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检索条件"任意字段=21st International Conference on Integer Programming and Combinatorial Optimization, IPCO 2020"

共 37 条记录，以下是21-30 订阅

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A combinatorial Algorithm for Computing the Rank of a Generic Partitioned Matrix with 2 x 2 Submatrices 1

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21st international conference on integer programming and combinatorial optimization (ipco)

作者： Hirai, Hiroshi Iwamasa, Yuni Univ Tokyo Tokyo 1138656 Japan Kyoto Univ Kyoto 6068501 Japan

ISBN: (数字)9783030457716

ISBN: (纸本)9783030457716;9783030457709

In this paper, we consider the problem of computing the rank of a block-structured symbolic matrix (a generic partitioned matrix) A= (A(alpha beta)x(alpha beta)), where A(alpha beta )is a 2 x 2 matrix over a field F and x(alpha beta) is an indeterminate for alpha = 1, 2, ..., mu and beta = 1, 2, ..., nu. This problem can be viewed as an algebraic generalization of the bipartite matching problem, and was considered by Iwata and Murota (1995). One of recent interests on this problem lies in the connection with non-commutative Edmonds' problem by Ivanyos, Qiao and Subrahamanyam (2018), and Garg, Gurvits, Oliveiva and Wigderson (2019), where a result by Iwata and Murota implicitly says that the rank and the non-commutative rank (nc-rank) are the same for this class of symbolic matrices. The main result of this paper is a combinatorial O((mu nu)(2) min{mu, nu})-time algorithm for computing the symbolic rank of a (2 x 2)-type generic partitioned matrix of size 2 mu x 2v. Our algorithm is based on the Wong sequence algorithm by Ivanyos, Qiao, and Subrahamanyam for the ncrank of a general symbolic matrix, but is simpler. Our proposed algorithm requires no blow-up operation, no field extension, and no additional care for bounding the bit-size. Moreover it naturally provides a maximum rank completion of A for an arbitrary field F.

关键词： Generic partitioned matrix Edmonds' problem Non-commutative Edmonds' problem

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Increasing the PV Hosting Capacity in Unbalanced Three-Phase Distribution Networks Through Reconfiguration with Closed-Loop Operation 21

Increasing the PV Hosting Capacity in Unbalanced Three-Phase...

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21st IEEE international conference on Environment and Electrical Engineering / 5th IEEE Industrial and Commercial Power Systems Europe (EEEIC/I and CPS Europe)

作者： Macedo, Leonardo H. Vargas, Renzo Home-Ortiz, Juan M. Mantovani, Jose R. S. Romero, Ruben Catalao, Joao P. S. UNESP Ilha Solteira Brazil Univ Fed Abc Santo Andre SP Brazil FEUP Porto Portugal INESC TEC Porto Portugal

ISBN: (数字)9781665436137

ISBN: (纸本)9781665436137

This article presents a novel mixed-integer nonlinear linear programming (MINLP) model to increase the photovoltaic (PV) hosting capacity in unbalanced three- phase electrical distribution systems. The problem considers the optimal operation of capacitor banks and network reconfiguration with radial and closed-loop operation. The objective function maximizes the PV hosting capacity of the system. A set of linearization strategies are employed to convert the presented MINLP model into a mixed-integer linear programming (MILP) problem. By considering the MILP formulation, optimality is guaranteed by using off-the-shelf optimization solvers. Several tests are carried out using a 25-node unbalanced three-phase distribution system. When examining network reconfiguration with radial and closed-loop operation, the results demonstrate that the proposed formulation is effective in increasing the penetration of PV sources.

关键词： Closed-loop operation hosting capacity mixed-integer linear programming network reconfiguration unbalanced three-phase electrical distribution networks

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Matheuristic Algorithm Based on Neighborhood structure to Solve the Reconfiguration Problem of Active Distribution Systems 21

Matheuristic Algorithm Based on Neighborhood Structure to So...

