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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Luis Olmos Adam Kaplan Maryam Jalalitabar Department of Computer Science California State University Northridge Northridge USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Integrating BERT-augmented Graph Neural Networks (GNNs) with integer linear programming (ILP) provides a novel framework for academic course recommendation and scheduling. Our approach enriches node representations with BERT embeddings to capture the semantic context of course descriptions. These representations are further refined using supervised contrastive loss within the GNN to enhance the understanding of course relationships and dependencies, leading to more accurate and context-aware recommendations. The framework incorporates an ILP scheduler that optimizes course sequences under academic constraints, including prerequisites, elective requirements, major-specific pathways, and semester unit limits. This method supports the creation of personalized multi-semester schedules tailored to students' academic goals, such as double majors or major-minor combinations. Evaluated on synthetic data, the approach demonstrates improved alignment with academic pathways, showcasing its potential to bridge the gap between semantic understanding and effective course planning.

关键词： Schedules Accuracy Semantics Machine learning Contrastive learning integer linear programming Graph neural networks Optimization Synthetic data Testing

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Mapping Spiking Neural Networks to Heterogeneous Crossbar Architectures using integer linear programming

Mapping Spiking Neural Networks to Heterogeneous Crossbar Ar...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Devin Pohl Aaron Young Kazi Asifuzzaman Narasinga Rao Miniskar Jeffrey S. Vetter Georgia Institute of Technology Oak Ridge National Lab

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Advances in novel hardware devices and architectures allow Spiking Neural Network (SNN) evaluation using ultra-low power, mixed-signal, memristor crossbar arrays. As individual network sizes quickly scale beyond the dimensional capabilities of single crossbars, networks must be mapped onto multiple crossbars. Crossbar sizes within modern Memristor Crossbar Architectures (MCAs) are determined predominately not by device technology but by network topology; more, smaller crossbars consume less area thanks to the high structural sparsity found in larger, brain-inspired SNNs. Motivated by continuing increases in SNN sparsity due to improvements in training methods, we propose utilizing heterogeneous crossbar sizes to further reduce area consumption. This approach was previously unachievable as prior compiler studies only explored solutions targeting homogeneous MCAs. Our work improves on the state-of-the-art by providing integer linear programming (ILP) formulations supporting arbitrarily heterogeneous architectures. By modeling axonal interactions between neurons, our methods produce better mappings while removing inhibitive a priori knowledge requirements. We first show a 16.7-27.6% reduction in area consumption for square-crossbar homogeneous architectures. Then, we demonstrate 66.9-72.7% further reduction when using a reasonable configuration of heterogeneous crossbar dimensions. Next, we present a new optimization formulation capable of minimizing the number of inter-crossbar routes. When applied to solutions already near-optimal in area, an 11.9-26.4% routing reduction is observed without impacting area consumption. Finally, we present a profile-guided optimization capable of minimizing the number of runtime spikes between crossbars. Compared to the best-area-then-route optimized solutions, we observe a further 0.5-14.8% inter-crossbar spike reduction while requiring 1–3 orders of magnitude less solver time.

关键词： Training Runtime Neuromorphics Neurons Memristors Spiking neural networks integer linear programming Routing Hardware Optimization

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integer linear programming formulations for double roman domination problem

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OPTIMIZATION METHODS & SOFTWARE 2022年第1期37卷 1-22页

作者： Cai, Qingqiong Fan, Neng Shi, Yongtang Yao, Shunyu Nankai Univ Coll Comp Sci Tianjin Peoples R China Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA Nankai Univ Ctr Combinator Tianjin Peoples R China Nankai Univ LPMC Tianjin Peoples R China

For a graph , a double Roman dominating function (DRDF) is a function having the property that if , then vertex v must have at least two neighbours assigned 2 under f or at least one neighbour u with , and if , then vertex v must have at least one neighbour u with . In this paper, we consider the double Roman domination problem, which is an optimization problem of finding the DRDF f such that is minimum. We propose five integer linear programming (ILP) formulations and one mixed integer linear programming formulation with polynomial number of constraints for this problem. Some additional valid inequalities and bounds are also proposed for some of these formulations. Further, we prove that the first four models indeed solve the double Roman domination problem, and the last two models are equivalent to the others regardless of the variable relaxation or usage of a smaller number of constraints and variables. Additionally, we use one ILP formulation to give an -approximation algorithm. All proposed formulations and approximation algorithm are evaluated on randomly generated graphs to compare the performance.

