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optimization of Large-Scale Water Transfer Networks: Conic Integer Programming Model and distributed parallel Algorithms

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AICHE JOURNAL 2017年第5期63卷 1566-1581页

作者： Li, Li-Juan Zhou, Rui-Jie Rensselaer Polytech Inst Dept Chem & Biol Engn Troy NY 12180 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA

The optimization of a multi-echelon water transfer network (WTN) and the associate transportation and inventory systems with demand uncertainty is addressed in article. Optimal network structure, facility locations, operation capacities, as well as the inventory and transportation decisions can be simultaneously determined by the mixed integer nonlinear programming (MINLP) model which includes bilinear, square root and nonlinear fractional terms. By exploiting the properties of this model, we reformulate the MINLP problem as a conic integer optimization model. To overcome the memory and computing bandwidth limitations caused by the huge number of active nodes in the branch-and-bound search tree, novel distributed parallel optimization algorithms based on Lagrangean relaxation and message passing interface as well as their serial versions are proposed to solve the resulting conic integer programming model. A regional WTN in China is studied to demonstrate the applicability of the proposed model and the performance of the algorithms. (C) 2016 American Institute of Chemical Engineers AIChE J

关键词： water transfer networks mixed integer conic quadratic programming Lagrangean relaxation message passing interface distributed parallel optimization

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Joint capacity planning and distribution network optimization of coal supply chains under uncertainty

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AICHE JOURNAL 2018年第4期64卷 1246-1261页

作者： Zhou, Rui-Jie Li, Li-Juan Stanford Univ Dept Elect Engn Stanford CA 94305 USA Rensselaer Polytech Inst Dept Chem & Biol Engn Troy NY 12180 USA

A two-stage stochastic integer programming model is developed to address the joint capacity planning and distribution network optimization of multiechelon coal supply chains (CSCs) under uncertainty. The proposed model not only introduces the uses of compound real options in sequential capacity planning, but also considers uncertainty induced by both risks and ambiguities. Both strategic decisions (i.e., facility locations and initial investment, service assignment across the entire CSC, and option holding status) and scenario-based operational decisions (i.e., facility operations and capacity expansions, outsourcing policy, and transportation and inventory strategies) can be simultaneously determined using the model. By exploiting the nested decomposable structure of the model, we develop a new distributed parallel optimization algorithm based on nonconvex generalized Bender decomposition and Lagrangean relaxation to mitigate the computation resource limitation. One of the main CSCs in China is studied to demonstrate the applicability of the proposed model and the performance of the algorithm. (c) 2017 American Institute of Chemical Engineers AIChE J, 64: 1246-1261, 2018

关键词： coal supply chains compound real options risk and ambiguity two-stage stochastic integer programming distributed parallel optimization

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Tactical capacity planning for semiconductor manufacturing: MILP models and scalable distributed parallel algorithms

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AICHE JOURNAL 2016年第11期62卷 3930-3946页

作者： Zhou, Rui-Jie Li, Li-Juan Stanford Univ Dept Elect Engn Stanford CA 94305 USA Rensselaer Polytech Inst Dept Chem & Biol Engn Troy NY 12180 USA

A multiperiod stochastic mixed-integer linear programming model is developed to address the tactical capacity planning of semiconductor manufacturing with considerations of complex routing of material flows, in-process inventory, demand and capacity variability, multisite production, capacity utilization rate, and downside risk management. Both planning level decisions (i.e., capacity allocation and customer service level decisions) as well as operational level decisions (i.e., production, inventory, and shipment decisions) can be simultaneously determined based on the two proposed multiobjective optimization models. To address the huge number of scenarios needed to characterize the uncertainty and the large number of first-stage integer variables in industrial scale applications, two novel scalable distributed parallel optimization algorithms are developed to mitigate the computational burden. The proposed mathematical models and algorithms are illustrated through two case studies from a major US semiconductor manufacturer. Results from these case studies provide key decision support for capacity expansion in semiconductor industry. (c) 2016 American Institute of Chemical Engineers AIChE J, 62: 3930-3946, 2016

关键词： semiconductor manufacturing capacity planning two-stage stochastic integer optimization distributed parallel optimization scalable optimization

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Optimal control of network services based on generalized particle model

Optimal control of network services based on generalized par...

