Constraint Algorithm Research Articles

Randomized Strategies for Robust Combinatorial Optimization

In this paper, we study the following robust optimization problem. Given an independence system and candidate objective functions, we choose an independent set, and then an adversary chooses one objective function, knowing our choice. The goal is to find a randomized strategy (i.e., a probability distribution over the independent sets) that maximizes the expected objective value in the worst case. This problem is fundamental in wide areas such as artificial intelligence, machine learning, game theory and optimization. To solve the problem, we propose two types of schemes for designing approximation algorithms. One scheme is for the case when objective functions are linear. It first finds an approximately optimal aggregated strategy and then retrieves a desired solution with little loss of the objective value. The approximation ratio depends on a relaxation of an independence system polytope. As applications, we provide approximation algorithms for a knapsack constraint or a matroid intersection by developing appropriate relaxations and retrievals. The other scheme is based on the multiplicative weights update (MWU) method. The direct application of the MWU method does not yield a strict multiplicative approximation algorithm but yield one with an additional additive error term. A key technique to overcome the issue is to introduce a new concept called (η,γ)-reductions for objective functions with parameters η and γ. We show that our scheme outputs a nearly α-approximate solution if there exists an α-approximation algorithm for a subproblem defined by (η,γ)-reductions. This improves approximation ratios in previous results. Using our result, we provide approximation algorithms when the objective functions are submodular or correspond to the cardinality robustness for the knapsack problem.

Application of Logging While Drilling Tool in Formation Boundary Detection and Geo-steering

Logging while drilling (LWD) plays a crucial role in geo-steering, which can determine the formation boundary and resistivity in real time. In this study, an efficient inversion, which can accurately invert formation information in real time on the basis of fast-forward modeling, is presented. In forward modeling, the Gauss–Legendre quadrature combined with the continued fraction method is used to calculate the response of the LWD instrument in a layered formation. In inversion modeling, the Levenberg–Marquardt (LM) algorithm, combined with the line search method of the Armijo criterion, are used to minimize the cost function, and a constraint algorithm is added to ensure the stability of the inversion. A positive and negative sign is added to the distance parameter to determine whether the LWD instrument is located above or below the formation boundary. We have carried out a series of experiments to verify the accuracy of the inversion. The experimental results suggest that the forward algorithm can make the infinite integral of the Bessel function rapidly converge, and accurately obtain the response of the LWD instrument in a layered formation. The inversion can accurately determine the formation resistivity and boundary in real time. This is significant for geological exploration.

Introduction of Local Spatial Constraints and Local Similarity Estimation in Possibilistic c-Means Algorithm for Remotely Sensed Imagery

This paper presents a unique Possibilistic c-Means with constraints (PCM-S) with Adaptive Possibilistic Local Information c-Means (ADPLICM) in a supervised way by incorporating local information through local spatial constraints and local similarity measures in Possibilistic c-Means Algorithm. PCM-S with ADPLICM overcome the limitations of the known Possibilistic c-Means (PCM) and Possibilistic c-Means with constraints (PCM-S) algorithms. The major contribution of proposed algorithm to ensure the noise resistance in the presence of random salt & pepper noise. The effectiveness of proposed algorithm has been analysed on random “salt and pepper” noise added on original dataset and Root Mean Square Error (RMSE) has been calculated between original dataset and noisy dataset. It has been observed that PCM-S with ADPLICM is effective in minimizing noise during supervised classification by introducing local convolution.

Inter-Block Dependency-Based CTU Level Rate Control for HEVC

Although high efficiency video coding (HEVC) has achieved high coding performance, it has been shown that dependent rate-distortion optimization (RDO) can still further improve the coding performance at the encoder side. Inspired by our previous work on temporal-dependent RDO, this paper proposes a rate control method at coding tree unit (CTU) level to obtain both higher rate-distortion (R-D) performance and lower bitrate errors. We first formulate the global optimization problem in hybrid video coding and present a general solution framework based on a weighted Lagrange multiplier, where the weighted coefficient is related to the strength of temporal dependency among coding units, and a propagation factor is introduced for quantitative measurement of the temporal dependency. Second, an estimation method of CTU level R-D model is proposed to characterize R-D relationship more accurately. Finally, under the constraint of frame level bit budget, a formulation combining inter-block dependency and R-D characteristic is developed for optimal bit allocation at the CTU level. In the process of encoding a frame, a relaxed constraint algorithm (RCA) and a strict constraint algorithm (SCA) are employed to reduce the difference between numbers of actual and target bits for the current frame, respectively. Experimental results demonstrate that, on the HEVC test model (HM-16.7), our methods including RCA and SCA achieve considerable R-D performance improvement with lower bitrate errors, outperforming the state-of-the-art schemes. It is worth mentioning that even compared with HEVC without rate control, the RCA can still achieve bitrate savings of 5.4% and 5.1% on average under configurations of low delay P and B frames, respectively.

