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16th Annual Wireless Telecommunications Symposium (WTS) - Global Wireless Communications - Present and Future

作者： Gu, Changpeng Su, Xin Zeng, Jie Ge, Lu Xiao, Chiyang Chongqing Univ Posts & Telecommun Broadband Wireless Access Lab Chongqing Peoples R China Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol Res Inst Informat Technol Beijing Peoples R China

ISBN: (纸本)9781509035991

Network slicing has been proposed as an innovative concept to conduct a dedicated and customized logical network with virtualized and physical resources, which help operators to launch and customize service with more flexibility. We analyse the relationship between the network slicing and physical network and define a mathematical model to construct logical network function graphs that can be mapped to the physical network. Further, we focus on demand oriented deployment of Network slicing applied to various application scenarios and formulate it as the integer quadratic programming for the placement of the network functions and connecting them together considering the limited network resources and QoS provision. We mainly discuss three kinds of network slicing deployment policy that highly influence resource cost of physical network. We evaluate resource utilization of network slicing model using a small substrate network scenario and analyze its deployment rule according to a combination of multiple optimization objectives and constraints implemented in YALMIP with CPLEX tools.

关键词： Network Slicing VNF Deployment Policy integer quadratic programming

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Maximizing Signal to Interference Noise Ratio for Massive MIMO: A Mathematical programming Approach 2

Maximizing Signal to Interference Noise Ratio for Massive MI...

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2nd South American Colloquium on Visible Light Communications (IEEE SACVLC)

作者： Adasme, Pablo Soto, Ismael San Juan, Enrique Seguel, Fabian Firoozabadi, Ali Dehghan Univ Santiago de Chile Dept Elect Engn Estn Cent Ave Ecuador 3519 Santiago Chile Univ Tecnol Metropolitana Dept Elect Av Jose Pedro Alessandri 1242 Santiago Chile

ISBN: (纸本)9781728163413

In this paper, we consider the problem of maximizing the worst user signal to interference noise ratio (SINR) for Massive Multiple Input Multiple Output (MaMIMO) systems subject to antenna assignment and multiuser interference constraints. In particular, we aim to choose a subset of antennas from a larger set while imposing a maximum interference value allowed in the system. Notice that MaMIMO technology has recently been considered as a strong candidate for 5G wireless communications by the research community as it provides better performance in terms of data rate. It also allows to transmit in higher frequency bands with strong signal performance and reliability. In order to propose new optimization models for this problem, we model SINR by using Manhattan, Euclidean and Infinity distance norms. Thus, we obtain integer quadratic and linear programming models that allow to obtain optimal solutions for the problem. As such, the proposed models can be used as a source of comparison for any exact or approximation method to be developed as part of future research. Finally, we propose a local search algorithm that allows to obtain feasible solutions in short CPU time. Our preliminary numerical results indicate that the linear models are preferable and that the local search approach obtains better solutions than the quadratic model for some of the tested instances in significantly less CPU time.

关键词： integer quadratic programming Mixed integer linear programming Resource allocation of Massive MIMO systems Manhattan Euclidean and Infinity distance norms

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L1 Norm Minimization approach to MIMO detector 8

L1 Norm Minimization approach to MIMO detector

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8th International Conference on Signal Processing and Communication Systems, (ICSPCS)

作者： Hashimoto, Yo Konishi, Katsumi Takahashi, Tomohiro Uruma, Kazunori Furukawa, Toshihiro Tokyo Univ Sci Tokyo 162 Japan Kogakuin Univ Tokyo 160 Japan

ISBN: (纸本)9781479952557

This paper deals with the quadrature amplitude modulation (QAM) problem for the multiple-input multiple-output (MIMO) channel. Based on the maximum likelihood estimation, the QAM detection problem is formulated as an integer quadratic programming, which is a combinatorial problem and difficult to obtain exact solutions. In order to overcome combinatorial difficulties, this paper formulates the QAM detection problem as the l(o) norm minimization problem and relaxes it into a quadratic programming with the l(1) norm regularization. Utilizing and modifying the forward-backward splitting (FOBOS) algorithm, a new QAM detection algorithm is proposed. This algorithm has a trade-off between the computational cost and the detection accuracy, which depends on a parameter of the algorithm. Numerical simulations show that the proposed algorithm works well and achieves a good detection performance with less computational cost comparing with the semidefinite relaxation (SDR) based algorithm.

