrate battery protective targets in GEVs charging management in renewable energy systems. Battery safeguard strategies are derived via an offline soft-run scheduling process, where V2G management is modeled as a constrained optimization problem based on estimated microgrid and GEVs states. Meanwhile, an online V2G regulator is built to facilitate the real-time scheduling of GEVs' charging. The extreme learning machine (ELM) algorithm is used to train the established online regulator by learning rules from soft-run strategies. The online charging coordination of GEVs is realized by the ELM regulator based on real-time sampled microgrid frequency. The effectiveness of the developed models is verified on a U.K. microgrid with actual energy generation and consumption data. This article can effectively enable V2G to promote local renewable energy with battery aging mitigated, thus economically benefiting EV owns and microgrid operators, and facilitating decarbonization at low costs.</p