Multi-Period Network Design Problem in Regional Hazardous Waste Management Systems.

The management of hazardous wastes in regions is required to design a multi-echelon network with multiple facilities including recycling, treatment and disposal centers servicing the transportation, recycling, treatment and disposal procedures of hazardous wastes and waste residues. The multi-period network design problem within is to determine the location of waste facilities and allocation/transportation of wastes/residues in each period during the planning horizon, such that the total cost and total risk in the location and transportation procedures are minimized. With consideration of the life cycle capacity of disposal centers, we formulate the problem as a bi-objective mixed integer linear programming model in which a unified modeling strategy is designed to describe the closing of existing waste facilities and the opening of new waste facilities. By exploiting the characteristics of the proposed model, an augmented -constraint algorithm is developed to solve the model and find highly qualified representative non-dominated solutions. Finally, computational results of a realistic case demonstrate that our algorithm can identify obviously distinct and uniformly distributed representative non-dominated solutions within reasonable time, revealing the trade-off between the total cost and total risk objectives efficiently. Meanwhile, the multi-period network design optimization is superior to the single-period optimization in terms of the objective quality.

Memetic Evolution for Generic Full-Body Inverse Kinematics in Robotics and Animation

In this paper, a novel and fast memetic evolutionary algorithm is presented which can solve fully constrained generic inverse kinematics with multiple end effectors and goal objectives, leaving high flexibility for the design of custom cost functions. The algorithm utilizes a hybridization of evolutionary and swarm optimization, combined with the limited-memory-Broyden-Fletcher-Goldfarb-Shanno with bound constraints algorithm for gradient-based optimization. Accurate solutions can be found in real-time and suboptimal extrema are robustly avoided, scaling well even for greatly higher degree of freedom. The algorithm provides a general framework for bounded continuous optimization which only requires two parameters for the number of individuals and elites to be set, and supports adding additional goals and constraints for inverse kinematics, such as minimal displacement between solutions, collision avoidance, or functional joint relations. Experimental results on several industrial and anthropomorphic robots as well as on virtual characters demonstrate the algorithm to be applicable for solving complex kinematic postures for different challenging tasks in robotics, human-robot interaction and character animation, including dexterous object manipulation, collision-free full-body motion, as well as animation post-processing for video games and films. Implementations are made available for Unity3D and robot operating system.

A stable graph layout algorithm for processes

AbstractProcess mining enables organizations to analyze data about their (business) processes. Visualization is key to gaining insight into these processes and the associated data. Process visualization requires a high‐quality graph layout that intuitively represents the semantics of the process. Process analysis additionally requires interactive filtering to explore the process data and process graph. The ideal process visualization therefore provides a high‐quality, intuitive layout and preserves the mental map of the user during the visual exploration. The current industry standard used for process visualization does not satisfy either of these requirements. In this paper, we propose a novel layout algorithm for processes based on the Sugiyama framework. Our approach consists of novel ranking and order constraint algorithms and a novel crossing minimization algorithm. These algorithms make use of the process data to compute stable, high‐quality layouts. In addition, we use phased animation to further improve mental map preservation. Quantitative and qualitative evaluations show that our approach computes layouts of higher quality and preserves the mental map better than the industry standard. Additionally, our approach is substantially faster, especially for graphs with more than 250 edges.