关键词： MIMO communication integer programming minimisation numerical analysis quadratic programming quadrature amplitude modulation FOBOS algorithm L1 norm minimization approach MIMO detector QAM detection algorithm SDR based algorithm combinatorial problem computational cost detection accuracy forward-backward splitting algorithm integer quadratic programming maximum likelihood estimation multiple-input multiple-output channel numerical simulations semidefinite relaxation based algorithm Approximation algorithms Detectors MIMO Quadrature amplitude modulation Signal processing algorithms Signal to noise ratio Vectors Quadrature amplitude modulation quadratic programming multiple input multiple output systems maximum likelihood estimation Signal processing algorithms signal to noise ratios MIMO communication Approximation algorithms integer programming Numerical Analysis

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Mixed Open-and-Closed Loop Satellite Task Planning 22

Mixed Open-and-Closed Loop Satellite Task Planning

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22nd International Conference on Information Fusion (FUSION)

作者： Tharmarasa, Ratnasingham Kirubarajan, Thia Berger, Jean Florea, Mihai Cristian TrackGen Solut Inc Mississauga ON Canada Valcartier Res Ctr Def Res & Dev Canada Quebec City PQ Canada Thales Res & Technol Thales Grp Quebec City PQ Canada

ISBN: (纸本)9780996452786

A key problem in earth observation using satellite-based sensors is to allocate the limited resources to achieve optimal performance. In the literature, several algorithms are proposed to handle this problem as an open-loop problem. Using a closed-loop formulation over an open-loop formulation would provide a better solution if the closed-loop solution could be implemented in real-time. However, an optimal closed-loop formulation may not be able to handle a medium or a large scale problem in real-time. In this work, we have proposed a mixed open-and-closed loop algorithm to handle the problem in real-time by taking the advantages of open- and closed-loop formulations. Simulation results illustrating the performance of the proposed algorithm are also presented.

关键词： Multi-satellite scheduling open-loop closed-loop Markov-decision process integer quadratic programming

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Portfolio Selection under Piecewise Affine Transaction Costs: An integer quadratic Formulation

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2nd International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences

作者： Lemrabott, Mohamed Gueye, Serigne Yassine, Adnan Rakotondratsimba, Yves Lab Math Appl Havre 25 Rue Philippe Lebon F-76600 Le Havre France ECE F-75007 Paris France

ISBN: (纸本)9783540874768

In this paper we consider the problem of selecting assets for which transaction costs are given by piecewise affine functions. Given practical constraints related to budget and buy-in thresholds, our purpose is to determine the number of each asset i that can produce the maximum return of a portfolio composed of (n + 1) assets (one of them is free of risk). The problem is formulated as an integer quadratic problem and afterwards linearized. some numerical experiments, using Ilog Cplex 10.1, has been performed. They show that the methodology is promising.

关键词： Piecewise transaction costs integer quadratic programming portfolio selection Linearization

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Robust Feature Point Matching With Sparse Model

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2014年第12期23卷 5175-5186页

作者： Jiang, Bo Tang, Jin Luo, Bin Lin, Liang Anhui Univ Sch Comp Sci & Technol Hefei 230026 Peoples R China Sun Yat Sen Univ Sch Adv Comp Guangzhou 510275 Guangdong Peoples R China

Feature point matching that incorporates pairwise constraints can be cast as an integer quadratic programming (IQP) problem. Since it is NP-hard, approximate methods are required. The optimal solution for IQP matching problem is discrete, binary, and thus sparse in nature. This motivates us to use sparse model for feature point matching problem. The main advantage of the proposed sparse feature point matching (SPM) method is that it generates sparse solution and thus naturally imposes the discrete mapping constraints approximately in the optimization process. Therefore, it can optimize the IQP matching problem in an approximate discrete domain. In addition, an efficient algorithm can be derived to solve SPM problem. Promising experimental results on both synthetic points sets matching and real-world image feature sets matching tasks show the effectiveness of the proposed feature point matching method.

关键词： Feature point matching integer quadratic programming nonnegative matrix factorization sparse model

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A decision space algorithm for multiobjective convex quadratic integer optimization

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COMPUTERS & OPERATIONS RESEARCH 2021年 134卷 105396-105396页

作者： De Santis, Marianna Eichfelder, Gabriele Sapienza Univ Roma Dipartimento Ingn Informat Automat & Gest Via Ariosto 25 I-00185 Rome Italy Tech Univ Ilmenau Inst Math Po 10 05 65 D-98684 Ilmenau Germany

We present a branch-and-bound algorithm for minimizing multiple convex quadratic objective functions over integer variables. Our method looks for efficient points by fixing subsets of variables to integer values and by using lower bounds in the form of hyperplanes in the image space derived from the continuous relaxations of the restricted objective functions. We show that the algorithm stops after finitely many fixings of variables with detecting both the full efficient and the nondominated set of multiobjective strictly convex quadratic integer problems. A major advantage of the approach is that the expensive calculations are done in a preprocessing phase so that the nodes in the branch-and-bound tree can be enumerated fast. We show numerical experiments on biobjective instances and on instances with three and four objectives.

