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OptAssign-A web-based tool for assigning students to groups

COMPUTERS & EDUCATION 2009年第4期53卷 1104-1119页

作者： Meyer, David Vienna Univ Econ & Business Adm Dept Informat Syst & Operat A-1090 Vienna Austria

A frequent task in educational settings is to assign students to groups based on stated preferences for some projects or topics. This paper introduces a web-based tool supporting both the work flow of collecting student data and the group formation. The latter is based on finding optimal solutions to suitable mathematical assignment problems, allowing for a number of constraints regarding size and structure of the groups. Evaluation results show advantages compared to manual procedures in terms of time savings for lecturers, and higher fairness and correctness as perceived by students. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Group formation linear assignment problem Optimization Web-based DSS Work flow

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Zero Element Row Augmenting Path Algorithm for linear assignment problems

Zero Element Row Augmenting Path Algorithm for Linear Assign...

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第28届中国控制与决策会议

作者： Wang-bao Xu Gen-xi Rong Xue-bo Chen School of Electronics and Information Engineering Liaoning University of Science and Technology

ISBN: (纸本)9781467397155

A zero element row augmenting path algorithm is proposed for linear assignment *** the algorithm,the maximum element in an assignment y is picked firstly,where y is calculated according to the reduced matrix of a given cost matrix at the initial *** to each row's zero element record and starting from the row containing the picked element,a zero element row augmenting path is computed while the cost matrix is further reduced and the zero element record of each row is ***,y is adjusted according to the augmenting path so that the number of the zero elements in y can increase *** above process is repeated until y is an optimal *** computational experiments are carried out and indicate that the proposed algorithm is even faster than the shortest augmenting algorithm.

关键词： linear assignment problem Hungarian Method and Shortest Augmenting Path Algorithm

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BER Minimization for AF Relay-Assisted OFDM Systems

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IEEE COMMUNICATIONS LETTERS 2015年第3期19卷 495-498页

作者： Qin, Dong Wang, Yan Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Jiangsu Peoples R China

We discuss bit error rate (BER) minimization problems in multiple amplify-and-forward (AF) relay-assisted or-thogonal frequency-division multiplexing (OFDM) system over frequency-selective fading channels with total power and individual power constraints, respectively. A joint optimization method of power loading, relay selection and subcarrier pairing is solicited to solve the problems with respect to the above two power constraint conditions. Particularly, we transform subcarrier pairing into a linear assignment problem and apply Jonker-Volgenant (JV) algorithm to deal with it. A simulation result demonstrates superior performances of our algorithm to those of other schemes for different relay locations.

关键词： BER relay linear assignment problem

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linear Regression Without Correspondences via Concave Minimization

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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 1580-1584页

作者： Peng, Liangzu Tsakiris, Manolis C. ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China

linear regression without correspondences concerns the recovery of a signal in the linear regression setting, where the correspondences between the observations and the linear functionals are unknown. The associated maximum likelihood function is NP-hard to compute when the signal has dimension larger than one. To optimize this objective function we reformulate it as a concave minimization problem, which we solve via branch-and-bound. This is supported by a computable search space to branch, an effective lower bounding scheme via convex envelope minimization and a refined upper bound, all naturally arising from the concave minimization reformulation. The resulting algorithm outperforms state-of-the-art methods for fully shuffled data and remains tractable for up to 8-dimensional signals, an untouched regime in prior work.

关键词： Minimization Upper bound Signal processing algorithms linear regression linear programming Sensors Estimation linear regression without correspondences unlabeled sensing homomorphic sensing concave minimization branch-and-bound linear assignment problem

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A Path-following Algorithm for Robust Point Matching

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IEEE SIGNAL PROCESSING LETTERS 2016年第1期23卷 89-93页

作者： Lian, Wei Changzhi Univ Dept Comp Sci Changzhi 046031 Shanxi Peoples R China

We propose a new point matching algorithm in this letter by minimizing a concave geometric matching cost function coming from the objective function of the robust point matching algorithm. Due to concavity of this function, naive optimization strategies such as gradient descent will fail. To address this problem, we use a path following strategy for optimization which works by adding a convex quadratic term to the objective function and then gradually transitioning from the state that there is only weight of the convex term to the state that there is only weight of the concave geometric matching cost term. Extensive experimental results demonstrate strong robustness of the method over several state-of-the-art methods and it also has good computational efficiency.

关键词： Combinatorial optimization deterministic annealing linear assignment problem path following point matching

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Random assignment problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2009年第1期194卷 1-17页

作者： Krokhmal, Pavlo A. Pardalos, Panos M. Univ Iowa Dept Mech & Ind Engn Iowa City IA 52242 USA Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA

Analysis of random instances of optimization problems provides valuable insights into the behavior and properties of problem's solutions, feasible region, and optimal values, especially in large-scale cases. A class of problems that have been studied extensively in the literature using the methods of probabilistic analysis is represented by the assignment problems, and many important problems in operations research and computer science can be formulated as assignment problems. This paper presents an overview of the recent results and developments in the area of probabilistic assignment problems, including the linear and multidimensional assignment problems, quadratic assignment problem, etc. (c) 2007 Elsevier B.V. All rights reserved.

