检索结果-内蒙古大学图书馆

subgradient algorithms on Riemannian manifolds of lower bounded curvatures

OPTIMIZATION 2018年第1期67卷 179-194页

作者： Wang, X. M. Guizhou Univ Coll Math & Stat Guiyang Guizhou Peoples R China

The subgradient algorithms for convex optimizations on Riemannian Manifolds of sectional curvatures bounded from below are studied;and the convergence results of the algorithms (employing diminishing step sizes and dynamic step sizes) are established. Some numerical experiments are provided to illustrate the convergence performances of the algorithms.

关键词： Riemannian manifold convex optimization subgradient algorithm sectional curvature Riemannian Lp center of mass

来源：评论

学校读者我要写书评

暂无评论

The volume algorithm: producing primal solutions with a subgradient method

引用

MATHEMATICAL PROGRAMMING 2000年第3期87卷 385-399页

作者： Barahona, F Anbil, R IBM Corp Thomas J Watson Res Ctr Yorktown Heights NY 10598 USA

We present an extension to the subgradient algorithm to produce primal as well as dual solutions. It can he seen as a fast way to carry out an approximation of Dantzig-Wolfe decomposition. This gives a Fast method for producing approximations for large scale linear programs. It is based on a new theorem in linear programming duality. We present successful experience with linear programs coming from set partitioning, set covering, max-cut and plant location.

关键词： subgradient algorithm Dantzig-Wolfe decomposition large scale linear programming

来源：评论

学校读者我要写书评

暂无评论

Scenario decomposable subgradient projection method for two-stage stochastic programming with convex risk measures

引用

JOURNAL OF APPLIED MATHEMATICS AND COMPUTING 2023年第3期69卷 2389-2419页

作者： Yu, Haodong Shanghai Lixin Univ Accounting & Finance Sch Stat & Math Shanghai Peoples R China

We consider the general two-stage convex stochastic programs with discrete distribution, in which the risk measure is only assumed to be convex and monotonic, not necessarily to be coherent or have special structures. We propose a scenario decomposition framework which incorporates subgradient computation and the incremental constraint projection steps. The decomposition of the algorithm is based on the scenario-wise separability of these computations. We analyze the convergence and local rate of convergence of the proposed method under mild conditions. This method is further applied to a class of distributionally robust two-stage stochastic programs. Numerical results of a practical multi-product assembly model are reported to demonstrate the effectiveness of the proposed method.

关键词： Two stage stochastic programming Convex risk measures Scenario decomposition subgradient algorithm Incremental constraint projection

来源：评论

学校读者我要写书评

暂无评论

A Distributed Control Method With Minimum Generation Cost for DC Microgrids

引用

IEEE TRANSACTIONS ON ENERGY CONVERSION 2016年第4期31卷 1462-1470页

作者： Wang, Zhongguan Wu, Wenchuan Zhang, Boming Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China

This paper proposes a fully distributed control method for dc microgrids to realize power balance and bus voltage recovery without central coordination. Based on peer-to-peer communication, distributed generators and energy storage systems need only exchange information between neighbors over a sparse communication network, and hence there is a minimal communication burden. By combining the equal increment rate criteria and a subgradient algorithm, this distributed control method can efficiently regulate the bus voltage to the nominal value with minimum generation cost. In addition, the proposed method can improve the utilization of renewable energy generation via reasonable power sharing among distributed generators. Both islanded mode and grid-connected mode were simulated as case studies. The results indicate that the method is reliable, scalable, and flexible over centralized schemes. Influences of communication delays and failures of communication links are also discussed in numerical tests.

关键词： DC microgrid distributed control minimum generation cost subgradient algorithm

来源：评论

学校读者我要写书评

暂无评论

Using a Conic Bundle Method to Accelerate Both Phases of a Quadratic Convex Reformulation

引用

INFORMS JOURNAL ON COMPUTING 2017年第2期29卷 318-331页

作者： Billionnet, Alain Elloumi, Sourour Lambert, Amelie Wiegele, Angelika ENSIIE CEDRIC FR-91025 Evry France ENSTA CEDRIC F-91762 Palaiseau France CNAM CEDRIC FR-75141 Paris 03 France Alpen Adria Univ Klagenfurt Inst Math Klagenfurt Austria

