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Parallel low-memory quasi-Newton optimization algorithm for molecular structure

CHEMICAL PHYSICS LETTERS 2013年 584卷 10-13页

作者： Klemsa, Jakub Rezac, Jan Acad Sci Czech Republ Inst Organ Chem & Biochem CR-16610 Prague Czech Republic

We present a novel parallel gradient optimization algorithm designed for the optimization of molecular geometry - the parallel preconditioned LBFGS (PP-LBFGS) method. In each step, several additional gradient calculations (performed in parallel with the calculation of the potential) are used to improve the most important elements of the Hessian. The sparsity of the connectivity matrix and the graph theory are used to estimate multiple Hessian elements from each additional gradient calculation. The simplest variant of the algorithm, which requires 4 gradient evaluations per cycle, converges 2x-4x faster than the LBFGS algorithm, depending on the size of the system. (C) 2013 Elsevier B. V. All rights reserved.

关键词： Parallel low-memory quasi-Newton structure Parallel Lines optimization algorithms Graph theory Hessian molecular structure Gradient

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Optimal Cable Tension Distribution of the High-Speed Redundant Driven Camera Robots Considering Cable Sag and Inertia Effects

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ADVANCES IN MECHANICAL ENGINEERING 2014年第Pt.3期2014卷 97-97页

作者： Su, Yu Qiu, Yuanying Liu, Peng Xidian Univ Key Lab Minist Educ Elect Equipment Struct Design Xian 710071 Peoples R China

Camera robots are high-speed redundantly cable-driven parallel manipulators that realize the aerial panoramic photographing. When long-span cables and high maneuverability are involved, the effects of cable sags and inertias on the dynamics must be carefully dealt with. This paper is devoted to the optimal cable tension distribution (OCTD for short) of the camera robots. Firstly, each fast varying-length cable is discretized into some nodes for computing the cable inertias. Secondly, the dynamic equation integrated with the cable inertias is set up regarding the large-span cables as catenaries. Thirdly, an iterative optimization algorithm is introduced for the cable tension distribution by using the dynamic equation and sag-to-span ratios as constraint conditions. Finally, numerical examples are presented to demonstrate the effects of cable sags and inertias on determining tensions. The results justify the convergence and effectiveness of the algorithm. In addition, the results show that it is necessary to take the cable sags and inertias into consideration for the large-span manipulators.

关键词： Cameras cable tension dynamic equation Catenary cable Inertial forces optimization algorithms Robot Manipulators

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Identifying trade-offs between ecosystem services, land use, and biodiversity: a plea for combining scenario analysis and optimization on different spatial scales

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CURRENT OPINION IN ENVIRONMENTAL SUSTAINABILITY 2013年第5期5卷 458-463页

作者： Seppelt, Ralf Lautenbach, Sven Volk, Martin UFZ Helmholtz Ctr Environm Res Dept Computat Landscape Ecol D-04318 Leipzig Germany Univ Bonn Inst Geodet & Geoinformat D-53115 Bonn Germany

Research on mitigating land use conflicts is characterized by a variety of projects from the global to various sub-global scales. These projects are aiming at disentangling feedbacks within changing socio-environmental systems to identify strategies for sustainable resource use. Our review shows that any global analysis benefits from systematic synthesis of sub-global research from various scales, while sub-global investigations require embedding in global scenarios. There is an urgent need for improved methods to identify trade-offs at all scales as scenario analysis frequently results in a discrete set of options. We argue that the use of optimization algorithms including Pareto-frontiers combined with scenario analysis can provide efficient options for sustainable land use from global to subglobal scales.

关键词： discrete set Land use Dermal denticles spatial scale BALANCES ecosystem services optimization algorithms scene analysis

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Distributed algorithms for optimization Problems with Equality Constraints

Distributed Algorithms for Optimization Problems with Equali...

