Linear Programming-based Approach Research Articles

Shared Spectrum Cooperative Networks: A Linear Programming-Based Approach

Shared Spectrum Cooperative Networks: A Linear Programming-Based Approach

An Approximate Dynamic Programming Approach to Dynamic Stochastic Matching

Dynamic stochastic matching problems arise in a variety of recent applications, ranging from ridesharing and online video games to kidney exchange. Such problems are naturally formulated as Markov decision processes (MDPs) that are, however, intractable in general. To improve tractability, we investigate the linear programming-based approach to approximate dynamic programming. This approach can provide both feasible control policies and bounds on the MDPs’ optimal policy value, which can be used to establish optimality gaps. However, the approximate linear programs (ALPs) resulting from this approach can often be difficult to solve. To address this computational challenge, we derive novel ALP reformulations that can be used for a broad class of dynamic stochastic matching problems that incorporate, among others, possible match failures and certain restrictions on feasible matchings. We show that these ALP reformulations can be solved efficiently and applied to a broad class of dynamic matching problems. In addition, our numerical results indicate that our ALP reformulations can produce tight bounds that allow us to establish near-optimal policy performance for a broad set of problem instances. Thus, ALP reformulations can present an attractive alternative for applications that involve dynamic stochastic matching. History: Accepted by Nicola Secomandi, Area Editor for Stochastic Models & Reinforcement Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0203 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0203 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

An Effective Deployment of Controllers in Software-Defined Networks

Network performance has become a crucial consideration in ensuring a flawless user experience in the era of data-intensive applications and cloud computing. Traditional Networks are not suitable for solving such issues, so Software-defined networking (SDN) has evolved as a game-changing technology, promising network flexibility, scalability, and centralized control. However, some challenges remain open to the research, and one of the most popularly known challenges is controller problem placement (CPP). Latency is also a major consideration in the network. However, implementing low-latency communication in SDN systems remains a difficult issue, especially as networks become more complicated. In the realm of SDN, where centralized control meets distributed data planes, minimizing latency between switches and controllers emerges as a pivotal challenge. This article delves into the critical concept of optimizing controller placement within SDN networks to minimize latency, with a primary focus on considering the switch-controller distance. The proposed work uses a Linear programming-based approach to identify the optimal controller location in the network by considering the switch-controller distance and the latency is calculated. Further, a comparison of the proposed work with a heuristic-based approach is performed which shows that the proposed work performs well in terms of latency.

Ensuring secure interoperation of access control in a multidomain environment

Interoperation can combine multiple resources and domains, thus it has been widely used in many practical industrial applications, such as distributed database systems. However, the merger of local access control policies in such systems may lead to security violations with regard to access control. For instance, a person can potentially have access (indirectly) to another one's file or data in the interoperation to which s/he should be denied access in the individual system. Therefore, it is critical to deal with such issues in a multidomain environment. Nevertheless, a real-world interoperation contains a large number of entities and access. This imposes a challenge to find the maximum secure interoperation in terms of direct data sharing among individual systems. To overcome this difficulty, we propose an integer linear programming-based approach which can find the maximum secure interoperation in a computationally efficient way. Experimental results are given to demonstrate the efficacy of our approach.

Efficient approaches to optimize energy consumption in 3D wireless video sensor network under the coverage and connectivity constraints

Two advanced approaches (PA_1, and PA_2) based on a realistic 3D model of video sensor nodes (VSNs) deployed randomly over a 2D target area are proposed to minimize energy consumption in the network maintaining area coverage and connectivity. The reduction of the number of active VSNs decreases energy consumption but at the same time reduces the area coverage and connectivity. These conflicting issues are resolved and an optimal solution is obtained by using an integer linear programming-based approach (PA_1). But PA_1 is not tractable for large instances as the problem is NP-Hard. Hence, a heuristic approach (PA_2) based on an advanced genetic algorithm is also proposed in the present work for obtaining a near-optimal solution. Simulation studies are carried out to compare the performance of PA_1 and PA_2 with the other three state-of-the-art approaches (APP_5, APP_6, and ET_3). Among the three existing approaches, APP_6/(ET_3) is the best in energy consumption/(area coverage). It is observed that for the same simulation environment, both PA_1 and PA_2 guarantee higher network services, by reducing energy consumption by 40.85% and 33.34% respectively compared to the best existing approach APP_6; and as well as by increasing area coverage by 0.94% than the best existing approach ET_3 for the node density 150 on the target area of size 75x75 square meter. Between PA_1 and PA_2, PA_2 generates a suboptimal solution and PA_1 substantiates its superiority by reducing energy consumption by 11.26% than PA_2 without losing area coverage for the same simulation environment.