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3rd International Conference on Service Systems and Service Management

作者： Shuai, Diamun Dong, Yuming Shuai, Qing East China Univ Sci & Technol Shanghai 200237 Peoples R China Qingdao Technol Univ Qingdao Peoples R China Huazhong Univ Sci & Technol Zhengzhou Peoples R China

ISBN: (纸本)1424404509

The bandwidth allocation problem in ATM networks is NP-complete. This paper presents a novel generalized particle approach (GPA) to optimize the bandwidth allocation and Q o S parameter for ATM networks. The GPA transforms the optimization of ATM networks into a kinematics and dynamics of numerous particles in a force-field. The GPA has many advantages in terms of the higher parallelism, multi-objective optimization, multi-type coordination, and easiness for hardware implementation. During the ATM networks optimization, the GPA may deal with a variety of random and emergent phenomena, such as the congestion, failure, and interaction. This paper also gives the GPA's propertied regarding its correctness, convergency and stability. The simulations have shown the effectiveness and suitability of the GPA to the optimization of ATM networks.

关键词： ATM networks bandwidth allocation quality of service generalized particle distributed parallel optimization

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parallel optimization of K-SVD Algorithm for Image Denoising Based on Spark 13

Parallel Optimization of K-SVD Algorithm for Image Denoising...

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13th IEEE International Conference on Signal Processing (ICSP)

作者： Tan Chao Wei Zhihui Wu ZeBin Chen Yufeng Gu Jingping Nanjing Univ Sci & Technol Sch Sci Nanjing Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509013456

With the development of society, the image size and resolution are gradually increasing. At the same time, the fast image processing is required. The K-SVD algorithm is an iterative method that alternates between sparse coding and updating the overcomplete dictionary. It is a very simple and highly effective method for the signal of sparse representation. However, the K-SVD algorithm is a computationally intensive algorithm. The traditional computing model will take a lot of time to complete the calculation. As the next generation computing model, Spark not only has a strong computing power, but also has a memory-based fast processing capacity. In order to process image denoising efficiently, this paper proposes a implementation of distributed parallel optimization of K-SVD algorithm (K-SVD-P) on Spark. The results showed that K-SVDP not only has a good speed-up ratio, but also retains the image texture and other details.

关键词： Spark image denoising K-SVD sparse coding updating dictionary distributed parallel optimization K-SVD-P

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A novel generalized particle approach to optimize ATM networks

A novel generalized particle approach to optimize ATM networ...

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3rd IASTED International Conference on Communications and Computer Networks

作者： Shuai, DX Gong, R Wang, X Liu, RR E China Univ Sci & Technol Dept Comp Sci & Engn Shanghai 200237 Peoples R China

ISBN: (纸本)0889865442

The bandwidth allocation problem in ATM networks is NP-complete.(1). This paper presents a novel generalized particle approach (GPA) to optimize the bandwidth allocation and Q circle S parameter for ATM networks. The GPA transforms the optimization of ATM networks into a kinematics and dynamics of numerous particles in a force-field. The GPA has many advantages in terms of the higher parallelism, multi-objective optimization, multi-type coordination, and easiness for hardware implementation. During the ATM networks optimization, the GPA may deal with a variety of random and emergent phenomena, such as the congestion, failure, and interaction. This paper also gives the GPNs propertied regarding its correctness, convergency and stability. The simulations have shown the effectiveness and suitability of the GPA to the optimization of ATN4 networks.