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IEEE Annual Conference on Decision and Control

作者： Ion Matei John S. Baras Palo Alto Research Center (PARC) Palo Alto CA Institute for Systems Research at the University of Maryland College Park MD

ISBN: (纸本)9781467357159

In this paper we introduce two discrete-time, distributed optimization algorithms executed by a set of agents whose interactions are subject to a communication graph. The algorithms can be applied to optimization problems where the cost function is expressed as a sum of functions, and where each function is associated to an agent. In addition, the agents can have equality constraints as well. The algorithms are not consensus-based and can be applied to non-convex optimization problems with equality constraints. We demonstrate that the first distributed algorithm results naturally from applying a first order method to solve the first order necessary conditions for a lifted optimization problem with equality constraints;the solution of our original problem is embedded in the solution of this lifted optimization problem. Using an augmented Lagrangian idea, we derive a second distributed algorithm that requires weaker conditions for local convergence compared to the first algorithm. For both algorithms we address the local convergence properties.

关键词： distributed algorithms Equality Agents Cost functions algorithms original problem discrete-time optimization algorithms lift

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Minimax Optimal algorithms for Unconstrained Linear optimization 13

Minimax Optimal Algorithms for Unconstrained Linear Optimiza...

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Annual Conference on Neural Information Processing Systems

作者： H. Brendan McMahan Jacob Abernethy Google Reasearch Seattle WA Computer Science and Engineering University of Michigan

ISBN: (纸本)9781632660244

We design and analyze minimax-optimal algorithms for online linear optimization games where the player's choice is unconstrained. The player strives to minimize regret, the difference between his loss and the loss of a post-hoc benchmark strategy. While the standard benchmark is the loss of the best strategy chosen from a bounded comparator set, we consider a very broad range of benchmark functions. The problem is cast as a sequential multi-stage zero-sum game, and we give a thorough analysis of the minimax behavior of the game, providing characterizations for the value of the game, as well as both the player's and the adversary's optimal strategy. We show how these objects can be computed efficiently under certain circumstances, and by selecting an appropriate benchmark, we construct a novel hedging strategy for an unconstrained betting game.

关键词： Game theory Players Game animals BENCHMARKS standardized benchmark Minimax Hunting optimization algorithms

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Multi-Query optimization in MapReduce Framework

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PROCEEDINGS OF THE VLDB ENDOWMENT 2013年第3期7卷 145-156页

作者： Wang, Guoping Chan, Chee-Yong Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore

MapReduce has recently emerged as a new paradigm for large-scale data analysis due to its high scalability, fine-grained fault tolerance and easy programming model. Since different jobs often share similar work (e.g., several jobs scan the same input file or produce the same map output), there are many opportunities to optimize the performance for a batch of jobs. In this paper, we propose two new techniques for multi-job optimization in the MapReduce framework. The first is a generalized grouping technique (which generalizes the recently proposed MRShare technique) that merges multiple jobs into a single job thereby enabling the merged jobs to share both the scan of the input file as well as the communication of the common map output. The second is a materialization technique that enables multiple jobs to share both the scan of the input file as well as the communication of the common map output via partial materialization of the map output of some jobs (in the map and/or reduce phase). Our second contribution is the proposal of a new optimization algorithm that given an input batch of jobs, produces an optimal plan by a judicious partitioning of the jobs into groups and an optimal assignment of the processing technique to each group. Our experimental results on Hadoop demonstrate that our new approach significantly outperforms the state-of-the-art technique, MRShare, by up to 107%.

关键词： Fault tolerance Grouping technique High scalabilities Large scale data analysis Mapreduce frameworks Multiquery optimization Optimal assignment optimization algorithms State of the art techniques

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Success and Failure of Adaptation-Diffusion algorithms for Consensus in Multi-Agent Networks

Success and Failure of Adaptation-Diffusion Algorithms for C...

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IEEE Annual Conference on Decision and Control

作者： Gemma Morral Pascal Bianchi Gersende Fort LTCI Telecom Paris-Tech & CNRS 46 rue Barrault 75634 Paris Cedex 13 France

ISBN: (纸本)9781467360890

This paper investigates the problem of distributed stochastic approximation in multi-agent systems. The algorithm under study consists of two steps: a local stochastic approximation step and a gossip step which drives the network to a consensus. The gossip step uses row-stochastic matrices to weight network exchanges. We first prove the convergence of a distributed optimization algorithm, when the function to optimize may not be convex and the communication protocol is independent of the observations. In that case, we prove that the average estimate converges to a consensus;we also show that the set of limit points is not necessarily the set of the critical points of the function to optimize and is affected by the Perron eigenvector of the mean-matrix describing the communication protocol. Discussion about the success or failure of convergence to the minimizers of the function to optimize is also addressed. In a second part of the paper, we extend the convergence results to the more general context of distributed stochastic approximation.

