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Efficient Feature Matching via Nonnegative Orthogonal Relaxation

INTERNATIONAL JOURNAL OF COMPUTER VISION 2019年第9期127卷 1345-1360页

作者： Jiang, Bo Tang, Jin Luo, Bin Anhui Univ Sch Comp Sci & Technol 111 Jiulong Rd Hefei Anhui Peoples R China

Feature matching problem that incorporates pair-wise constraints can be formulated as an integer quadratic programming (IQP) problem with one-to-one matching constraint. Since it is NP-hard, relaxation models are required. One main challenge for optimizing IQP matching is how to incorporate the discrete one-to-one matching constraint in IQP matching optimization. In this paper, we present a new feature matching relaxation model, called Nonnegative Orthogonal Relaxation (NOR), that aims to optimize IQP matching problem in nonnegative orthogonal domain. One important benefit of the proposed NOR model is that it can naturally incorporate the discrete one-to-one matching constraint in its optimization and can return a desired sparse (approximate discrete) solution for the problem. An efficient and effective update algorithm has been developed to solve the proposed NOR model. Promising experimental results on several benchmark datasets demonstrate the effectiveness and efficiency of the proposed NOR method.

关键词： Feature matching Nonnegative orthogonal constraint Multiplicative update integer quadratic programming

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Detection of Code Spread OFDM based on 0–1 integer quadratic programming

Detection of Code Spread OFDM based on 0–1 Integer Quadrati...

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Asilomar Conference on Signals, Systems & Computers

作者： Ali Elghariani Michael D. Zoltowski School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (纸本)9781467350501

In this paper we introduce integer quadratic programming (IQP) approach to detect QPSK Code Spread OFDM (CSOFDM) signal by formulating the problem as a combinatorial optimization problem. The Branch and Bound (BB) algorithm is utilized to solve this integer quadratic programming problem. Furthermore, we propose preprocessing procedures that can be applied prior to BB so that the computational complexity of the optimum receiver is reduced. The basic idea of the proposed preprocessing is to introduce a good choice of upper bound for the BB search tree and/or reduce the search space of the BB problem. Performance and complexity computations of these preprocessing are investigated. Simulation results show that the proposed preprocessing when applied prior to BB provides optimal or near optimal performance with a significantly reduced computational complexity.

关键词： CSOFDM integer quadratic programming Branch and Bound MMSE LLR Computational complexity quadratic programming complexity classes Mini Mental State Examination optimum receiver integer Orthogonal frequency division multiplexing Pretreatment optimum performance

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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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Maximizing heterogeneous coverage in over and under provisioned visual sensor networks

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2018年第Dec.期124卷 44-62页

作者： Al Zishan, Abdullah Karim, Imtiaz Shubha, Sudipta Saha Rahman, Ashikur Bangladesh Univ Engn & Technol Dept Comp Sci & Engn CSE Dhaka Bangladesh

We address "heterogeneous coverage" in visual sensor networks where coverage requirements of some randomly deployed targets vary from target to target. The main objective is to maximize the coverage of all the targets to achieve their respective coverage requirement by activating minimal sensors. The problem can be viewed as an interesting variation of the classical Max-Min problem (i.e., Maximum Coverage with Minimum Sensors (MCMS)). Therefore, we study the existing integer Linear programming (ILP) formulation for single and k-coverage MCMS problem in the state-of-the-art and modify them to solve the heterogeneous coverage problem. We also propose a novel integer quadratic programming (IQP) formulation that minimizes the Euclidean distance between the achieved and the required coverage vectors. Both ILP and IQP give exact solution when the problem is solvable but as they are non-scalable due to their computational complexity, we devise a Sensor Oriented Greedy Algorithm (SOGA) that approximates the formulations. For under-provisioned networks where there exist insufficient number of sensors to meet the coverage requirements, we propose prioritized IQP and reduced-variance IQP formulations to capture prioritized and group wise balanced coverage respectively. Moreover, we develop greedy heuristics to tackle under provisioned networks. Extensive evaluations based on simulation illustrate the efficiency and efficacy of the proposed formulations and heuristics under various network settings. Additionally, we compare our methodologies and algorithm with two state-of-the-art algorithms available for target coverage and show that our methodologies and algorithm substantially outperform both the algorithms.

