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检索条件"主题词=Algorithm design and analysis"

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Optimization combining derivative-free global exploration with derivative-based local refinement 56

Optimization combining derivative-free global exploration wi...

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56th Annual IEEE Conference on Decision and Control (CDC)

作者： Alimo, Shahrouz Ryan Beyhaghi, Pooriya Bewley, Thomas R. Univ Calif San Diego Dept MAE La Jolla CA 92093 USA Univ Calif San Diego Fac Dept MAE La Jolla CA USA

ISBN: (纸本)9781509028733

This paper proposes a hybrid optimization scheme combining an efficient (and, under the appropriate assumptions, provably globally convergent) derivative-free optimization algorithm (dubbed.-DOGS), to globally explore expensive nonconvex functions, with a new derivative-based local optimization algorithm, to maximally accelerate local convergence from promising feasible points discovered in parameter space. The resulting hybrid optimization scheme proceeds iteratively, automatically shifting between (derivative-free) global exploration and (derivative-based) local refinement as appropriate. The new derivative-based local refinement method implemented uses the Voronoi partitioning of all existing datapoints at each iteration to establish a "modified trust-region" around the current best point, within which derivative-based optimization is considered. The resulting algorithm is analyzed, and its global convergence is proven under certain assumptions on the objective function. Finally, the algorithm is applied to nonconvex optimization problems with multiple local minima, and its computational cost compared with that of the original Delta-DOGS algorithm.

关键词： Optimization Convergence Uncertainty Acceleration Linear programming Search problems algorithm design and analysis

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Distributed Triangle Counting in the Graphulo Matrix Math Library

Distributed Triangle Counting in the Graphulo Matrix Math Li...

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IEEE High Performance Extreme Computing Conference (HPEC)

作者： Hutchison, Dylan Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781538634721

Triangle counting is a key algorithm for large graph analysis. The Graphulo library provides a framework for implementing graph algorithms on the Apache Accumulo distributed database. In this work we adapt two algorithms for counting triangles, one that uses the adjacency matrix and another that also uses the incidence matrix, to the Graphulo library for server-side processing inside Accumulo. Cloud-based experiments show a similar performance profile for these different approaches on the family of power law Graph500 graphs, for which data skew increasingly bottlenecks. These results motivate the design of skew-aware hybrid algorithms that we propose for future work.

关键词： algorithm design and analysis Libraries Distributed databases Cloning Encoding Data models

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Social Grammatical Evolution with Imitation Learning for Real-Valued Function Estimation

Social Grammatical Evolution with Imitation Learning for Rea...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Le, Nam O'Neill, Michael Fagan, David Brabazon, Anthony Univ Coll Dublin Nat Comp Res & Applicat Grp Dublin Ireland

ISBN: (纸本)9781509046010

Drawing on a rich literature concerning social learning in animals, this paper presents a variation of Grammatical Evolution (GE) which incorporates one of the most powerful forms of social learning, namely imitation learning. This replaces the traditional method of 'communication' between individuals in GE - crossover - which is drawn from an evolutionary metaphor. The paper provides an introduction to social learning, describes the proposed variant of GE, and tests on a series of benchmark symbolic regression problems. The results obtained are encouraging, being very competitive when compared with canonical GE. It is noted that the literature on social learning provides a number of useful meta-frameworks which can be used in the design of new search algorithms and to allow us to better understand the strengths and weaknesses of existing algorithms. Future work is indicated in this area.

关键词： Sociology Statistics algorithm design and analysis Animals Benchmark testing Optimization

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Efficiently Finding All Power Flow Solutions to Tree Networks 55

Efficiently Finding All Power Flow Solutions to Tree Network...

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55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

作者： Zachariah, Alisha Charles, Zachary Univ Wisconsin Dept Math Madison WI 53706 USA

ISBN: (纸本)9781538632666

In this paper we present an algorithm which provably finds all voltage solutions to the power flow equations on tree networks. Our algorithm uses elimination theory to reduce the equations to univariate polynomials whose roots govern the voltages of the entire tree. The algorithm utilizes the sparse structure of the tree to decrease its computational cost. We show that the runtime of the algorithm depends on the degrees of the vertices in the tree. This implies that in physically motivated distribution networks, our algorithm runs much faster than in the worst-case scenario, scaling more like the number of real solutions as opposed to the number of complex solutions. Finally, we compare our algorithm to common approaches to finding all voltage solutions, including homotopy continuation methods and Grobner basis methods. We show that our algorithm outperforms these other methods in the low-, medium-, and high-degree settings and scales better with the number of buses.