Common Lyapunov Functions for Switched Linear Systems: Linear Programming-Based Approach

We study the stability of an equilibrium of arbitrarily switched, autonomous, continuous-time systems through the computation of a common Lyapunov function (CLF). The switching occurs between a finite number of individual subsystems, each of which is assumed to be linear. We present a linear programming (LP) based approach to compute a continuous and piecewise affine (CPA) CLF and compare this approach with different methods in the literature. In particular we compare it with the prevalent use of linear matrix inequalities (LMIs) and semidefinite optimization to parameterize a quadratic common Lyapunov function (QCLF) for the linear subsystems.

Steiner Tree Problem in Graphs and Mixed Integer Linear Programming-Based Approach in GAMS

The Steiner tree problem in graphs involves finding a minimum cost tree which connects a defined subset of the vertices. This problem generalises the minimum spanning tree problem, in contrast, it is NP-complete and is usually solved for large instances by deterministic or stochastic heuristic methods and approximate algorithms. In this paper, however, we focus on a different approach, based on the formulation of a mixed integer programming model and its modification for solving in the professional optimization tool GAMS, which is now capable of solving even large instances of problems of exponential complexity.

Data Envelopment Analysis of linguistic features and passage relevance for open-domain Question Answering

Question Answering (QA) systems play an important role in today’s human–computer interaction systems. QA performance can be significantly improved using effective answer passage retrieval and ranking techniques. Our focus in this paper is on both non machine learning-based and deep learning-based passage retrieval and ranking systems for QA to leverage linguistic features within the text of questions and passages and improve passage ranking effectiveness. We propose a decoupled linguistic and linear programming-based approach for passage ranking using the Data Envelopment Analysis (DEA) technique to improve over well-established answer passage retrieval techniques. Our method scores passages using information retrieval and deep learning relevance metrics, represents retrieved passages using their relevance scores and several linguistic features, and finally makes use of DEA to re-rank the retrieved list of passages. The high effectiveness and significance of our proposed passage ranking method is demonstrated based on several experiments that we have conducted on a standard benchmark data set.

Anticipating technical car sequencing rules in the master production scheduling of mixed-model assembly lines

In this paper, we propose a new linear programming-based approach that enables the consideration of technical car sequencing rules in the master production scheduling of mixed-model assembly lines at a much higher level of detail than previous approaches. To this end, we investigate certain interdependencies of car sequencing rules, which have largely been neglected, both in practice and in the research literature. We illustrate the existence and impact of these interdependencies and show that they induce additional implicit constraints, which can be represented by linear inequalities and incorporated into linear optimization models for master production scheduling. In a numerical study, we evaluate the approach and show, that it can significantly reduce sequencing violations compared to existing approaches.

Robust and budget-constrained encoding configurations for in-memory database systems

Data encoding has been applied to database systems for decades as it mitigates bandwidth bottlenecks and reduces storage requirements. But even in the presence of these advantages, most in-memory database systems use data encoding only conservatively as the negative impact on runtime performance can be severe. Real-world systems with large parts being infrequently accessed and cost efficiency constraints in cloud environments require solutions that automatically and efficiently select encoding techniques, including heavy-weight compression. In this paper, we introduce workload-driven approaches to automaticaly determine memory budget-constrained encoding configurations using greedy heuristics and linear programming. We show for TPC-H, TPC-DS, and the Join Order Benchmark that optimized encoding configurations can reduce the main memory footprint significantly without a loss in runtime performance over state-of-the-art dictionary encoding. To yield robust selections, we extend the linear programming-based approach to incorporate query runtime constraints and mitigate unexpected performance regressions.

A linear programming-based approach to estimate discrete probability functions with given quantiles

The aim of this paper is to estimate probability distribution functions with maximum entropy and known quantiles‎. ‎The paper formulates the problem as a nonlinear optimization problem‎, ‎and converts it into a system of nonlinear equations by Lagrange multipliers method‎. ‎Finally‎, ‎an efficient method is proposed to obtain a solution of the nonlinear system‎. ‎The method needs to solve a linear programming problem in each iteration‎. ‎Since linear programming problems can be solved in a reasonable time‎, ‎our proposed method is faster than generic methods of solving nonlinear programming problems‎. ‎Several computational experiment are provided to demonstrate the performance and validation of our proposed method‎.