Active constraint spectral clustering based on Hessian matrix

Applying the pairwise constraint algorithm to spectral clustering has become a hot topic in data mining research in recent years. In this paper, a clustering algorithm is proposed, called an active constraint spectral clustering based on Hessian matrix (ACSCHM); this algorithm not only use Hessian matrix instead of Laplacian matrix to free the parameter but also use an active query function to dynamically select constraint pairs and use these constraints to tune and optimize data points. In this paper, we used active query strategy to replace the previous random query strategy, which overcame the instability of the clustering results brought by the random query and enhanced the robustness of the algorithm. The unique parameter in the Hessian matrix was obtained by the spectral radius of the matrix, and the parameter selection problem in the original spectral clustering algorithm was also solved. Experiments on multiple UCI data sets can prove the effectiveness of this algorithm.

Prediction of impact response of delaminated pretwisted stiffened shell

ABSTRACT The dynamic response of delaminated twisted stiffened cylindrical shell under low-velocity impact of multiple masses is presented. The impactors are considered to impact at two arbitrary locations simultaneously or at different time, wherein the contact forces of the impactor are computed by using the modified Hertzian contact law. Multipoint constraint algorithm is used to create rectangular inter-ply delamination. An eight-noded isoparametric shell element is combined with a three-noded isoparametric beam element to model the stiffened panel. The governing equations are derived from Lagrange’s equation of motion and solved by Newmark’s constant acceleration algorithm to perform the parametric study.

Spatial weight matrix impact on real estate hierarchical clustering in the process of mass valuation

Research background: The value of the property can be determined on an individual or mass basis. There are a number of situations in which uniform and relatively fast results obtained by means of mass valuation undoubtedly outweigh the advantages of the individual approach. In literature and practice there are a number of different types of models of mass valuation of real estate. For some of them it is postulated or required to group the valued properties into homogeneous subset due to various criteria. One such model is Szczecin Algorithm of Real Estate Mass Appraisal (SAREMA). When using this algorithm, the area to be valued should be divided into the so-called location attractiveness areas (LAZ). Such division can be made in many ways. Regardless of the method of clustering, its result should be assessed, depending on the degree of implementation of the adopted criterion of division quality. A better division of real estate will translate into more accurate valuation results.
 Purpose of the article: The aim of the article is to present an application of hierarchical clustering with a spatial constraints algorithm for the creation of LAZ. This method requires the specification of spatial weight matrix to carry out the clustering process. Due to the fact that such a matrix can be specified in a number of ways, the impact of the proposed types of matrices on the clustering process will be described. A modified measure of information entropy will be used to assess the clustering results.
 Methods: The article utilises the algorithm of agglomerative clustering, which takes into account spatial constraints, which is particularly important in the context of real estate valuation. Homogeneity of clusters will be determined with the means of information entropy.
 Findings & Value added: The main achievements of the study will be to assess whether the type of the distance matrix has a significant impact on the clustering of properties under valuation.

Fast exact algorithms for some connectivity problems parameterized by clique-width

Given a clique-width k-expression of a graph G, we provide 2O(k)⋅n time algorithms for connectivity constraints on locally checkable properties such as Node-Weighted Steiner Tree, Connected Dominating Set, or Connected Vertex Cover. We also propose a 2O(k)⋅n time algorithm for Feedback Vertex Set. The best running times for all the considered problems were 2O(k⋅log⁡(k))⋅nO(1).

Distributed Generalized Nash Equilibria Computation of Monotone Games via Double-Layer Preconditioned Proximal-Point Algorithms

In this paper, we investigate distributed generalized Nash equilibrium (GNE) computation of monotone games with affine coupling constraints. Each player can only utilize its local objective function, local feasible set and a local block of the coupling constraint, and can only communicate with its neighbours. We assume the game has monotone pseudo-subdifferential without Lipschitz continuity restrictions. We design novel center-free distributed GNE seeking algorithms for equality and inequality affine coupling constraints, respectively. A proximal alternating direction method of multipliers(ADMM) is proposed for the equality case, while for the inequality case, a parallel splitting type algorithm is proposed. In both algorithms, the GNE seeking task is decomposed into a sequential NE computation of regularized subgames and distributed update of multipliers and auxiliary variables, based on local data and local communication. Our two double-layer GNE algorithms need not specify the inner-loop NE seeking algorithm and moreover, only require that the strongly monotone subgames are inexactly solved. We prove their convergence by showing that the two algorithms can be seen as specific instances of preconditioned proximal point algorithms} (PPPA) for finding zeros of monotone operators. Applications and numerical simulations are given for illustration.

