43 Result(s)

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  1. No Access

    Article

    An Algorithmic Framework of Generalized Primal–Dual Hybrid Gradient Methods for Saddle Point Problems

    The primal–dual hybrid gradient method (PDHG) originates from the Arrow–Hurwicz method, and it has been widely used to solve saddle point problems, particularly in image processing areas. With the introduction...

    Bingsheng He, Feng Ma, Xiaoming Yuan in Journal of Mathematical Imaging and Vision (2017)

  2. No Access

    Article

    On the Iteration Complexity of Some Projection Methods for Monotone Linear Variational Inequalities

    Projection-type methods are important for solving monotone linear variational inequalities. In this paper, we analyze the iteration complexity of two projection methods and accordingly establish their worst-ca...

    Caihua Chen, Xiaoling Fu, Bingsheng He in Journal of Optimization Theory and Applica… (2017)

  3. No Access

    Article

    On the Proximal Jacobian Decomposition of ALM for Multiple-Block Separable Convex Minimization Problems and Its Relationship to ADMM

    The augmented Lagrangian method (ALM) is a benchmark for solving convex minimization problems with linear constraints. When the objective function of the model under consideration is representable as the sum o...

    Bingsheng He, Hong-Kun Xu, Xiaoming Yuan in Journal of Scientific Computing (2016)

  4. No Access

    Chapter

    Application of the Strictly Contractive Peaceman-Rachford Splitting Method to Multi-Block Separable Convex Programming

    Recently, a strictly contractive Peaceman-Rachford splitting method (SC-PRSM) was proposed to solve a convex minimization model with linear constraints and a separable objective function which is the sum of tw...

    Bingsheng He, Han Liu, Juwei Lu in Splitting Methods in Communication, Imagin… (2016)

  5. Article

    The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent

    The alternating direction method of multipliers (ADMM) is now widely used in many fields, and its convergence was proved when two blocks of variables are alternatively updated. It is strongly desirable and pra...

    Caihua Chen, Bingsheng He, Yinyu Ye, Xiaoming Yuan in Mathematical Programming (2016)

  6. No Access

    Article

    On the convergence rate of Douglas–Rachford operator splitting method

    This note provides a simple proof of a worst-case convergence rate measured by the iteration complexity for the Douglas–Rachford operator splitting method for finding a root of the sum of two maximal monotone ...

    Bingsheng He, Xiaoming Yuan in Mathematical Programming (2015)

  7. No Access

    Article

    On non-ergodic convergence rate of Douglas–Rachford alternating direction method of multipliers

    This note proposes a novel approach to derive a worst-case \(O(1/k)\) ...

    Bingsheng He, Xiaoming Yuan in Numerische Mathematik (2015)

  8. No Access

    Article

    Generalized alternating direction method of multipliers: new theoretical insights and applications

    Recently, the alternating direction method of multipliers (ADMM) has received intensive attention from a broad spectrum of areas. The generalized ADMM (GADMM) proposed by Eckstein and Bertsekas is an efficient...

    Ethan X. Fang, Bingsheng He, Han Liu, Xiaoming Yuan in Mathematical Programming Computation (2015)

  9. No Access

    Chapter and Conference Paper

    Understanding the Behavior of Solid State Disk

    In this paper, we develop a family of methods to characterize the behavior of new-generation Solid State Disks (SSDs). We first study how writes are handled inside the SSD by varying request size of writes and...

    Qingchao Cai, Rajesh Vellore Arumugam in Proceedings of the 18th Asia Pacific Sympo… (2015)

  10. No Access

    Chapter and Conference Paper

    Fast Subgraph Matching on Large Graphs using Graphics Processors

    Subgraph matching is the task of finding all matches of a query graph in a large data graph, which is known as an NP-complete problem. Many algorithms are proposed to solve this problem using CPUs. In recent y...

