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IEEE Power and Energy Society General Meeting

作者： Andy X. Sun Dzung T. Phan Soumyadip Ghosh School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta GA 30332 USA Business Analytics and Mathematical Sciences IBM Thomas J. Watson Research Center Yorktown Heights NY 10598 USA

ISBN: (纸本)9781479913022

Motivated by the increasing complexity in the control of distribution level electric power systems especially in a smart grid environment, we propose fully decentralized algorithms to solve alternating current (AC) optimal power flow (OPF) problems. The key feature of the proposed algorithms is a complete decentralization of computation down to nodal level. In this way, no central or sub-area controller is needed, and the OPF problem is solved by individual nodes, which only have local knowledge of the system. Preliminary results show promising performance of the fully decentralized algorithms.

关键词： Fully Decentralized AC Optimal Power flow algorithms Decentralized Smart grids optimal load flow ALTERNATING CURRENT Knowledge systems Indigenous knowledge power systems algorithms

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Generalized Steady-State VSC MTDC Model for Sequential AC/DC Power flow algorithms

Generalized Steady-State VSC MTDC Model for Sequential AC/DC...

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IEEE Power and Energy Society General Meeting

作者： Jef Beerten Stijn Cole Ronnie Belmans Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece

ISBN: (纸本)9781479913022

In this paper, a steady-state multi-terminal voltage source converter high voltage direct current (VSC MTDC) model is introduced. The proposed approach is extended to include multiple AC and DC grids with arbitrary topologies. The DC grids can thereby interconnect arbitrary buses in one or more non-synchronized AC systems. The converter equations are derived in their most general format and correctly define all set-points with respect to the system bus instead of the converter or filter bus, which is often done to simplify calculations. The paper introduces a mathematical model to include the converter limits and discusses how the equations change when a transformerless operation is considered or when the converter filter is omitted. An AC/VSC MTDC power flow is implemented using MATPOWER to show the validity of the generalized power flow model.

关键词： Generalized Steady-State VSC MTDC Model for Sequential AC/DC Power flow algorithms DC grid Converters system buses Power flow Variable structure control voltage source converter ADIPOQ gene direct current AC SYSTEMS Motor buses flow models Mathematical Model

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Data locality and replica aware virtual cluster embeddings

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THEORETICAL COMPUTER SCIENCE 2017年 697卷 37-57页

作者： Fuerst, Carlo Pacut, Maciej Schmid, Stefan TU Berlin Berlin Germany Univ Wroclaw Wroclaw Poland Aalborg Univ Aalborg Denmark

Virtualized datacenters offer great flexibilities in terms of resource allocation. In particular, by decoupling applications from the constraints of the underlying infrastructure, virtualization supports an optimized mapping of virtual machines as well as their interconnecting network (the so-called virtual cluster) to their physical counterparts: a graph embedding problem. However, existing virtual cluster embedding algorithms often ignore a crucial dimension of the problem, namely data locality: the input to a cloud application such as MapReduce is typically stored in a distributed, and sometimes redundant, file system. Since moving data is costly, an embedding algorithm should be data locality aware, and allocate computational resources close to the data;in case of redundant storage, the algorithm should also optimize the replica selection. This paper initiates the algorithmic study of data locality aware virtual cluster embeddings on datacenter topologies. We show that despite the multiple degrees of freedom in terms of embedding, replica selection and assignment, many problems can be solved efficiently. We also highlight the limitations of such optimizations, by presenting several NP-hardness proofs;interestingly, our hardness results also hold in uncapacitated networks of small diameter. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Virtual network embeddings flow algorithms NP hardness

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Parallel Pipelined Filter Ordering with Precedence Constraints

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ACM TRANSACTIONS ON algorithms 2012年第4期8卷 41-41页

作者： Deshpande, Amol Hellerstein, Lisa Univ Maryland College Pk MD 20742 USA NYU Polytech Inst Brooklyn NY USA