关键词： Random assignment problems linear assignment problem Quadratic assignment problem Multidimensional assignment problem Bottleneck assignment problem Probabilistic analysis Asymptotic analysis Fitness landscape analysis

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A novel multitarget tracking algorithm for Myosin VI protein molecules on actin filaments in TIRFM sequences

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JOURNAL OF MICROSCOPY 2015年第3期260卷 312-325页

作者： Li, G. Sanchez, V. Nagaraj, P. C. S. B. Khan, S. Rajpoot, N. Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England LUMS Dept Biol Lahore Pakistan

We propose a novel multitarget tracking framework for Myosin VI protein molecules in total internal reflection fluorescence microscopy sequences which integrates an extended Hungarian algorithm with an interacting multiple model filter. The extended Hungarian algorithm, which is a linear assignment problem based method, helps to solve measurement assignment and spot association problems commonly encountered when dealing with multiple targets, although a two-motion model interacting multiple model filter increases the tracking accuracy by modelling the nonlinear dynamics of Myosin VI protein molecules on actin filaments. The evaluation of our tracking framework is conducted on both real and synthetic total internal reflection fluorescence microscopy sequences. The results show that the framework achieves higher tracking accuracies compared to the state-of-the-art tracking methods, especially for sequences with high spot density.

关键词： IMM filter Kalman filter linear assignment problem motion model multitarget tracking

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Comparison and Bayesian Estimation of Feature Allocations

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2023年第4期32卷 1371-1382页

作者： Dahl, David B. Johnson, Devin J. Andros, R. Jacob Brigham Young Univ Dept Stat Provo UT 84602 USA Duke Univ Dept Stat Sci Durham NC USA

Feature allocation models postulate a sampling distribution whose parameters are derived from shared features. Bayesian models place a prior distribution on the feature allocation, and Markov chain Monte Carlo is typically used for model fitting, which results in thousands of feature allocations sampled from the posterior distribution. Based on these samples, we propose a method to provide a point estimate of a latent feature allocation. First, we introduce FARO loss, a function between feature allocations which satisfies quasi-metric properties and allows for comparing feature allocations with differing numbers of features. The loss involves finding the optimal feature ordering among all possible orderings, but computational feasibility is achieved by framing this task as a linear assignment problem. We also introduce the FANGS algorithm to obtain a Bayes estimate by minimizing the Monte Carlo estimate of the posterior expected FARO loss using the available samples. FANGS can produce an estimate other than those visited in the Markov chain. We provide an investigation of existing methods and our proposed methods. Our loss function and search algorithm are implemented in the fangs package in R.

关键词： Bayesian nonparametrics Hamming distance Indian buffet process linear assignment problem

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Distribution functions, external limits and optimal transport

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INDAGATIONES MATHEMATICAE-NEW SERIES 2015年第5期26卷 823-841页

作者： Iaco, M. R. Thonhauser, S. Tichy, R. F. Graz Univ Technol Inst Anal & Computat Number Theory Math A A-8010 Graz Austria

Encouraged by the study of extremal limits for sums of the form lim(N ->) (infinity) 1/N Sigma(N)(n=1) c(x(n), y(n)) with uniformly distributed sequences {x(n)}, {y(n)} the following extremal problem is of interest max(gamma) integral([0,1]2) c(x, y) gamma (dx, dy), for probability measures gamma on the unit square with uniform marginals, i.e., measures whose distribution function is a copula. The aim of this article is to relate this problem to combinatorial optimization and to the theory of optimal transport. Using different characterizations of maximizing gamma's one can give alternative proofs of some results from the field of uniform distribution theory and beyond that treat additional questions. Finally, some applications to mathematical finance are addressed. (C) 2015 Royal Dutch Mathematical Society (KWG). Published by Elsevier B.V. All rights reserved.

关键词： Distribution functions Optimal transport linear assignment problem Copulas

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A One-Layer Dual Recurrent Neural Network with a Heaviside Step Activation Function for linear Programming with Its linear assignment Application

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21st International Conference on Artificial Neural Networks, ICANN 2011

作者： Liu, Qingshan Wang, Jun Southeast Univ Sch Automat Nanjing 210096 Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Sha Tin Hong Kong Peoples R China

ISBN: (纸本)9783642217371;9783642217388

This paper presents a one-layer recurrent neural network for solving linear programming problems. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter. The number of neurons in the neural network is the same as the number of decision variables of the dual optimization problem. Compared with the existing neural networks for linear programming, the proposed neural network has salient features such as finite-time convergence and lower model complexity. Specifically, the proposed neural network is tailored for solving the linear assignment problem with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

关键词： Recurrent neural networks linear programming global convergence in finite time linear assignment problem

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