关键词： Artificial neural networks algorithm design and analysis Power system stability Topology Electronic mail Geometry

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design of Autopilot Platform Using HILS Approach

Design of Autopilot Platform Using HILS Approach

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International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE)

作者： Elsadig, Mohammed A. Elbakri, Mohamed A. Univ Alneelain Dept Postgrad Studies Khartoum Sudan Univ Khartoum Dept Elect & Elect Engn Khartoum Sudan

ISBN: (纸本)9781509018093

Each day comes, the sector of autopilot and Unmanned vehicles get bigger exponentially;the autopilot design process plays an essential role in the unmanned aerial vehicle (UAV) development process. Autopilot can be designed and developed using different approaches depending on the control theory which is adopted in this paper Proportional-integral-derivative (PID) is adopted. Testing of autopilot system during design process can be done using different methods such as wind tunnel which is the most accurate but also the most expensive, real fly tests may be used in verification of the autopilot but it's too risky, the third approach which is used in this paper is the Hardware in the loop simulation (HILS). Matlab/Simulink in association with Aerosim blockset library were used to develop the HILS, Arduino mega board was used as the autopilot hardware due to its availability, simplicity, and cost efficient features. A PID-based autopilot was designed for UAV stability and automatic pre-defined route following, the system has been tested using HILS with acceptable performance and cost efficient approach.

关键词： Atmospheric modeling Aircraft Hardware Damping Mathematical model MATLAB algorithm design and analysis Automatic pilots algorithm design and analysis Atmospheric models Computer hardware Aircraft MATLAB absorption of shock Mathematical Model hardware-in-the-loop simulation Control Theory drone honey bees Unmanned Aerial Vehicle design process

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Parallel and Efficient Sensitivity analysis of Microscopy Image Segmentation Workflows in Hybrid Systems

Parallel and Efficient Sensitivity Analysis of Microscopy Im...

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IEEE International Conference on Cluster Computing (CLUSTER)

作者： Barreiros, Willian, Jr. Teodoro, George Kurc, Tahsin Kong, Jun Melo, Alba C. M. A. Saltz, Joel Univ Brasilia Dept Comp Sci Brasilia DF Brazil SUNY Stony Brook Dept Biomed Informat Stony Brook NY 11794 USA Oak Ridge Natl Lab Sci Data Grp Oak Ridge TN USA Emory Univ Biomed Informat Dept Atlanta GA 30322 USA

ISBN: (纸本)9781538623268

We investigate efficient sensitivity analysis (SA) of algorithms that segment and classify image features in a large dataset of high-resolution images. algorithm SA is the process of evaluating variations of methods and parameter values to quantify differences in the output. A SA can be very compute demanding because it requires re-processing the input dataset several times with different parameters to assess variations in output. In this work, we introduce strategies to efficiently speed up SA via runtime optimizations targeting distributed hybrid systems and reuse of computations from runs with different parameters. We evaluate our approach using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. The SA attained a parallel efficiency of over 90% on 256 nodes. The cooperative execution using the CPUs and the Phi available in each node with smart task assignment strategies resulted in an additional speedup of about 2x. Finally, multi-level computation reuse lead to an additional speedup of up to 2.46x on the parallel version. The level of performance attained with the proposed optimizations will allow the use of SA in large-scale studies.

关键词： Image segmentation Sensitivity analysis Optimization algorithm design and analysis Runtime Image analysis

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GEEK: Grammatical Evolution for automatically Evolving Kernel functions 16

GEEK: Grammatical Evolution for automatically Evolving Kerne...

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16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications / The 11th IEEE International Conference on Big Data Science and Engineering / The 14th IEEE International Conference on Embedded Software and Systems

作者： Sousa, Arua de M. Lorena, Ana C. Basgalupp, Marcio P. Univ Fed Sao Paulo Inst Sci & Technol Sao Paulo Brazil

ISBN: (纸本)9781509049066

One of the key aspects in the successful use of kernel methods such as Support Vector Machines is the proper choice of the kernel function. While there are several well known kernel functions which can produce satisfactory results for various applications (e.g. RBF), they do not take into account specific characteristics of the data sets. Moreover, they have a set of parameters to be tuned. In this paper, we propose GEEK, a Grammatical Evolution approach for automatically Evolving Kernel functions. GEEK uses a grammar composed of simple mathematical operations extracted from known kernels and is also able to optimize some of their parameters. When combined through the Grammatical Evolution, these operations give rise to more complex kernel functions, adapted to each specific problem in a data-driven approach. The predictive results obtained by Support Vector Machines using the GEEK kernel functions were in general statistically similar to those of the standard RBF, Polynomial and Sigmoid kernel functions, which had their parameters optimized by a grid search method. Nonetheless, the GEEK kernels were able to handle more properly imbalanced classification problems, whilst the results of the standard kernel functions were biased towards the majority class.