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21st IEEE international conference on Environment and Electrical Engineering / 5th IEEE Industrial and Commercial Power Systems Europe (EEEIC/I and CPS Europe)

作者： Yumbla Romero, Jairo Gonzalo Home-Ortiz, Juan M. Javadi, Mohammad S. Gough, Matthew Sanches Mantovani, Jose Roberto Catalao, Joao P. S. Sao Paulo State Univ Dept Elect Engn Ilha Solteira Brazil INESC TEC Porto Portugal FEUP Porto Portugal

ISBN: (纸本)9781665436137

The problem of reconfiguration for active distribution systems is formulated as a stochastic mixed-integer second-order conic programming (MISOCP) model that simultaneously considers the minimization of energy power losses and CO2 emissions. The solution of the model determines the optimal radial topology, the operation of switchable capacitor banks, and the operation of dispatchable and non - dispatchable distributed generators. A stochastic scenario-based model is considered to handle uncertainties in load behavior, solar irradiation, and energy prices. The optimal solution of this model can be reached with a commercial solver;however, this is not computationally efficient. To tackle this issue a novel methodology which explores the efficiency of classical optimization techniques and heuristic based on neighborhood structures, referred as matheuristic algorithm is proposed. In this algorithm. the neighborhood search is carried out using the solution of reduced MISOCP models that are obtained from the original formulation of the problem. Numerical experiments are performed using several systems to compare the performance of the proposed matheuristic against the direct solution by the commercial solver CPLEX. Results demonstrate the superiority of the proposed methodology solving the problem for large-scale systems.

关键词： CO2 emissions mitigation distribution systems reconfiguration matheuristic mixed-integer second-order conic programming neighborhood search

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Solving the Coarseness Problem by ILP Using Column Generation 21st

Solving the Coarseness Problem by ILP Using Column Generatio...

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21st international conference on Computational Science and Its Applications (ICCSA)

作者： Sapucaia, Allan de Rezende, Pedro J. de Souza, Cid C. Univ Estadual Campinas Inst Comp Campinas Brazil

ISBN: (纸本)9783030869762;9783030869755

A core problem in machine learning is the classification of points in high dimensions. In an attempt to minimize the resources required and facilitate data visualization, a recent effort has been made to project the points non-linearly onto the plane and apply classification methods. The quality of any such projection can be measured by evaluating how the resulting points of different classes are set apart. In this paper, we study integer linear programming (ILP) models to determine the coarseness of sets of bicolored points in the plane, which measures how well-separated these two classes are. The complexity of computing the coarseness of a point set is unknown, but conjectured to be NP-hard. We present a base ILP model for the problem with an exponential number of variables and constraints, followed by a series of improvements in the quality of its relaxation and close with an efficient column generation implementation. These modifications allow us to solve instances with three times as many points as the base model, within the same time and memory limits. By combining of our preprocessing techniques and a heuristic from the literature, we are even able to solve to proved optimality many instances of our benchmark in polynomial time. A comprehensive experimental study is presented to evaluate the impact of each improvement.

关键词： Computational geometry combinatorial optimization Coarseness

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Tight Approximation Bounds for Maximum Multi-coverage 1

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21st international conference on integer programming and combinatorial optimization (ipco)

作者： Barman, Siddharth Fawzi, Omar Ghoshal, Suprovat Gurpinar, Emirhan Indian Inst Sci Bangalore Karnataka India Univ Lyon CNRS ENS Lyon UCBLLIP F-69342 Lyon 07 France