In the parallel pipelined filter ordering problem, we are given a set of n filters that run in parallel. The filters need to be applied to a stream of elements, to determine which elements pass all filters. Each filter has a rate limit r(i) on the number of elements it can process per unit time, and a selectivity p(i), which is the probability that a random element will pass the filter. The goal is to maximize throughput. This problem appears naturally in a variety of settings, including parallel query optimization in databases and query processing over Web services. We present an O(n(3)) algorithm for this problem, given tree-structured precedence constraints on the filters. This extends work of Condon et al. [2009] and Kodialam [2001], who presented algorithms for solving the problem without precedence constraints. Our algorithm is combinatorial and produces a sparse solution. Motivated by join operators in database queries, we also give algorithms for versions of the problem in which "filter" selectivities may be greater than or equal to 1. We prove a strong connection between the more classical problem of minimizing total work in sequential filter ordering (A), and the parallel pipelined filter ordering problem (B). More precisely, we prove that A is solvable in polynomial time for a given class of precedence constraints if and only if B is as well. This equivalence allows us to show that B is NP-Hard in the presence of arbitrary precedence constraints (since A is known to be NP-Hard in that setting).

关键词： Pipelined filter ordering parallel databases query optimization flow algorithms sequential testing

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Color-Coding algorithms to the Balanced Path Problem: Computational Issues

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INFORMS JOURNAL ON COMPUTING 2011年第3期23卷 446-459页

作者： Cappanera, Paola Scutella, Maria Grazia Univ Florence Dipartimento Sistemi & Informat I-50139 Florence Italy Univ Pisa Dipartimento Informat I-56127 Pisa Italy

Given a weighted directed network G, we consider the problem of computing k balanced paths from given source nodes to given destination nodes of G, i.e., k paths such that the difference in cost between the longest path and the shortest path is minimized. Although not yet investigated by the OR scientific community, except for some preliminary theoretical results concerning the special case of acyclic networks, balanced path problems arise in several interesting applications, such as in transportation and in telecommunication settings. In this work, the focus is on the computation of node-disjoint balanced paths in the general case, where the input graph G could have any structure. Starting from some algorithmic ideas proposed for acyclic networks, a general framework based on the color-coding method for computing simple paths is first described. Then the general framework is specialized, and a pool of algorithms is designed that includes both an exact approach as well as alternative heuristics. The algorithms have been tested on a large suite of instances generated from some benchmark telecommunication instances. An additional set of instances, generated from some benchmark crew scheduling instances, has been used to get an idea of the behavior of the algorithms in the context of transportation applications. The obtained computational results are very interesting. For the telecommunication instances, in some cases the exact algorithm produced the optimal solution very rapidly;in the remaining cases, some of the proposed heuristics were able to generate high-quality solutions in a very quick time. As for the crew scheduling instances, which are larger and sometimes appear more difficult than the telecommunication ones, a suitable combination of the proposed color-coding issues allowed us to compute the optimal solutions in very short times.

关键词： flow algorithms heuristics analysis of algorithms flow applications

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A Computational Study of the Pseudoflow and Push-Relabel algorithms for the Maximum flow Problem

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OPERATIONS RESEARCH 2009年第2期57卷 358-376页

作者： Chandran, Bala G. Hochbaum, Dorit S. Analyt Operat Engn Inc Boston MA 02109 USA Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA 94720 USA Univ Calif Berkeley Walter A Haas Sch Business Berkeley CA 94720 USA

We present the results of a computational investigation of the pseudoflow and push-relabel algorithms for the maximum flow and minimum s-t cut problems. The two algorithms were tested on several problem instances from the literature. Our results show that our implementation of the pseudoflow algorithm is faster than the best-known implementation of push-relabel on most of the problem instances within our computational study.

关键词： flow algorithms lowest label normalized tree parametric flow pseudoflow algorithm

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Incremental Network Optimization: Theory and algorithms

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OPERATIONS RESEARCH 2009年第3期57卷 586-594页

作者： Seref, Onur Ahuja, Ravindra K. Orlin, James B. Virginia Polytech Inst & State Univ Dept Business Informat Technol Blacksburg VA 24061 USA Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA MIT Alfred P Sloan Sch Management Cambridge MA 02139 USA

In an incremental optimization problem, we are given a feasible solution x(0) of an optimization problem P, and we want to make an incremental change in x(0) that will result in the greatest improvement in the objective function. In this paper, we study the incremental optimization versions of six well-known network problems. We present a strongly polynomial algorithm for the incremental minimum spanning tree problem. We show that the incremental minimum cost flow problem and the incremental maximum flow problem can be solved in polynomial time using Lagrangian relaxation. We consider two versions of the incremental minimum shortest path problem, where increments are measured via arc inclusions and arc exclusions. We present a strongly polynomial time solution for the arc inclusion version and show that the arc exclusion version is NP-complete. We show that the incremental minimum cut problem is NP-complete and that the incremental minimum assignment problem reduces to the minimum exact matching problem, for which a randomized polynomial time algorithm is known.