关键词： Communication protocols Converge stochastic approximation NETWORKING AGENT optimization algorithms Gossip Set of points Convexity

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Optimal algorithms for Testing Closeness of Discrete Distributions 14

Optimal Algorithms for Testing Closeness of Discrete Distrib...

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Annual ACM-Society for Industrial and Applied Mathmatics Symposium on Discrete algorithms

作者： Siu-On Chan Ilias Diakonikolas Gregory Valiant Paul Valiant MSR New England University of Edinburgh Stanford University Brown University

ISBN: (纸本)9781611973389

We study the question of closeness testing for two discrete distributions. More precisely, given samples from two distributions p and q over an n-element set, we wish to distinguish whether p = q versus p is at least ε-far from q, in either l_1 or l_2 distance. Batu et al [BFR~+00, BFR~+13] gave the first sub-linear time algorithms for these problems, which matched the lower bounds of [Val11] up to a logarithmic factor in n, and a polynomial factor of ε. In this work, we present simple testers for both the l_1 and l_2 settings, with sample complexity that is information-theoretically optimal, to constant factors, both in the dependence on n, and the dependence on ε; for the l_1 testing problem we establish that the sample complexity is Θ(max{n~(2/3)/=ε~(4/3), n~(1/2)/ε~2}).

关键词： sample complexity optimization algorithms Intimacy Lower bound sublinear DEPENDENCE

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RASR/NN: THE RWTH NEURAL NETWORK TOOLKIT FOR SPEECH RECOGNITION

RASR/NN: THE RWTH NEURAL NETWORK TOOLKIT FOR SPEECH RECOGNIT...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Simon Wiesler Alexander Richard Pavel Golik Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781479928941

This paper describes the new release of RASR - the open source version of the well-proven speech recognition toolkit developed and used at RWTH Aachen University. The focus is put on the implementation of the NN module for training neural network acoustic models. We describe code design, configuration, and features of the NN module. The key feature is a high flexibility regarding the network topology, choice of activation functions, training criteria, and optimization algorithm, as well as a built-in support for efficient GPU computing. The evaluation of run-time performance and recognition accuracy is performed exemplary with a deep neural network as acoustic model in a hybrid NN/HMM system. The results show that RASR achieves a state-of-the-art performance on a real-world large vocabulary task, while offering a complete pipeline for building and applying large scale speech recognition systems.

关键词： Speech recognition Acoustic models Neural network runtime performance optimization algorithms speech recognition system Buildings

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Opportunistic sensor scheduling in wireless control systems

Opportunistic sensor scheduling in wireless control systems

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IEEE Annual Conference on Decision and Control

作者： Konstantinos Gatsis Miroslav Pajic Alejandro Ribeiro George J. Pappas Department of Electrical and Systems Engineering University of Pennsylvania 200 South 33rd Street Philadelphia PA 19104

ISBN: (纸本)9781467360890

We consider a wireless control system where multiple power-constrained sensors transmit plant output measurements to a controller over a shared wireless medium. A centralized scheduler, situated at the controller, grants channel access to a single sensor on each time step. Given plant and controller dynamics, we design scheduling and transmit power policies that adapt opportunistically to the random wireless channel conditions experienced by the sensors. The objective is to obtain a stable system, by minimizing the expected decrease rate of a given Lyapunov function, while respecting the sensors' power constraints. We develop an online optimization algorithm based on the random channel sequence observed during execution which converges almost surely to the optimal protocol design.

关键词： Sensors wireless remote control optimization algorithms Liapunov function stream order Controllers stabilization system wireless wireless medium wireless channels Legal Executions

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