We present algorithm MIQCR-CB that is an advancement of MIQCR. MIQCR is a method for solving mixed-integer quadratic programs and works in two phases: the first phase determines an equivalent quadratic formulation with a convex objective function by solving a semidefinite problem (SDP);in the second phase, the equivalent formulation is solved by a standard solver. Because the reformulation relies on the solution of a large-scale semidefinite program, it is not tractable by existing semidefinite solvers even for medium-sized problems. To surmount this difficulty, we present in MIQCR-CB a sub-gradient algorithm within a Lagrangian duality framework for solving (SDP) that substantially speeds up the first phase. Moreover, this algorithm leads to a reformulated problem of smaller size than the one obtained by the original MIQCR method, which results in a shorter time for solving the second phase. We present extensive computational results to show the efficiency of our algorithm. First, we apply MIQCR-CB to the k-cluster problem that can be formulated by a binary quadratic program. As an illustration of the efficiency of our new algorithm, for instances of size 80 and of density 25%, MIQCR-CB is on average 78 times faster for phase 1 and 24 times faster for phase 2 than the original MIQCR. We also compare MIQCR-CB with QCR and with BiqCrunch, two methods devoted to binary quadratic programming. We show that MIQCR-CB is able to solve most of the 225 considered instances within three hours of CPU time. We also present experiments on two classes of general integer instances where we compare MIQCR-CB with MIQCR, Couenne, and Cplex 12. 6. We demonstrate the significant improvement over the original MIQCR approach. Finally, we show that MIQCR-CB is able to solve almost all of the considered instances, whereas Couenne and Cplex 1 2. 6 are not able to solve half of them.

关键词： semidefinite programming Lagrangian duality subgradient algorithm bundle method convex reformulation quadratic 0-1 programming k-cluster densest subgraph

来源：评论

学校读者我要写书评

暂无评论

Content distribution by multiple multicast trees and intersession cooperation: Optimal algorithms and approximations

引用

COMPUTER NETWORKS 2015年 83卷 100-117页

作者： Zheng, Xiaoying Cho, Chunglae Xia, Ye Chinese Acad Sci Shanghai Adv Res Inst Beijing 100864 Peoples R China Shanghai Res Ctr Wireless Commun Shanghai Peoples R China Elect & Telecommun Res Inst Taejon 305606 South Korea Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA

In traditional massive content distribution with multiple sessions, the sessions form separate overlay networks and operate independently, where some sessions may suffer from insufficient resources even though other sessions have excessive resources. To cope with this problem, we consider the universal swarming approach, which allows multiple sessions to cooperate with each other. We formulate the problem of finding the optimal resource allocation to maximize the sum of the session utilities and present a subgradient algorithm which converges to the optimal solution in the time-average sense. The solution involves an NP-hard subproblem of finding a minimum-cost Steiner tree. We cope with this difficulty by using a column generation method, which reduces the number of Steiner-tree computations. Furthermore, we allow the use of approximate solutions to the Steiner-tree subproblem. We show that the approximation ratio to the overall problem turns out to be no less than the reciprocal of the approximation ratio to the Steiner-tree subproblem. Simulation results demonstrate that universal swarming improves the performance of resource-poor sessions with negligible impact to resource-rich sessions. The proposed approach and algorithm are expected to be useful for infrastructure-based content distribution networks with long-lasting sessions and relatively stable network environment. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi-tree multicast Optimization Tree packing Universal swarming Column generation subgradient algorithm

来源：评论

学校读者我要写书评

暂无评论

A weak-to-strong convergence principle for Fejer-monotone methods in Hilbert spaces

引用

MATHEMATICS OF OPERATIONS RESEARCH 2001年第2期26卷 248-264页

作者： Bauschke, HH Combettes, PL Okanagan Univ Coll Dept Math & Stat Kelowna BC V1V 1V7 Canada Univ Paris 06 Anal Numer Lab F-75005 Paris France

We consider a wide class of iterative methods arising in numerical mathematics and optimization that are known to converge only weakly. Exploiting an idea originally proposed by Haugazeau, we present a simple modification of these methods that makes them strongly convergent without additional assumptions. Several applications are discussed.