ISBN: (数字)9783030457716

ISBN: (纸本)9783030457716;9783030457709

In the classic maximum coverage problem, we are given sub- sets T-1, ..., T-m of a universe [n] along with an integer k and the objective is to find a subset S subset of [m] of size k that maximizes C(S) := vertical bar boolean OR(i is an element of S) T-i vertical bar. It is well-known that the greedy algorithm for this problem achieves an approximation ratio of (1 - e(-1)) and there is a matching inapproximability result. We note that in the maximum coverage problem if an element e is an element of [n] is covered by several sets, it is still counted only once. By contrast, if we change the problem and count each element e as many times as it is covered, then we obtain a linear objective function, C-(infinity) (S) = Sigma(i is an element of S) vertical bar T-i vertical bar which can be easily maximized under a cardinality constraint. We study the maximum l-multi-coverage problem which naturally interpolates between these two extremes. In this problem, an element can be counted up to times but no more;hence, we consider maximizing the function C-(l) (S) = Sigma(e is an element of[n]) min{l, vertical bar i is an element of S : e is an element of T-i}vertical bar}, subject to the constraint vertical bar S vertical bar <= k. Note that the case of l = 1 corresponds to the standard maximum coverage setting and l = infinity gives us a linear objective. We develop an efficient approximation algorithm that achieves an approximation ratio of 1 - l(l) e(-l)/l! for the l-multi-coverage problem. In particular, when l = 2, this factor is 1 - 2e(-2) approximate to 0.73 and as grows the approximation ratio behaves as 1 - 1/root 2 pi l. We also prove that this approximation ratio is tight, i.e., establish a matching hardness-of-approximation result, under the Unique Games Conjecture. This problem is motivated by the question of finding a code that optimizes the list-decoding success probability for a given noisy channel. We show how the multi-coverage problem can be relevant in oth

关键词： Approximation algorithms Covering problems Submodular optimization

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21st international conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

21st International Conference on Parallel and Distributed Co...

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21st international conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

ISBN: (纸本)9783030692438

The proceedings contain 35 papers. The special focus in this conference is on Parallel and Distributed Computing, Applications, and Technologies. The topics include: On the Non-ergodic Convergence Rate of the Directed Nonsmooth Composite optimization;6D Pose Estimation Based on the Adaptive Weight of RGB-D Feature;blockchain-Based Secure Outsourcing of Fully Homomorphic Encryption Using Hidden Ideal Lattice;multiple Projections Learning for Dimensional Reduction;Preventing DDoS Attacks on Bitcoin Memory Pool by the Dynamic Fee Threshold Mechanism;The Compiler of DFC: A Source Code Converter that Transform the Dataflow Code to the Multi-threaded C Code;online Learning-Based Co-task Dispatching with Function Configuration in Edge Computing;System-Level FPGA Routing for Logic Verification with Time-Division Multiplexing;protein Interresidue Contact Prediction Based on Deep Learning and Massive Features from Multi-sequence Alignment;heterogeneous Software Effort Estimation via Cascaded Adversarial Auto-Encoder;see Fine Color from the Rough Black-and-White;data Aggregation Aware Routing for Distributed Training;a New integer programming Model for the File Transfer Scheduling Problem;approximation Algorithms for the General Cluster Routing Problem;maximizing Group Coverage in Social Networks;lightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification;the Hybrid Navigation Method in Face of Dynamic Obstacles;a Relaxed Balanced Lock-Free Binary Search Tree;A Dynamic Parameter Tuning Method for High Performance SpMM;data Caching Based Transfer optimization in Large Scale Networks;a Novel Distributed Reinforcement Learning Method for Classical Chinese Poetry Generation;second-Order Convolutional Neural Network Based on Cholesky Compression strategy;submodular Maximization with Bounded Marginal Values;the Prize-Collecting k-steiner Tree Problem.

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Distributed Server Allocation Model with Preventive start-Time optimization against Single Failure 21

Distributed Server Allocation Model with Preventive Start-Ti...

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21st IEEE international conference on High Performance Switching and Routing (IEEE HPSR)

作者： Masuda, Shuto He, Fujun Kawabata, Akio Oki, Eiji Kyoto Univ Grad Sch Informat Kyoto Japan NTT Network Serv Syst Labs Tokyo Japan

ISBN: (纸本)9781728148465

This paper proposes a distributed server allocation model with the preventive start-time optimization against a single server failure. The proposed model preventively determines the assignment of servers to users under each failure pattern to minimize the largest maximum delay among all failure patterns. We formulate the proposed model as an integer linear programming problem. We prove the NP-completeness for the considered problem. The numerical results reveal that the proposed model reduces the largest maximum delay compared to one baseline;it avoids instability caused by the unnecessary disconnection, which frequently occurs in the other baseline.

关键词： Server allocation problem preventive start-time optimization server failure NP-complete

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optimization of Migration Cost for Network Function Virtualization Replacement 21

Optimization of Migration Cost for Network Function Virtuali...