Improved hop‐based localisation algorithm for irregular networks

The hop-based localisation algorithm uses hop-by-hop propagation to establish node-to-anchor distance estimation, which does not require costly and complicated ranging hardware. This helps boost system performance, while minimising the cost of localising the nodes within the network. However, the application of hop-based localisation algorithms is restricted due to their dramatic accuracy degradation in irregular network, which is mainly caused by the large error of distance estimation. The authors find that the error variance of the estimated distance increases as the hop count increases, i.e. there is a heteroscedasticity problem in the distance estimation process, which will affect the location estimation. In this study, by exploring the error during the location estimation, they aim to find and employ the optimal weighted function to improve localisation accuracy. A geometric constraint algorithm is also devised to correct the incorrectly estimated location by mitigating the adverse effects from flip ambiguity. By combining the optimal weighted function and the geometric constraint algorithm, a novel hop-based localisation algorithm is proposed in this study. Both the theoretical analysis and experimental results show that the proposed method has not only maintained the economic characteristics of hop-based localisation, but also has the high localisation accuracy where it can be adapted to various networks with different node distributions.

Dynamic optimization of a district energy system with storage using a novel mixed-integer quadratic programming algorithm

As more renewable energy is integrated into the power grid, it is increasingly important to exploit variable electricity pricing structures to minimize commercial utility costs and enable more intermittent renewables on the grid through proactive management of energy storage. Using data from a large campus district energy system, equipped with centralized chilled water plants and a thermal energy storage tank, a novel technique is proposed to optimize this system in real-time, formulated as a mixed-integer quadratic programming problem. This method, titled Quadratic Programming Hybrid with Augmented Constraints, is sufficiently fast to be computed in real-time for a district chiller system. This method is compared to both Branch and Bound and a simple logical decision algorithm in both speed and optimality. The proposed method for solving this mixed-integer quadratic programming problem proves very successful at achieving a near-optimal solution when compared to a standard Branch and Bound (BnB) algorithm. Although suboptimal, the proposed algorithm takes 99.96–99.99% less computational time than the standard BnB and computes an answer to within 29.9% of the BnB objective function. When compared to a simple logical decision algorithm, which represents an operator manually controlling the plant, the proposed method is estimated to yield 8.10–33.7% in savings on chiller energy costs. The Quadratic Programming Hybrid with Augmented Constraints algorithm shows potential for use in a real-time optimization application to exploit variable electricity pricing and significantly reduce the costs of running a chiller plant with thermal energy storage.

Local Fourier Analysis of Balancing Domain Decomposition By Constraints Algorithms

Local Fourier analysis is a commonly used tool for the analysis of multigrid and other multilevel algorithms, providing both insight into observed convergence rates and predictive analysis of the performance of many algorithms. In this paper, for the first time, we adapt local Fourier analysis to examine variants of two- and three-level balancing domain decomposition by constraints (BDDC) algorithms, to better understand the eigenvalue distributions and condition number bounds on these preconditioned operators. This adaptation is based on a modified choice of basis for the space of Fourier harmonics that greatly simplifies the application of local Fourier analysis in this setting. The local Fourier analysis is validated by considering the two-dimensional Laplacian and predicting the condition numbers of the preconditioned operators with different sizes of subdomains. Several variants are analyzed, showing that the two- and three-level performance of the “lumped” variant can be greatly improved when used in multiplicative combination with a weighted diagonal scaling preconditioner, with weight optimized through the use of local Fourier analysis.

Fuzzy Based Approach to Incorporate Spatial Constraints in Possibilistic c-Means Algorithm for Remotely Sensed Imagery

This paper presents an unique Possibilistic c-Means with constraints (PCM-S) algorithms in a supervised way. This algorithm overcome the disadvantage of Possibilistic c-Means (PCM) algorithm by incorporating local information through spatial constraints to control the effect of neighboring terms. PCM-S has been deployed by adding spatial constraints in order to provide robustness to noise and outliers. FORMOSAT-2 satellite imagery of Haridwar city has been used and classified result is tested with Mean Membership Difference method.