关键词： Multiobjective optimization Convex quadratic optimization integer quadratic programming Branch-and-bound algorithm

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CONSTRUCTION OF TEST PROBLEMS IN quadratic BIVALENT programming

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ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE 1991年第1期17卷 74-87页

作者： PARDALOS, PM Pennsylvania State Univ. University Park

A method of constructing test problems for constrained bivalent quadratic programming is presented. For any feasible integer point of a given domain, the method generates quadratic functions whose minimum over the given domain occurs at the selected point. Certain properties of unconstrained quadratic zero-one programs that determine the difficulty of the test problems are also discussed. In addition, a standardized random test problem generator for unconstrained quadratic zero-one programming is given.

关键词： GLOBAL OPTIMIZATION integer quadratic programming TEST PROBLEMS

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An integer programming Based Approach to Delivery Drone Routing under Load-Dependent Flight Speed

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DRONES 2023年第5期7卷 320-320页

作者： Nishira, Mao Ito, Satoshi Nishikawa, Hiroki Kong, Xiangbo Tomiyama, Hiroyuki Ritsumeikan Univ Grad Sch Sci & Engn Kusatsu Shiga 5258577 Japan Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka 5650871 Japan Toyama Prefectural Univ Fac Engn Dept Intelligent Robot Imizu Toyama 9390398 Japan

Delivery drones have been attracting attention as a means of solving recent logistics issues, and many companies are focusing on their practical applications. Many research studies on delivery drones have been active for several decades. Among them, extended routing problems for drones have been proposed based on the Traveling Salesman Problem (TSP), which is used, for example, in truck vehicle routing problems. In parcel delivery by drones, additional constraints such as battery capacity, payload, and weather conditions need to be considered. This study addresses the routing problem for delivery drones. Most existing studies assume that the drone's flight speed is constant regardless of the load. On the other hand, some studies assume that the flight speed varies with the load. This routing problem is called the Flight Speed-Aware Traveling Salesman Problem (FSTSP). The complexity of the drone flight speed function in this problem makes it difficult to solve the routing problem using general-purpose mathematical optimization solvers. In this study, the routing problem is reduced to an integer programming problem by using linear and quadratic approximations of the flight speed function. This enables us to solve the problem using general-purpose mathematical optimization solvers. In experiments, we compared the existing and proposed methods in terms of solving time and total flight time. The experimental results show that the proposed method with multiple threads has a shorter solving time than the state-of-the-art method when the number of customers is 17 or more. In terms of total flight time, the proposed methods deteriorate by an average of 0.4% for integer quadratic programming and an average of 1.9% for integer cubic programming compared to state-of-the-art methods. These experimental results show that the quadratic and cubic approximations of the problem have almost no degradation of the solution.

关键词： delivery drones traveling salesman problem flight speed-aware traveling salesman problem integer quadratic programming integer cubic programming

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Per-Instance Algorithm Configuration in Homogeneous Instance Spaces: A Use Case in Reconfigurable Assembly Systems

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APPLIED SCIENCES-BASEL 2024年第14期14卷 6035页

作者： Vargas, Daniel Guzman Gautama, Sidharta Uzunosmanoglu, Mehmet Raa, Birger Limere, Veronique Univ Ghent Dept Ind Syst Engn & Prod Design B-9000 Ghent Belgium Flanders MakeUGent ISyE CoreLab B-3920 Lommel Belgium Flanders MakeUGent CVAMO CoreLab B-3920 Lommel Belgium Univ Ghent Dept Business Informat & Operat Management B-9000 Ghent Belgium

The physical capabilities of a reconfigurable assembly system (RAS) increase the agility and responsiveness of the system in highly volatile market conditions. However, achieving optimal RAS utilization entails solving complex optimization problems effectively and efficiently. These optimizations often define homogenous sets of problem instances. While algorithm configuration in such homogeneous contexts traditionally adopts a "one-size-fits-all" approach, recent studies have shown the potential of per-instance algorithm configuration (PIAC) methods in these settings. In this work, we evaluate and compare the performance of different PIAC methods in this context, namely Hydra-a state-of-the-art PIAC method-and a simpler case-based reasoning (CBR) approach. We evaluate the impact of the tuning time budget and/or the number of unique problem instances used for training on each of the method's performance and robustness. Our experiments show that whilst Hydra fails to improve upon the default algorithm configuration, the CBR method can lead to 16% performance increase using as few as 100 training instances. Following these findings, we evaluate Hydra's methodology when applied to homogenous instance spaces. This analysis shows the limitations of Hydra's inference mechanisms in these settings and showcases the advantages of distance-based approaches used in CBR.

关键词： per-instance algorithm configuration reconfigurable assembly systems homogeneous instance spaces integer quadratic programming

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