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21st international Arab conference on Information Technology (ACIT)

作者： Shoura, Fadia Gharaibeh, Ammar Alouneh, Sahel German Jordanian Univ Dept Comp Engn Amman Jordan Al Ain Univ Coll Engn Abu Dhabi U Arab Emirates

ISBN: (纸本)9781728188553

Today's networks are concerned about making the control of communication flexible and improving the existing management systems in such a manner that reduces the Capital expenditures (CAPEX) and operating expenses (OPEX), through reducing equipment costs and energy efficiency. Along with the benefits of decreasing the time to promote new services to the clients, service providers' attention has gradually moved to Network Function Virtualization (NFV), which is a potential technology decoupling network functionalities from hardware and is a promise of high performance service provision with optimizing resource utilization across various infrastructures. However, to simultaneously achieve these goals, sometimes it is necessary to instantiate a new function depending on the traffic pattern of high-bandwidth characteristics and Quality of Service (QoS) measures. Due to the limited resources at the node, other functions in the node may need to be migrated to other nodes in order to provide resources for the new functions. Existing works related to the Virtual Network Function (VNF) deployment and migration usually focus on proposing new deployment strategies and migration mechanisms. However, reducing migration cost restricted to memory, CPU, and bandwidth capacities is not considered in those studies. In this work, the problem of virtual network functions migration is formulated as an integer Linear Program (ILP) with the objective of minimizing the migration cost while satisfying computing and network resource capacities constraints and selecting the minimum cost path from the source to the destination node. Since the ILP is NP-complete, we propose a greedy minimum migration cost (GMMC) algorithm. Simulation results show that the proposed GMMC algorithm can reduce the total migration cost by up to 61% and the number of migrations by up to 52% when compared to the state-of-the-art schemes.

关键词： integer Linear programming Migration Cost Network Function Virtualization optimization

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optimization of Urban Traffic in City Network 20

Optimization of Urban Traffic in City Network

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21st international conference on Computer Systems and Technologies, CompSystech 2020

作者： stoilova, Krasimira stoilov, Todor Institute of Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria and N. Vaptsarov Naval Academy Varna Bulgaria

ISBN: (纸本)9781450377683

The urban traffic in city areas leads to serious problems with traffic jams, air and noise pollution, social problems, etc. A solution for improving the urban traffic is proposed. A real distribution problem is formalized by mixed integer-linear programming problem. Instead of specialized software tools for optimization, open software available for any customer or manager is proposed here. The existing problem is modelled in Excel environment and solved by its application Solver. It is shown the uniqueness of modelling by its small modification leading to substantial decision making of the modified model. Both models lead to improving the traffic flows and decreasing the traffic routes according to the formalization of the tasks and received optimization solutions. © 2020 ACM.

关键词： Decision making

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A Hybrid Algorithm for the Unrelated Parallel Machine Scheduling Problem 1

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21st international conference on Enterprise Information Systems (ICEIS)

作者： Rego, Marcelo Ferreira Freitas Souza, Marcone Jamilson Univ Fed Ouro Preto UFOP Programa Posgrad Ciencia Comp BR-35400000 Ouro Preto MG Brazil Univ Fed Vales Jequitinhonha & Mucuri UFVJM BR-39100000 Diamantina Brazil

ISBN: (数字)9783030407834

ISBN: (纸本)9783030407834;9783030407827

This work proposes a hybrid algorithm for the unrelated parallel machine scheduling problem with sequence-dependent setup times, aiming to minimize the makespan. The proposed algorithm, named Enhanced Smart General Variable Neighborhood Search (e-SGVNS), combines heuristic and exact optimization strategies to explore the solution space of the problem. The exact strategy works like a local search and consists of applying a mathematical programming formulation based on the time-dependent traveling salesman problem to obtain the optimal solution to the sequencing problem on each machine. In turn, the heuristic strategy explores neighborhoods based on swap and insertion moves. The computational results, performed in benchmark instances from literature, showed that e-SGVNS is competitive when compared to state-of-the-art algorithms.

关键词： Unrelated parallel machine scheduling Makespan VNS Metaheuristic Mixed integer linear programming

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