关键词： convex feasibility Fejer-monotonicity firmly nonexpansive mapping fixed point Haugazeau maximal monotone operator projection proximal point algorithm resolvent subgradient algorithm

来源：评论

学校读者我要写书评

暂无评论

Toward efficient parallel routing optimization for large-scale SDN networks using GPGPU

引用

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2018年 113卷 1-13页

作者： Wang, Xiong Zhang, Qian Ren, Jing Xu, Shizhong Wang, Sheng Yu, Shui Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic Australia

Routing optimization is an efficient way to improve network performance and guarantee the QoS requirements of users. However, with the rapid growth of network size and traffic demands, the routing optimization of SDN networks with centralized control plane is facing the scalability issue. To overcome the scalability issue, we aim to speed up the routing optimization process in large networks by utilizing the massive parallel computation capability of GPU. In this paper, we develop an efficient Lagrangian Relaxation based Parallel Routing Optimization algorithm (LR-PROA). LR-PROA first decomposes the routing optimization problem into a set of path calculation problems for the traffic demands by relaxing the link capacity constraints, then the path calculation tasks are dispatched to GPU and executed concurrently on GPU. In order to achieve high degree of parallelism, LR-PROA also parallelizes the path calculation process for each traffic demand. Furthermore, to improve the convergence speed, LR-PROA uses efficient methods to adjust the calculated paths for a part of traffic demands and set the step size of subgradient algorithm for solving the Lagrangian dual problem in each iteration. Our evaluations on synthetic network topologies verify that LR-PROA has good optimization performance as well as superior calculation time efficiency. In our simulations, LR-PROA is up to tens of times faster than benchmark algorithms in large networks.

关键词： Routing optimization Graphics processing unit (GPU) Parallel algorithm Laglangian relaxation subgradient algorithm

来源：评论

学校读者我要写书评

暂无评论

Multicast With Intraflow Network Coding in Multirate Multichannel Wireless Mesh Networks

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2013年第8期62卷 3913-3927页

作者： Lin, Kate Ching-Ju Yang, De-Nian Acad Sinica Res Ctr Informat Technol Innovat Taipei 115 Taiwan Acad Sinica Inst Informat Sci Taipei 115 Taiwan

It has been shown that intraflow network coding (NC) can improve the multicast throughput in single-channel single-rate wireless mesh networks. Most existing studies focus on developing a practical intraflow NC multicast protocol. With the rise of wireless products supporting multirate multichannel communications nowadays, it has become increasingly important to improve the NC gain by utilizing multiple bit rates and nonoverlapping channels, but new challenges have also arisen due to various tradeoffs. In a multirate multichannel multihop network, overhearing opportunities and bandwidth utilization are highly correlated to the selection of the channel and rate, which in turn determines the achievable throughput of multicast with intraflow NC. Specifically, wireless nodes can transmit at higher bit rates over orthogonal channels to increase the forwarding throughput, but the strategy reduces overhearing opportunities and the coding gain in multicast with intraflow NC. To achieve the best tradeoff between the given two aspects, we formulate a new optimization model called multirate multichannel multicast with intraflow NC (RCMNC), which solves the joint channel assignment, rate selection, and flow allocation problems for multihop intraflow NC multicast. We then reformulate the primal problem as the Lagrangian dual problem and propose an algorithm to approximate the solution of the dual problem in polynomial time. Simulation results show that, by assigning a suitable rate and channel to each network interface, the multicast throughput with intraflow NC under RCMNC is up to 3.3 times higher than that achieved by the existing approach in a multirate multichannel wireless mesh network.

关键词： Intraflow network coding (NC) multicast subgradient algorithm wireless mesh networks (WMNs)

来源：评论

学校读者我要写书评

暂无评论

Control learning rate for autism facial detection via deep transfer learning

引用

SIGNAL IMAGE AND VIDEO PROCESSING 2023年第7期17卷 3713-3720页

作者： El Mouatasim, Abdelkrim Ikermane, Mohamed Ibnou Zohr Univ Fac Polydisciplinary Ouarzazate FPO Appl Math & Computat Sci AMCS Grp Ouarzazate 45800 Morocco

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects social interaction and communication. Early detection of ASD can significantly improve outcomes for individuals with the disorder, and there has been increasing interest in using machine learning techniques to aid in the diagnosis of ASD. One promising approach is the use of deep learning techniques, particularly convolutional neural networks (CNNs), to classify facial images as indicative of ASD or not. However, choosing a learning rate for optimizing the performance of these deep CNNs can be tedious and may not always result in optimal convergence. In this paper, we propose a novel approach called the control subgradient algorithm (CSA) for tackling ASD diagnosis based on facial images using deep CNNs. CSA is a variation of the subgradient method in which the learning rate is updated by a control step in each iteration of each epoch. We apply CSA to the popular DensNet-121 CNN model and evaluate its performance on a publicly available facial ASD dataset. Our results show that CSA is faster than the baseline method and improves the classification accuracy and loss compared to the baseline. We also demonstrate the effectiveness of using CSA with L-1-regularization to further improve the performance of our deep CNN model.

关键词： subgradient algorithm Nonsmooth optimization Learning rate Autism facial detection Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：