    Ha-Nguyen Tran, Jung-jae Kim, Bingsheng He in Database Systems for Advanced Applications (2015)

  11. No Access

    Article

    Inexact Alternating-Direction-Based Contraction Methods for Separable Linearly Constrained Convex Optimization

    Alternating direction method of multipliers has been well studied in the context of linearly constrained convex optimization. In the last few years, we have witnessed a number of novel applications arising fro...

    Guoyong Gu, Bingsheng He, Junfeng Yang in Journal of Optimization Theory and Applications (2014)

  12. No Access

    Article

    Customized proximal point algorithms for linearly constrained convex minimization and saddle-point problems: a unified approach

    This paper focuses on some customized applications of the proximal point algorithm (PPA) to two classes of problems: the convex minimization problem with linear constraints and a generic or separable objective...

    Guoyong Gu, Bingsheng He, Xiaoming Yuan in Computational Optimization and Applications (2014)

  13. No Access

    Article

    On the O(1/t) convergence rate of the projection and contraction methods for variational inequalities with Lipschitz continuous monotone operators

    Nemirovski’s analysis (SIAM J. Optim. 15:229–251, 2005) indicates that the extragradient method has the O(1/t) convergence rate for variational inequalities with Lipschitz continuous monotone operators. For the s...

    Xingju Cai, Guoyong Gu, Bingsheng He in Computational Optimization and Applications (2014)

  14. No Access

    Chapter

    GPU-Accelerated Cloud Computing for Data-Intensive Applications

    Recently, many large-scale data-intensive applications have emerged from the Internet and science domains. They pose significant challenges on the performance, scalability and programmability of existing data ...

    Baoxue Zhao, Jianlong Zhong, Bingsheng He in Cloud Computing for Data-Intensive Applica… (2014)

  15. No Access

    Article

    A customized proximal point algorithm for convex minimization with linear constraints

    This paper demonstrates a customized application of the classical proximal point algorithm (PPA) to the convex minimization problem with linear constraints. We show that if the proximal parameter in metric for...

    Bingsheng He, Xiaoming Yuan, Wenxing Zhang in Computational Optimization and Applications (2013)

  16. No Access

    Article

    Handling partitioning skew in MapReduce using LEEN

    MapReduce is emerging as a prominent tool for big data processing. Data locality is a key feature in MapReduce that is extensively leveraged in data-intensive cloud systems: it avoids network saturation when p...

    Shadi Ibrahim, Hai Jin, Lu Lu, Bingsheng He in Peer-to-Peer Networking and Applications (2013)

  17. No Access

    Article

    A proximal point algorithm revisit on the alternating direction method of multipliers

    The alternating direction method of multipliers (ADMM) is a benchmark for solving convex programming problems with separable objective functions and linear constraints. In the literature it has been illustrate...

    XingJu Cai, GuoYong Gu, BingSheng He, XiaoMing Yuan in Science China Mathematics (2013)

  18. No Access

    Chapter and Conference Paper

    A Framework for Analyzing Monetary Cost of Database Systems in the Cloud

    In this paper, we propose to develop a framework to analyze the monetary cost of running database systems in the public cloud. The framework offers guidelines and methodologies in analyzing and estimating mone...

    Changbing Chen, Bingsheng He in Web-Age Information Management (2013)

  19. No Access

    Chapter and Conference Paper

    Spectral Decomposition for Optimal Graph Index Prediction

    There is an ample body of recent research on indexing for structural graph queries. However, as verified by our experiments with a large number of random and scale-free graphs, there may be a great variation i...

    Liyan Song, Yun Peng, Byron Choi in Advances in Knowledge Discovery and Data M… (2013)

  20. No Access

    Chapter and Conference Paper

    MROrder: Flexible Job Ordering Optimization for Online MapReduce Workloads

    MapReduce has become a widely used computing model for large-scale data processing in clusters and data centers. A MapReduce workload generally contains multiple jobs. Due to the general execution constraints ...

    Shanjiang Tang, Bu-Sung Lee, Bingsheng He in Euro-Par 2013 Parallel Processing (2013)

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