关键词： Directional sensors Heterogeneous coverage integer linear programming integer quadratic programming Visual sensor network

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Energy efficient temporal load aware resource allocation in cloud computing datacenters

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2018年第1期7卷 1-24页

作者： Vakilinia, Shahin ETS Synchromedia Lab 500 Rue Jean Estress Montreal PQ H3C6W1 Canada

Cloud computing datacenters consume huge amounts of energy, which has high cost and large environmental impact. There has been significant amount of research on dynamic power management, which shuts down unutilized equipment in a datacenter to reduce energy consumption. The main consumers of power in a datacenter are servers, communications network and the cooling system. Optimization of power in a datacenter is a difficult problem because of server resource constraints, network topology and bandwidth constraints, cost of VM migration, the heterogeneity of workloads and the servers. The arrival of new jobs and departure of completed jobs also create workload heterogeneity in time. As a result, most of the previous research has concentrated on partial optimization of power consumption, which optimizes either server and/or network power consumption through placement of VMs. Temporal load aware optimization, minimization of power consumption as a function of time has vastly been studied. When optimization also included migration, then solution had been divided into two steps, in the first step optimization of server and/or network power consumption is performed and in the second step migration of VMs has been taken care of, which is not an optimal solution. In this work, we develop joint optimization of power consumption of servers, network communications and cost of migration with workload and server heterogeneity subject to resource and bandwidth constraints through VM placement. Optimization results in an integer quadratic program (IQP) with linear/quadratic constraints in number of VMs assigned to a job on a server. IQP can only be solved for very small size systems, however, we have been able to decompose IQP to master and pricing sub-problems which may be solved through column generation technique for systems with larger sizes. Then, we have extended the optimization to manage temporal heterogeneity of the workload. It is assumed that time-axis is slotted and at

关键词： Cloud computing Virtual machine placement integer linear programming integer quadratic programming Optimization Resource allocation Column generation Datacenter power management

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Minimizing the tracking error of cardinality constrained portfolios

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COMPUTERS & OPERATIONS RESEARCH 2018年第Feb.期90卷 33-41页

作者： Mutunge, Purity Haugland, Dag Univ Bergen Dept Informat Bergen Norway

We study the problem of selecting a restricted number of shares included in a stock market index, such that the portfolio resembles the index as closely as possible. To measure the difference between the portfolio and the index, referred to as the tracking error, we use a quadratic function with the covariance matrix of the index returns as coefficient matrix. The problem is proved to be strongly NP-hard, and we give theoretical evidence that continuous relaxations of mixed integer quadratic programming (MIQP) formulations are likely to produce poor lower bounds on the tracking error. For fast computation of near-optimal portfolios, we demonstrate how the best-extension-by-one construction heuristic can be designed to run in time bounded by a fourth order polynomial. We also show that the running time of one iteration of the best-exchange-by one improvement heuristic is of the same order. Computational experiments applied to real-life stock market indices show that in instances where an index of less than 500 assets is to be tracked by a portfolio of 10 assets, a commercially available MIQP solver fails to reduce the integrality gap below 94% in 30 CPU-minutes. In contrast, the construction heuristic under study needs less than 30 CPU-seconds to produce a portfolio of 100 assets tracking an index of nearly 2000 assets. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Portfolio management Index tracking integer quadratic programming Heuristics

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Utilizing soft constraints to enhance medical relation extraction from the history of present illness in electronic medical records

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JOURNAL OF BIOMEDICAL INFORMATICS 2018年 87卷 108-117页