Performance assessment and capital budgeting based on performance

PurposeThe purpose of this paper is to propose two performance-based capital budgeting (PBCB) approaches. The proposed approaches aim to assign limited capital within different firms based on their performance. These approaches have been applied to capital budgeting process of the technical and vocational training schools of Semnan Province, Iran for the fiscal year 2014–2016. Although the current capital of each decision-making unit (DMU) is secured in the first approach, the second approach provides possibility of sharing the capital between DMUs.Design/methodology/approachData envelopment analysis which is a broadly used mathematical programming technique for assessing performance of DMUs is utilized for the first phase of both approaches. The proposed models are based on linear programming. Different scenarios are presented and their pros and cons for the capital budgeting process are discussed.FindingsThe proposed approaches are applied to capital budgeting process for a fiscal year of technical and vocational training schools of Semnan Province, Iran. The budget allocation of the previous year has been found to be non-optimal in terms of budget consuming. This emphasizes reconsideration of budget allotment within schools. The results show a high potential for producing more outputs. The second approach that provides the possibility of sharing and realloting of budget between schools based on their performance may be crucial for those schools that are not performing efficiently because there is possibility of losing budget in each given year in contrast with the previous years.Originality/valueThis paper proposes two linear programming-based approaches for the PBCB. The author not only deals with static framework but also proposes dynamic structured models. Using performance-based budgeting in organizations has been emphasized by authorities in Iran for many years. Using the proposed approaches, different suggestion and policy recommendation for decision makers in process of capital budgeting process within period of study are provided for technical and vocational training schools of Semnan Province, Iran.

Optimal reliability improvement strategy in radial distribution networks with island operation of distributed generation

This study presents a mixed integer linear programming-based approach for determining the optimal strategy for reliability improvement in radial distribution networks where island operation of distributed generators (DGs) is allowed. The proposed approach simultaneously defines the optimal number, type, and location of new automation devices (ADs) to be installed in a network, the optimal relocation of the existing ADs and the optimal creation of DG islands. The load curtailment possibility in creating islands is considered along with the multi-level load model. The presented numerical results indicate the effectiveness of the proposed approach and the importance of simultaneous consideration of the aforementioned individual strategies in determining the best reliability improvement strategy in radial distribution networks with DGs.

Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation technique to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

Better ILP-Based Approaches to Haplotype Assembly.

Haplotype assembly is to directly construct the haplotypes of an individual from sequence fragments (reads) of the individual. Although a number of programs have been designed for computing optimal or heuristic solutions to the haplotype assembly problem, computing an optimal solution may take days or even months while computing a heuristic solution usually requires a trade-off between speed and accuracy. This article refines a previously known integer linear programming-based (ILP-based) approach to the haplotype assembly problem in twofolds. First, the read-matrices of some datasets (such as NA12878) come with a quality for each base in the reads. We here propose to utilize the qualities in the ILP-based approach. Secondly, we propose to use the ILP-based approach to improve the output of any heuristic program for the problem. Experiments with both real and simulated datasets show that the qualities of read-matrices help us find more accurate solutions without significant loss of speed. Moreover, our experimental results show that the proposed hybrid approach improves the output of ReFHap (the current leading heuristic) significantly (say, by almost 25% of the QAN50 score) without significant loss of speed, and can even find optimal solutions in much shorter time than the original ILP-based approach. Our program is available upon request to the authors.

Assignment of inter-die signals in a simplified wiring model for die-stacking SiP designs

Compared with traditional flow in IC designs, the assignment of the inter-die signals between different dies is an important stage in a die-stacking SiP design. In this paper, given a tolerant spacing rule between two inter-die signals, the crossing constraint between two inter-die signals can be firstly defined for the bonding wires in a die-stacking SiP design with a simplified wiring model [7]. Furthermore, based on the connection constraint on any inter-die signal, the capacity constraint on any assigned pad on dies and the crossing constraint between two inter-die signals, an integer linear programming-based (ILP-based) approach is proposed to assign all the inter-die signals to minimize the total wirelength in a die-stacking SiP design. Compared with Lin’s two-stage approach [4] for some tested examples without a tolerant spacing distance between two inter-die signals in an Euclidean wiring model, the experimental results show that our proposed ILP-based approach increases 4.5% of CPU time and reduces 6.2% of total wirelength to assign all the inter-die signals on the average. Besides that, compared with Yan’s iterative approach [6] for some tested examples without a tolerant spacing distance between two inter-die signals in an Euclidean wiring model, the experimental results show that our proposed ILP-based approach uses reasonable CPU time to reduce 5.3% of total wirelength to assign all the inter-die signals on the average. Compared with Lin’s modified two-stage approach and Yan’s modified iterative approach for some dense tested examples with a tolerant spacing distance between two inter-die signals in a simplified wiring model [7], the experimental results show that Lin’s modified two-stage approach only achieves 95.9% of the assignment ratio on the average, Yan’s modified iterative approach only achieves 96.6% of the assignment ratio on the average and our proposed ILP-based approach uses reasonable CPU time to achieve 100% of the assignment ratio.