关键词： Kernel Grammar Support vector machines Standards Optimization algorithm design and analysis Prediction algorithms

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An Enhanced Mixed Norm Error Criterion Adaptive Filtering algorithm for Sparse Channel Estimation

An Enhanced Mixed Norm Error Criterion Adaptive Filtering Al...

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Progress in Electromagnetics Research Symposium - Spring (PIERS)

作者： Wang, Yanyan Li, Yingsong Diao, Ming Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9781509062690

Mixed norm error (MNE) criterion has been successfully used to develop adaptive filtering algorithms. Recently, adaptive filter algorithms based on MNE has an excellent performance for system identification, including channel estimation. In this paper, an enhanced sparse mixed l(2) and l(p) norm error criterion algorithm is proposed by the use of a soft parameter function to modify the basic cost function of l(2) and l(p) algorithm to generate a desired zero attractor. The proposed sparse MNE algorithm can well explore the essential sparsity in multi-path wireless channel because of the designed zero attractor. The proposed sparse MNE algorithm is analyzed and mathematically derived in detail. Simulation examples are presented to verify its behavior via a sparse wireless multi-path channel. Additionally, we will also investigate the parameter affects on the proposed algorithm. The obtained results confirm that the excellent behavior of the proposed sparse MNE algorithm is achieved in terms of both convergence and steady-state error in comparison with the LMS/F, L2LP and their corresponding sparse algorithms.

关键词： Convergence algorithm design and analysis Channel estimation Signal processing algorithms Cost function Mathematical model Electromagnetics

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VACA: A High-Performance Variable-length Adaptive CRC algorithm 28

VACA: A High-Performance Variable-length Adaptive CRC Algori...

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28th Annual IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

作者： Chi, Mucong Liu, Jun BUPT Ctr Data Sci Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9781538635315

Cyclic Redundancy Check (CRC) is one of the most important error-detecting codes used in digital communication and storage systems. A number of algorithms based on parallel table lookups, which can process 32 or 64 bits at a time, have been proposed to accelerate CRC generation process. In recent years, parallel CRC algorithms with multi-processor architecture become popular, which further increase the processing speed. However, there is still much room to reduce the synchronization and recombination overheads for large messages to be processed. In this paper, we propose a coarse-grained parallel CRC algorithm for efficient n-core processor implementation. Our algorithm can be easily combined with existing fine-grained CRC methods to obtain a high performance. The synchronization and recombination in the proposed algorithm are deferred and needed only once to minimize the overhead cost. The evaluation results demonstrate that the proposed algorithm can achieve a speedup of a factor of almost n.

关键词： Software algorithms Synchronization Table lookup Generators algorithm design and analysis Acceleration Software

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Disordered Speech Quality Estimation Using Linear Prediction

Disordered Speech Quality Estimation Using Linear Prediction

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

作者： Ali, Yousef S. Ettomi Parsa, Vijay Doyle, Phillip Berkane, Soulaimane Univ Western Ontario Dept Elect & Comp Engn London ON Canada Univ Western Ontario Sch Commun & Speech Disorders London ON Canada

ISBN: (纸本)9781538607008

Tracheoesophageal (TE) speech is generated by patients who have undergone a total laryngectomy where the larynx (voice box) is removed and replaced by a tracheoesophageal puncture. This work presents a novel low complexity algorithm to estimate the degree of severity of disordered TE speech. The proposed algorithm uses features which are computed from 32-ms voiced frames of the speech signal. A 21-st order LPC analysis is performed on each voiced frame of the speech and high order statistics (central moments: mean, standard deviation, skewness and kurtosis) are extracted from the LPC coefficients, Cepstral coefficients and the LPC residual signal. The averages of each of these moments are computed along with the pitch average over all voiced frames yielding a total of 14 quality features. Experimental results with two sets of databases (20 and 35 TE speakers) showed that the proposed speech quality estimation approach performs well with a correlation with subjective scores in the range between 0.81 and 0.86.

关键词： Speech Estimation Correlation Databases Cepstral analysis Feature extraction algorithm design and analysis

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