Weighted lp Norm Sparse Error Constraint Based ADMM for Image Denoising

In the process of image denoising, the accurate prior knowledge cannot be learned due to the influence of noise. Therefore, it is difficult to obtain better sparse coefficients. Based on this consideration, a weighted lp norm sparse error constraint (WPNSEC) model is proposed. Firstly, the suitable setting of power p in the lp norm is made a detailed analysis. Secondly, the proposed model is extended to color image denoising. Since the noise of RGB channels has different intensities, a weight matrix is introduced to measure the noise levels of different channels, and a multichannel weighted lp norm sparse error constraint algorithm is proposed. Thirdly, in order to ensure that the proposed algorithm is tractable, the multichannel WPNSEC model is converted into an equality constraint problem solved via alternating direction method of multipliers (ADMM) algorithm. Experimental results on gray image and color image datasets show that the proposed algorithms not only have higher peak signal‐to‐noise ratio (PSNR) and feature similarity index (FSIM) but also produce better visual quality than competing image denoising algorithms.

Parametric Study on the First Ply Failure Load of Delaminated Shallow Pretwisted Conical Shells – A Finite Element Approach

The present study deals with the effect of offset length on the first ply failure load of undelaminated and mid plane delaminated composite shallow conical shells. Numerical results were obtained using finite element codes for the failure loads of undelaminated and mid plane delaminated composite conical shell for different offset length considering twisted and untwisted instance. From Lagrange’s equation generalized dynamic equations have been derived for moderate rotational speed ignoring the Coriolis Effect. Based on Mindlin’s theory the theoretical formulation has been carried out and it has been implemented by using an isoparametric plate bending element having eight nodes. Multi-point constraint algorithm has been used to ensure the equilibrium of resultant forces, moments and compatibility of deformation at delamination crack front. The standard Eigen value problem has been solved based on standard QR iteration algorithms. Convergence studies are carried out considering mesh sizes, and results are validated with open literature to verify the correctness of the present finite element formulation. The first ply failure load considering different failure criteria namely Max stress (independent), Tsai Hill, Tsai Wu Hahn and Tsai Hill Hoffman are evaluated. The variation of the failure loads for different offset length and twist angle are illustrated and presented.

Bi-objective Optimization of Multiple-route Job Shop Scheduling with Route Cost

Abstract Multiple-route job shop scheduling problem (MRJSP) is an interesting variation of the job shop scheduling problem such that each job has one or more available processing routes, each of which is of a non-identical number of operations. We investigate the bi-objective MRJSP problem to minimize the makespan and the cost of selected processing routes in this work. Different processing routes of any job are of non-equal processing cost. We establish a bi-objective integer programming model, and devise an epsilon constraint algorithm together with a non-dominated sorting genetic algorithm II (NSGA-II). The epsilon constraint method produces exact solutions for small job instances, while NSGA-II can efficiently solve large job instances. Numerical experiments validate the proposed model and algorithms.

An Online Task Assignment Based on Quality Constraint for Spatio-Temporal Crowdsourcing

Crowdsourcing is the perfect embodiment of group wisdom. With the rapid development of mobile network and the sharing economy model, spatio-temporal crowdsourcing technology has been research hotspot. Task assignment is one of the core issues of spatio-temporal crowdsourcing technology. There are three algorithms: Random Algorithm , Random-Threshold-Based Algorithm (RT) and Adaptive random-threshold-based Algorithm (Adaptive RT) for maximizing the total utility in the online task assignment of three types of objects, tasks, workers and workplaces. But these algorithms ignore the distance cost and fairness between task requester and workers. Unfairness means that higher task’s reward with lower worker’s success ratio or lower task’s reward with higher worker’s success ratio in a match. Therefore, this paper proposes Quality Constraint Algorithm (QCA), which quantifies fairness between task requester and workers as match quality and adopts a matching strategy of automatic negotiation on task’s reward to improve the average match quality. QCA not only has higher average match quality and higher total utility, but also optimizes the average distance cost. Compared with Adaptive RT, QCA has an average increment of 11% on total utility, an average increment of 19% on average match quality and an average decrease of 17% on distance cost. In term of time cost, QCA is only 8% of Adaptive RT.