关键词： Double roman domination integer linear programming approximation algorithm 90C10 68W25

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integer linear programming optimization of joint RRM policies for heterogeneous wireless systems

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COMPUTER NETWORKS 2012年第1期56卷 112-126页

作者： Lucas-Estan, M. C. Gozalvez, J. Sanchez-Soriano, J. Univ Miguel Hernandez Elche Ubiquitous Wireless Commun Res Uwicore Lab Elche 03202 Spain Univ Miguel Hernandez Elche Operat Res Ctr CIO Elche 03202 Spain

Wireless systems will be characterized by the coexistence of heterogeneous Radio Access Technologies (RATs) with different, but also complementary, performance and technical characteristics. These heterogeneous wireless networks will provide network operators the possibility to efficiently and coordinately use the heterogeneous radio resources, for which novel Joint Radio Resource Management (.JRRM) policies need to be designed. In this context, this work proposes and evaluates a JRRM policy that simultaneously determines for each user an adequate combination of RAT and number of radio resources within such RAT to guarantee the user/service QoS requirements, and efficiently distribute the radio resources considering a user fairness approach aimed at maximizing the system capacity. To this aim, the JRRM algorithm, which takes into account the discrete nature of radio resources, is based on integer linear programming optimization mechanisms. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Joint Radio Resource Management Heterogeneous wireless systems integer linear programming Multimedia

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integer linear programming as a tool for constructing trees from quartet data

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2005年第3期29卷 196-203页

作者： Weyer-Menkhoff, J Devauchelle, C Grossmann, A Grünewald, S Univ Gottingen Fak Biol Inst Mikrobiol & Genet Abt Bioinformat D-37073 Gottingen Germany Genopole Evry LGI F-91000 Evry France Univ Canterbury Allan Wilson Ctr Mol Ecol & Evolut Dept Math & Stat Christchurch 1 New Zealand

The task of the quartet puzzling problem is to find a best-fitting binary X-tree for a finite n-set from confidence values for the 3 ((n)(4)) binary trees with exactly four leaves from X, its fitness being measured by the sum of the confidence values of all "induced" four-leaves subtrees. We describe a method for finding an exact solution of this problem by integer linear programming. Similar procedures can also be used for finding, e.g. best-fitting "circular" networks. A crucial problem in this context is, of course, how to obtain the input confidence values for the quartet trees. We propose to use inner products of rate-matrix diagonals calculated for pairs of taxa and present the trees resulting from applying our approach to two data sets of up to 36 mitochondrial sequences of mammals including an outgroup. (c) 2004 Elsevier Ltd. All rights reserved.

关键词： Weighted quartet integer linear programming Observed rate matrix Mammals’ mitochondrial evolution Phylogeny

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integer linear programming-Based Property Checking for Asynchronous Reactive Systems

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2013年第2期39卷 216-236页

作者： Leue, Stefan Wei, Wei Univ Konstanz Dept Comp & Informat Sci Constance Germany Darmstadt SAP AG SAP Res Ctr Darmstadt Germany

Asynchronous reactive systems form the basis of a wide range of software systems, for instance in the telecommunications domain. It is highly desirable to rigorously show that these systems are correctly designed. However, traditional formal approaches to the verification of these systems are often difficult because asynchronous reactive systems usually possess extremely large or even infinite state spaces. We propose an integer linear program (ILP) solving-based property checking framework that concentrates on the local analysis of the cyclic behavior of each individual component of a system. We apply our framework to the checking of the buffer boundedness and livelock freedom properties, both of which are undecidable for asynchronous reactive systems with an infinite state space. We illustrate the application of the proposed checking methods to Promela, the input language of the SPIN model checker. While the precision of our framework remains an issue, we propose a counterexample guided abstraction refinement procedure based on the discovery of dependences among control flow cycles. We have implemented prototype tools with which we obtained promising experimental results on real-life system models.