关键词： ATM networks bandwidth allocation quality of service generalized particle distributed parallel optimization

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FAST PRINCIPAL COMPONENT ANALYSIS FOR HYPERSPECTRAL IMAGING BASED ON CLOUD COMPUTING

FAST PRINCIPAL COMPONENT ANALYSIS FOR HYPERSPECTRAL IMAGING ...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Li, Yonglong Wu, Zebin Wei, Jie Plaza, Antonio Li, Jun Wei, Zhihui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Univ Extremadura Dept Technol Comp & Commun E-10003 Caceres Spain Sun Yat Sen Univ Sch Geog & Planning Guangzhou 510275 Guangdong Peoples R China

ISBN: (纸本)9781479979295

Principal component analysis (PCA) is an important method for feature extraction of hyperspectral remote sensing image. With the development of hyperspectral sensors, the magnitude of hyperspectral data grows quickly, and it is a challenging task to efficiently reduce the data dimension and compress massive data volumes in hyperspectral imaging. In this paper, a distributed parallel optimization of PCA algorithm (PCA_DP) is presented on cloud computing architecture. The realization of the proposed method using Apache Hadoop and MapReduce model is described and evaluated. The experiments conducted on real hyperspectral images of different sizes, demonstrate significant acceleration factor of PCA_DP. It is efficient for massive hyperspectral data processing.

关键词： Principal component analysis hyperspectral remote sensing Hadoop distributed parallel optimization

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Convex optimization using Sparsified Stochastic Gradient Descent with Memory

Convex Optimization using Sparsified Stochastic Gradient Des...

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作者： Jean-Baptiste Cordonnier Ecole Polytechnique Federale de Lausanne

学位级别：硕士

The interest for distributed stochastic optimization has raised to train complex Machine Learning models with more data on distributed systems. Increasing the computation power speeds up the training but it faces a communication bottleneck between workers which hurts the scale-up of these distributed algorithms. Previous work tried to address this issue through quantization by broadcasting low precision updates (Seide et al., 2014; Alistarh et al., 2017) or through sparsification by sharing only the most important coordinates to update (Aji and Heafield, 2017; Lin et al., 2018). Even though the sparsifica- tion method works well in practice, it lacked a theoretical proof until now. We propose a sparsification scheme for SGD where only a small constant number of coordinates are applied at each iteration. The amount of data to be communicated is drastically reduced while the O (1 /T ) convergence rate of vanilla SGD is preserved. Our concise proof extends to parallel setting and gains a linear speed up in the number of workers. We exper- iment with sparsified SGD with memory in C++ and Python, and show the excellent convergence properties for top- k and random- k operators. Our scheme outperforms QSGD in progress per number of bits sent. It also opens the path to using lock-free asynchronous parallelization (e. g. Hogwild! Niu et al. (2011)) on dense problems as the sparsity of the gradient updates is enforced.

关键词： convex optimization stochastic gradient descent distributed parallel optimization sparsification communication efficiency

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parallel optimization of K-SVD Algorithm for Image Denoising Based on Spark

Parallel Optimization of K-SVD Algorithm for Image Denoising...

引用

2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Tan Chao Wei Zhihui Wu Ze Bin Chen Yufeng Gu Jingping School of Science Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing University of Science and Technology

With the development of society, the image size and resolution are gradually increasing. At the same time, the fast image processing is required. The K-SVD algorithm is an iterative method that alternates between sparse coding and updating the overcomplete dictionary. It is a very simple and highly effective method for the signal of sparse representation. However, the K-SVD algorithm is a computationally intensive algorithm. The traditional computing model will take a lot of time to complete the calculation. As the next generation computing model, Spark not only has a strong computing power, but also has a memory-based fast processing capacity. In order to process image denoising efficiently, this paper proposes a implementation of distributed parallel optimization of K-SVD algorithm(K-SVD-P) on Spark. The results showed that K-SVDP not only has a good speed-up ratio, but also retains the image texture and other details.

关键词： Spark image denoising K-SVD sparse coding updating dictionary distributed parallel optimization K-SVD-P

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