作者： Chen, Li Li, Yuanju Chen, Weipeng Liu, Xinglong Yu, Zhonghua Zhang, Siyuan Sichuan Univ Dept Comp Sci Chengdu Sichuan Peoples R China Chengdu Univ TCM Sch Basic Med Chengdu Sichuan Peoples R China

Relation extraction between medical concepts from electronic medical records has pervasive applications as well as significance. However, previous researches utilizing machine learning algorithms judge the semantic types of medical concept pair mentions independently. In fact, different concept pair mentions in the same context are of dependencies which can provide beneficial evidences for identifying their relation types. To the best of our knowledge, only one study has considered such dependencies in discharge summaries. However, its hard constraints are not applied effectively to the History of Present Illness (HPI) in electronic Medical Records. According to the writing characteristics of HPI records, we generalize two regularities of dependencies among concept pairs mentioned in an HPI record to enhance the performance of relation extraction. We incorporate the two soft constraints corresponding to the regularities and the posterior probabilities returned by a local classifier into a joint inference process which applies integer quadratic programming method to carry out collective classification for all concept pair mentions in an HPI record. We implement four local classification models including support vector machine, logistics regression, random forest and piecewise convolutional neural networks to examine the performance of our approach. A series of experimental results demonstrate that our collective classification method has made a principal improvement and outperforms the other state-of-the-art methods.

关键词： Electronic medical record History of present illness Relation extraction integer quadratic programming Soft constraints

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Lagrangian relaxation graph matching

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PATTERN RECOGNITION 2017年第0期61卷 255-265页

作者： Jiang, Bo Tang, Jin Cao, Xiaochun Luo, Bin Anhui Univ Sch Comp Sci & Technol 111 Jiu Long Rd Hefei 230601 Anhui Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing Peoples R China

Graph matching is a fundamental problem in computer vision area. Graph matching problem that incorporates pair-wise constraints can be formulated as an integer quadratic programming (IQP) problem with affine mapping constraint. Since it is known to be NP-hard, approximate relaxation methods are usually required to find approximate solutions. In this paper, we present a new effective graph matching relaxation method, called Lagrangian relaxation graph matching (LRGM), which aims to generate a relaxation model by incorporating the affine mapping constraint into the matching objective optimization. There are three main benefits of the proposed LRGM method: (1) The nonnegative affine mapping constraint encoding one-to-one mapping is naturally incorporated in LRGM relaxation via Lagrangian regularization. (2) By further adding a l(1)-norm constraint, LRGM can generate a sparse solution empirically and thus returns a desired discrete solution for original IQP matching problem. (3) An effective update algorithm is derived to solve the proposed LRGM model. Theoretically, the converged solution can be proven to be Karush-Kuhn-Tucker (KKT) optimal. Experimental results on both synthetic data and real-world image datasets show the effectiveness and benefits of the proposed LRGM method. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Graph matching integer quadratic programming Lagrangian relaxation Multiplicative update

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Orchestrating for Deployment of Network Slicing 16

Orchestrating for Deployment of Network Slicing

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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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Reducing the number of variables in integer quadratic programming problem

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APPLIED MATHEMATICAL MODELLING 2010年第2期34卷 424-436页

作者： Zhou MinJin Chen Wei Shanghai Univ Dept Math Shanghai Peoples R China

In this paper. a new variable reduction technique is presented for general integer quadratic programming problem (GP), under which some variables of (GP) can be fixed at zero without sacrificing optimality. A sufficient condition and a necessary condition for the identification of dominated terms are provided. By comparing the given data of the problem and the upper bound of the variables, if they meet certain conditions, some variables can be fixed at zero. We report a computational study to demonstrate the efficacy of the proposed technique in solving general integer quadratic programming problems. Furthermore, we discuss separable integer quadratic programming problems in a simpler and clearer form. (c) 2009 Elsevier Inc. All rights reserved.

关键词： integer quadratic programming Variable reduction Separable integer quadratic programming

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