关键词： Software verification formal methods property checking integer linear programming static analysis abstraction refinement counterexamples asynchronous communication buffer boundedness livelock freedom control flow cycles cycle dependences UML Promela

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integer linear programming-based multi-objective scheduling for scientific workflows in multi-cloud environments

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JOURNAL OF SUPERCOMPUTING 2019年第10期75卷 6683-6709页

作者： Mohammadi, Somayeh PourKarimi, Latif Pedram, Hossein Islamic Azad Univ Sci & Res Branch Dept Comp Engn Tehran Iran Razi Univ Dept Math Kermanshah Iran Amirkabir Univ Technol Dept Comp Engn Tehran Iran

Scientific communities are motivated to schedule the data-intensive scientific workflows in multi-cloud environments, where considerable diverse resources are provided by multiple clouds and resource limitation imposed by individual clouds is overcome. However, this scheduling involves two conflicting objectives: minimizing cost and makespan. In general, dealing with such conflicting criteria is a difficult task. But fortunately recent efficient methods for solving multi-objective optimization problems motivated us to provide a multi-objective model considering minimization of cost and makespan as objectives. For solving this model, we use different scalarization procedures such as weighted-sum, Benson's scalarization and weighted min-max under different scenarios. Moreover, we investigate the stability of obtained solutions and propose a new approach for determining the most stable solution related to weighted-sum and weighted min-max as post-optimality analysis. Results indicate that our proposed weighted-sum approach outperforms the previously developed methods in terms of hypervolume.

关键词： Multi-cloud Workflow scheduling Multi-objective optimization integer linear programming

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integer linear programming formulations of the filter partitioning minimization problem

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2020年第2期40卷 431-453页

作者： Rahmani, Hazhar O'Kane, Jason M. Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA

Combinatorial filters, which take the form of labelled transition graphs, are a general representation for filtering and inference tasks in robotics. They are of particular interest in contexts where the objective is to minimize the computational resources needed to execute the filter. One specific problem is called the filter minimization (FM) problem, in which the goal is to find, for a given original filter, a state-minimal filter equivalent to the original filter. We consider a special case of FM, called the filter partitioning minimization (FPM) problem, in which the reduced filter must partition the state space of the original filter. This problem has been proven to be NP-hard. This paper considers the practical problem of solving FPM in spite of these hardness results. In contrast to the best known algorithm for this problem, a heuristic approach based on graph coloring proposed by O'Kane and Shell, we show how to convert an FPM instance to an instance of the well-known integer linear programming (ILP) problem. We present three distinct formulations of this reduction. Though ILP is itself a challenging problem, reducing FPM to ILP has the advantage that the ILP problem has been studied in great detail, and highly-optimized solvers are readily available. We describe experiments comparing this approach to the heuristic algorithm of O'Kane and Shell. The results show that the proposed ILP technique performs better in computing exact solutions as the filter sizes grow, and that the ILP approach obtains higher-quality feasible solutions, in contexts where time limitations prohibit the computation of exact solutions.

关键词： Combinatorial filters State space reduction integer linear programming Automata minimization Combinatorial optimization

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integer linear programming models for 2-staged two-dimensional Knapsack problems

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MATHEMATICAL programming 2003年第2-3期94卷 257-278页

作者： Lodi, A Monaci, M Univ Bologna Dipartimento Elettron Informat & Sistemist I-40136 Bologna Italy

We are given a unique rectangular piece of stock material S, with height H and width W, and a list of in rectangular shapes to be cut from S. Each shape's type i (i = l,....,in) is characterized by a height (h) over bar (i), a width (w) over bar (i), a profit (p) over bar (i), and an upper bound ub(i) indicating the maximum number of items of type i which can be cut. We refer to the Two-Dimensional Knapsack (TDK) as the problem of determining a cutting pattern of S maximizing the sum of the profits of the cut items. In particular, we consider the classical variant of TDK in which the maximum number of cuts allowed to obtain each item is fixed to 2. and we refer to this problem as 2-staged TDK (2TDK). For the 2TDK problem we present two new integer linear programming models, we discuss their properties, and we compare them with other formulations in terms of the LP bound they provide. Finally, both models are computationally tested within a standard branch-and-bound framework on a large set of instances from the literature by reinforcing them with the addition of linear inequalities to eliminate symmetries.

关键词： packing cutting integer linear programming

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integer linear programming model for multidimensional two-way number partitioning problem

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2010年第8期60卷 2302-2308页

作者： Kojic, Jelena Univ Belgrade Fac Math Belgrade 11000 Serbia

This paper introduces a multidimensional generalization of the two-way number partitioning problem, as well as an integer linear programming formulation of the problem. There are n binary variables and 2p constraints. The numerical experiments are made on a randomly generated set. In view of its integer linear programming formulation, tests are run in CPLEX. This NP-hard problem uses a set of vectors rather than a set of numbers. The presented experimental results indicate that the generalized problem is much harder than the initial problem. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Number partitioning integer linear programming Combinatorial optimization

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