Quantifying Significance of MHC II Residues.

The major histocompatibility complex (MHC), a cell-surface protein mediating immune recognition, plays important roles in the immune response system of all higher vertebrates. MHC molecules are highly polymorphic and they are grouped into serotypes according to the specificity of the response. It is a common belief that a protein sequence determines its three dimensional structure and function. Hence, the protein sequence determines the serotype. Residues play different levels of importance. In this paper, we quantify the residue significance with the available serotype information. Knowing the significance of the residues will deepen our understanding of the MHC molecules and yield us a concise representation of the molecules. In this paper we propose a linear programming-based approach to find significant residue positions as well as quantifying their significance in MHC II DR molecules. Among all the residues in MHC II DR molecules, 18 positions are of particular significance, which is consistent with the literature on MHC binding sites, and succinct pseudo-sequences appear to be adequate to capture the whole sequence features. When the result is used for classification of MHC molecules with serotype assigned by WHO, a 98.4 percent prediction performance is achieved. The methods have been implemented in java (http://code.google.com/p/quassi/).

On Efficient Utilization of Training Pilots for Linear Equalization of QAM Transmissions

Conventional approaches to linear equalization of data transmissions under frequency selective channels simply treat the problem as a system identification issue. However, practical data communication systems impose additional conditions on the channel input signals such as LDPC error correction coding and known data constellation. We study a unified framework to integrate these known constraints into the channel equalization procedure and propose a linear programming-based approach to channel equalization for QAM signals that can succeed with significantly reduced pilots. In addition to utilizing the information of QAM constellation, our method exploits the code information of LDPC forward error correction coding to improve the equalization performance and convergence properties.

Green Provisioning of Many-to-Many Sessions Over WDM Optical Networks

In a many-to-many session, a participant distributes and receives traffic flows to/from all others in the same session. It is desirable to achieve both high resource utilization and low power consumption when such sessions are provisioned. In wavelength division multiplexing (WDM) optical networks, traffic grooming has been widely applied as the key technology for aggregating sessions to improve resource utilization. However, the current many-to-many grooming approaches, the lightpath circle and hubbed light-tree, do not make an effort to reduce the power consumption of many-to-many sessions. In this paper, we formulate the green routing and aggregation of many-to-many sessions problem and prove its NP-completeness. For problem solving in WDM optical networks, we apply a rational combined method of lightpaths. Both an integer linear programming-based approach and a novel graph-based heuristic are devised. We further provide an illustrative comparison between our heuristic and the existing benchmarks in terms of power consumption. Extensive simulation results demonstrate that, compared with the benchmarks, our heuristic improves the average power efficiency in the range of 13-19%.

Model-based decision support for optimal brochure pricing: applying advanced analytics in the tour operating industry

The research presented in this paper is motivated by an industry project conducted with TUI Deutschland, Germany's leading tour operator. We consider the decision problem of optimally determining hotel room prices to be published in the tour operator's brochure, which is usually valid for a half-year period. In practice, this task is performed manually by a large number of pricing specialists, each of whom is in charge of setting up to 100,000 prices. In this paper, we develop an advanced analytics approach to provide decision support for this task. More precisely, we propose a mixed integer linear programming-based approach, involving state-of-the-art methods from data analysis and optimization. In this context, we formally introduce the brochure pricing problem as a new optimization problem and present several alternative mathematical model formulations. The problem incorporates demand-side behavior by including a general attraction model whose parameters can be obtained from past booking data. Furthermore, we present different real-world scenarios of model-based decision support, showing how the brochure pricing problem and some variants thereof can be integrated into the manual decision making process, given the requirement of using standard optimization software. For example, the model-based approach can help the pricing specialist balance the objective of profit maximization and the disadvantage of a very complicated pricing structure.