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A dynamic optimization model for integrated production planning: Computational aspects

IEEE Transactions on Systems, Man and Cybernetics 1979年第11期9卷 689-695页

作者： Gruver, William A. Narasimhan, S.L. North Carolina State University Raleigh NC. United States Department of Management Science University of Rhode Island Kingston RI 02881 United States

The formulation and algorithmic solution of a class of optimal control models is discussed for integrated production, inventory, and research and development (R&D) planning. The model is based on the constrained minimization of the cost of labor, capital, and R&D engineering subject to a production constraint utilizing a Cobb-Douglas type input-output function. The solution technique utilizes a modification of a gradient projection algorithm due to Demyanov and Rubinov. Numerical results include a sensitivity analysis of the cost structures. Copyright © 1979 by The Institute of Electrical and Electronics Engineers, Inc.

关键词： Production planning Research and development Optimal control Technological innovation Hidden Markov models Economic forecasting Uncertainty Cost function projection algorithms Sensitivity analysis

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Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections 3

Projecting "better than randomly": How to reduce the dimensi...

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3rd IEEE/ACM International Conference on Data Science and Advanced Analytics (DSAA)

作者： Wojnowicz, Michael Zhang, Di Chisholm, Glenn Zhao, Xuan Wolff, Matt Cylance Inc Dept Res & Intelligence Irvine CA 92612 USA

ISBN: (纸本)9781509052066

For very large datasets, random projections (RP) have become the tool of choice for dimensionality reduction. This is due to the computational complexity of principal component analysis. However, the recent development of randomized principal component analysis (RPCA) has opened up the possibility of obtaining approximate principal components on very large datasets. In this paper, we compare the performance of RPCA and RP in dimensionality reduction for supervised learning. In Experiment 1, study a malware classification task on a dataset with over 10 million samples, almost 100,000 features, and over 25 billion non-zero values, with the goal of reducing the dimensionality to a compressed representation of 5,000 features. In order to apply RPCA to this dataset, we develop a new algorithm called large sample RPCA (LS-RPCA), which extends the RPCA algorithm to work on datasets with arbitrarily many samples. We find that classification performance is much higher when using LS-RPCA for dimensionality reduction than when using random projections. In particular, across a range of target dimensionalities, we find that using LS-RPCA reduces classification error by between 37% and 54%. Experiment 2 generalizes the phenomenon to multiple datasets, feature representations, and classifiers. These findings have implications for a large number of research projects in which random projections were used as a preprocessing step for dimensionality reduction. As long as accuracy is at a premium and the target dimensionality is sufficiently less than the numeric rank of the dataset, randomized PCA may be a superior choice. Moreover, if the dataset has a large number of samples, then LS-RPCA will provide a method for obtaining the approximate principal components.

关键词： Machine learning Predictive models projection algorithms

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Distributed kernel regression: An algorithm for training collaboratively

Distributed kernel regression: An algorithm for training col...

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IEEE Information Theory Workshop

作者： Predd, J. B. Kulkarni, S. R. Poor, H. V. Princeton Univ Dept Elect Engn Princeton NJ 08540 USA

ISBN: (纸本)9781424400355

This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed databases. After formalizing a general model for distributed learning, an algorithm for collaboratively training regularized kernel least-squares regression estimators is derived. Noting that the algorithm can be viewed as an application of successive orthogonal projection algorithms, its convergence properties are investigated and the statistical behavior of the estimator is discussed in a simplified theoretical setting.

关键词： projection algorithms distributed databases algorithms signal processing Distribute Estimators data mining Distance learning learning algorithms

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Resolution Maps: A Novel Metric for Electrical Impedance Tomography 20

Resolution Maps: A Novel Metric for Electrical Impedance Tom...

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20th International Conference on Body Sensor Networks

作者： Sheflin, Jessie M. Onsager, Claire C. Grayson, Matthew A. Northwestern Univ Biomed Engn Evanston IL USA Northwestern Univ Elec & Comp Engn Evanston IL USA Northwestern Univ Elec & Comp Engn Appl Phys Evanston IL USA

ISBN: (纸本)9798331530143

Resolution maps are a novel analysis method for electrical impedance tomography (EIT) that quantifies the precision and accuracy of image reconstruction at different locations within the region of interest. A projection operator is introduced which projects individual mesh elements of the simulated conducting area into the basis of orthogonal functions that span the data space of chosen measurements. Resolution maps are then made by evaluating these projections with regard to three different figures of merit: mean displacement from the original mesh coordinates, radial standard deviation, and ellipticity. This novel metric allows for comparisons between candidate sets of measurements and electrode geometries, as well as comparisons between locations in the same mesh.

关键词： Electrical impedance tomography Spatial resolution projection algorithms

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COMPRESSIVE SENSING FOR INCOHERENT IMAGING SYSTEMS WITH OPTICAL CONSTRAINTS

COMPRESSIVE SENSING FOR INCOHERENT IMAGING SYSTEMS WITH OPTI...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Renna, Francesco Rodrigues, Miguel R. D. Chen, Minhua Calderbank, Robert Carin, Lawrence Univ Porto Inst Telecomunicacoes Dept Ciencia Comp Rua Campo Alegre 823 P-4100 Oporto Portugal UCL Dept Elect & Elect Engn London WC1E 6BT England Univ Chicago Dept Stat Chicago IL 60637 USA Duke Univ Dept Elect Engn Durham NC 27708 USA

ISBN: (纸本)9781479903566

We consider the problem of linear projection design for incoherent optical imaging systems. We propose a computationally efficient method to obtain effective measurement kernels that satisfy the physical constraints imposed by an optical system, starting first from arbitrary kernels, including those that satisfy a less demanding power constraint. Performance is measured in terms of mutual information between the source input and the projection measurement, as well as reconstruction error for real world images. A clear improvement in the quality of image reconstructions is shown with respect to both random and adaptive projection designs in the literature.