关键词： distance algorithms flow algorithms theory

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algorithms for Distributional and Adversarial Pipelined Filter Ordering Problems

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ACM TRANSACTIONS ON algorithms 2009年第2期5卷 1–34页

作者： Condon, Anne Deshpande, Amol Hellerstein, Lisa Wu, Ning Univ British Columbia Dept Comp Sci Vancouver BC V5Z 1M9 Canada Univ Maryland Dept Comp Sci College Pk MD 20742 USA NYU Polytech Inst Dept Comp & Informat Sci New York NY 10003 USA

Pipelined filter ordering is a central problem in database query optimization. The problem is to determine the optimal order in which to apply a given set of commutative filters (predicates) to a set of elements (the tuples of a relation), so as to find, as efficiently as possible, the tuples that satisfy all of the filters. Optimization of pipelined filter ordering has recently received renewed attention in the context of environments such as the Web, continuous high-speed data streams, and sensor networks. Pipelined filter ordering problems are also studied in areas such as fault detection and machine learning under names such as learning with attribute costs, minimum-sum set cover, and satisficing search. We present algorithms for two natural extensions of the classical pipelined filter ordering problem: (1) a distributional-type problem where the filters run in parallel and the goal is to maximize throughput, and (2) an adversarial-type problem where the goal is to minimize the expected value of multiplicative regret. We present two related algorithms for solving (1), both running in time O(n(2)), which improve on the O(n(3) log n) algorithm of Kodialam. We use techniques from our algorithms for (1) to obtain an algorithm for (2).

关键词： Pipelined filter ordering selection ordering query optimization flow algorithms

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The pseudoflow algorithm: A new algorithm for the maximum-flow problem

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OPERATIONS RESEARCH 2008年第4期56卷 992-1009页

作者： Hochbaum, Dorit S. Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA 94720 USA Univ Calif Berkeley Walter A Haas Sch Business Berkeley CA 94720 USA

We introduce the pseudoflow algorithm for the maximum-flow problem that employs only pseudoflows and does not generate flows explicitly. The algorithm solves directly a problem equivalent to the minimum-cut problem-the maximum blocking-cut problem. Once the maximum blocking-cut solution is available, the additional complexity required to find the respective maximum-flow is O(m log n). A variant of the algorithm is a new parametric maximum-flow algorithm generating all breakpoints in the same complexity required to solve the constant capacities maximum-flow problem. The pseudoflow algorithm has also a simplex variant, pseudoflow-simplex, that can be implemented to solve the maximum-flow problem. One feature of the pseudoflow algorithm is that it can initialize with any pseudoflow. This feature allows it to reach an optimal solution quickly when the initial pseudoflow is "close" to an optimal solution. The complexities of the pseudoflow algorithm, the pseudoflow-simplex, and the parametric variants of pseudoflow and pseudoflow-simplex algorithms are all O(mn log n) on a graph with n nodes and m arcs. Therefore, the pseudoflow-simplex algorithm is the fastest simplex algorithm known for the parametric maximum-flow problem. The pseudoflow algorithm is also shown to solve the maximum-flow problem on s, t-tree networks in linear time, where s, t-tree networks are formed by joining a forest of capacitated arcs, with nodes s and t adjacent to any subset of the nodes.

关键词： flow algorithms lowest label maximum flow normalized tree parametric flow pseudoflow algorithm

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Maximum network flows with concave gains

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MATHEMATICAL PROGRAMMING 2006年第3期107卷 439-459页

作者： Shigeno, M Univ Tsukuba Grad Sch Syst & Informat Engn Tsukuba Ibaraki 3058573 Japan

This paper deals with a generalized maximum flow problem with concave gains, which is a nonlinear network optimization problem. Optimality conditions and an algorithm for this problem are presented. The optimality conditions are extended from the corresponding results for the linear gain case. The algorithm is based on the scaled piecewise linear approximation and on the fat path algorithm described by Goldberg, Plotkin and Tardos for linear gain cases. The proposed algorithm solves a problem with piecewise linear concave gains faster than the naive solution by adding parallel arcs.

关键词： generalized networks flow algorithms nonlinear optimization

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