关键词： Compressed sensing low resolution imaging projection algorithms

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Resource Efficient and Accurate Altitude Conversion to Mean Sea Level

Resource Efficient and Accurate Altitude Conversion to Mean ...

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IEEE/ION Position, Location and Navigation Symposium (PLANS)

作者： Julian, Brian Angermann, Michael Google New York NY 10011 USA Google Mountain View CA USA

ISBN: (纸本)9781665417723

In this paper we present a novel approach to efficiently and accurately convert altitudes to Mean Sea Level in real-time on resource limited devices, such as phones and wearables. The conversions are valid worldwide and do not require a network connection. We first discuss using a composite image to store on disk a spherical projection of an equirectangular map provided by the National Geospatial-Intelligence Agency. We then discuss creating an optimal image for our application, followed by using a least-recently-used cache to reduce volatile memory usage. An implementation of our work will be open-sourced and natively part of upcoming Android framework releases. It will also be available as a Jetpack library to support applications running on older API levels.

关键词： Geophysical measurements geophysical image processing projection algorithms digital representation mobile computing

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Jointly Optimal Fair Data Collection and Trajectory Design algorithms in UAV-Aided Cellular Networks

Jointly Optimal Fair Data Collection and Trajectory Design A...

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Song, Dan Zhai, Xiangping Bryce Liu, Xin Tan, Chee Wei Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Dalian Univ Technol Dalian Peoples R China City Univ Hong Kong Hong Kong Peoples R China Nanjing Univ Aeronaut & Astronaut Minist Ind & Informat Technol Key Lab Safety Crit Software Nanjing Peoples R China

ISBN: (纸本)9781728195056

Due to the flexible deployment and low cost of unmanned aerial vehicle (UAV), the integration of UAV and wireless cellular networks is widely regarded as a promising technology to enhance the performance of wireless cellular communications. This paper considers a UAV-aided wireless cellular communication system with multiple adjacent ground users (GUs), where the primary mission of UAV is to collect data from all of the GUs. We take the GUs as the topological nodes and combine their communication ranges to construct a ground topology structure (GTS), with the purpose of designing a reasonable trajectory for the UAV to execute the data collection tasks, while ensuring the fairness of transmission among all of GUs. In order to solve these problems, we utilize parallel projection algorithm onto homogeneous and heterogeneous GTS respectively to obtain a group of waypoints which construct the UAV trajectory, we formulate the fairness of data collection as a min-max problem. Finally, simulation experiments show the trajectory design results of the homogeneous and heterogeneous GTS respectively. Numerical results further vaildate the effectiveness of our proposed algorithms.

关键词： Unmanned aerial vehicle (UAV) wireless cellular networks trajectory design projection algorithms

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New Double projection Algorithm for Solving Variational Inequalities

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JOURNAL OF APPLIED MATHEMATICS 2013年第unknown期000卷 714397-1-714397-8页

作者： Zheng, Lian Yangtze Normal Univ Dept Math & Comp Sci Chongqing 408100 Peoples R China

We propose a class of new double projection algorithms for solving variational inequality problem, which can be viewed as a framework of the method of Solodov and Svaiter by adopting a class of new hyperplanes. By the separation property of hyperplane, our method is proved to be globally convergent under very mild assumptions. In addition, we propose a modified version of our algorithm that finds a solution of variational inequality which is also a fixed point of a given nonexpansive mapping. If, in addition, a certain local error bound holds, we analyze the convergence rate of the iterative sequence. Numerical experiments prove that our algorithms are efficient.

关键词： projection algorithm variational projection projection judicial separation Fixed point projection algorithms algorithms Local errors Hyperplane inequalities DOUBLE

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Linearly constrained orthogonal projection algorithm for reduced rank adaptive array processing

Linearly constrained orthogonal projection algorithm for red...

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Antennas and Propagation Society International Symposium

作者： Ding Qian jun Fei Ai guo Chen Hui

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Combining genetic algorithm and random projection strategy for (l, d)-motif discovery

Combining genetic algorithm and random projection strategy f...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Hongwei Huo Zhenhua Zhao Vojislav Stojkovic Lifang Liu School of Computer Science and Technology Xidian University Xi'an China Computer Science Department Morgan State University Baltimore MD USA

Identification of planted (Z, d)-motifs is an important and hard challenging problem in computational biology. In this paper, we present an original algorithm that combines genetic algorithm (GA) and random projection strategy (RPS) GARPS to identify (I, d)-motifs. We start with RPS to find good starting positions by introducing position-weight function and constructing a new hash function based on the function and return a set of candidate motifs. Then, we use the results(good candidate motifs) from RPS as the initial population of genetic algorithm to make series of iterations to refine motif candidates. We use the global search capability of GA and RPS are explored in GARPS. Experimental results on simulated data show that GARPS performs better than the projection algorithm and solves the most of challenging planted motif finding problems and improves finding faint motifs.

关键词： Genetic algorithms Sequences Evolutionary computation Computer science Biological system modeling projection algorithms System testing Evolution (biology) DNA computing Pattern matching

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