Journal of Global Optimization最新文献

Smoothing penalty approach for solving second-order cone complementarity problems 解决二阶锥体互补问题的平滑惩罚法

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-09-18 DOI: 10.1007/s10898-024-01427-8

Chieu Thanh Nguyen, Jan Harold Alcantara, Zijun Hao, Jein-Shan Chen

引用次数: 0

Aircraft conflict resolution with trajectory recovery using mixed-integer programming 利用混合整数程序设计恢复轨迹的飞机冲突解决方法

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-09-17 DOI: 10.1007/s10898-024-01393-1

Fernando Dias, David Rey

{"title":"Aircraft conflict resolution with trajectory recovery using mixed-integer programming","authors":"Fernando Dias, David Rey","doi":"10.1007/s10898-024-01393-1","DOIUrl":"https://doi.org/10.1007/s10898-024-01393-1","url":null,"abstract":"To guarantee the safety of flight operations, decision-support systems for air traffic control must be able to improve the usage of airspace capacity and handle increasing demand. This study addresses the aircraft conflict avoidance and trajectory recovery problem. The problem of finding the least deviation conflict-free aircraft trajectories that guarantee the return to a target waypoint is highly complex due to the nature of the nonlinear trajectories that are sought. We present a two-stage iterative algorithm that first solves initial conflicts by manipulating their speed and heading control and then identifying each aircraft’s optimal time to recover its trajectory towards their nominal one. We extend existing mixed-integer programming formulations by modelling speed and heading control as continuous variables while recovery time is treated as a discrete variable. We develop a novel iterative approach which shows that the trajectory recovery costs can be anticipated by inducing avoidance trajectories with higher deviation, therefore obtaining earlier recovery time within a few iterations. Numerical results on benchmark conflict resolution problems show that this approach can solve instances with up to 30 aircraft within 10 min.","PeriodicalId":15961,"journal":{"name":"Journal of Global Optimization","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved approximation algorithms for the k-path partition problem K 路径分割问题的改进近似计算法

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-09-13 DOI: 10.1007/s10898-024-01428-7

Shiming Li, Wei Yu, Zhaohui Liu

{"title":"Improved approximation algorithms for the k-path partition problem","authors":"Shiming Li, Wei Yu, Zhaohui Liu","doi":"10.1007/s10898-024-01428-7","DOIUrl":"https://doi.org/10.1007/s10898-024-01428-7","url":null,"abstract":"The k-path partition problem (kPP), defined on a graph (G=(V,E)), is a well-known NP-hard problem when (kge 3). The goal of the kPP is to find a minimum collection of vertex-disjoint paths to cover all the vertices in G such that the number of vertices on each path is no more than k. In this paper, we give two approximation algorithms for the kPP. The first one, called Algorithm 1, uses an algorithm for the (0,1)-weighted maximum traveling salesman problem as a subroutine. When G is undirected, the approximation ratio of Algorithm 1 is (frac{k+12}{7} -frac{6}{7k} ), which improves on the previous best-known approximation algorithm for every (kge 7). When G is directed, Algorithm 1 is a (left( frac{k+6}{4} -frac{3}{4k}right) )-approximation algorithm, which improves the existing best available approximation algorithm for every (kge 10). Our second algorithm, i.e. Algorithm 2, is a local search algorithm tailored for the kPP in undirected graphs with small k. Algorithm 2 improves on the approximation ratios of the best available algorithm for every (k=4,5,6). Combined with Algorithms 1 and 2, we have improved the approximation ratio for the kPP in undirected graphs for each (kge 4) as well as the approximation ratio for the kPP in directed graphs for each (kge 10). As for the negative side, we show that for any (epsilon >0) it is NP-hard to approximate the kPP (with k being part of the input) within the ratio (O(k^{1-epsilon })), which implies that Algorithm 1 is asymptotically optimal.","PeriodicalId":15961,"journal":{"name":"Journal of Global Optimization","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On convergence of a q-random coordinate constrained algorithm for non-convex problems 论非凸问题 q 随机坐标约束算法的收敛性

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-09-12 DOI: 10.1007/s10898-024-01429-6

A. Ghaffari-Hadigheh, L. Sinjorgo, R. Sotirov

引用次数: 0

A QoS and sustainability-driven two-stage service composition method in cloud manufacturing: combining clustering and bi-objective optimization 云制造中以服务质量和可持续性为导向的两阶段服务组合方法：聚类与双目标优化相结合

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-09-12 DOI: 10.1007/s10898-024-01430-z

Chunhua Tang, Shuangyao Zhao, Han Su, Binbin Chen

{"title":"A QoS and sustainability-driven two-stage service composition method in cloud manufacturing: combining clustering and bi-objective optimization","authors":"Chunhua Tang, Shuangyao Zhao, Han Su, Binbin Chen","doi":"10.1007/s10898-024-01430-z","DOIUrl":"https://doi.org/10.1007/s10898-024-01430-z","url":null,"abstract":"Manufacturing service composition (MSC) is a core technology in cloud manufacturing (CMfg), which has been intensively studied to find an optimal composite service with the best quality of service (QoS). With the continuous expansion of CMfg platforms, the difficulty of MSC is gradually increasing. Large-scale platforms have put forward higher requirements for combination efficiency, and its open and dynamic environment makes service QoS exhibit strong uncertainty, leading to reliability issues of MSC. Meanwhile, the increased number of services and users makes it necessary for the platform to consider the sustainability issue, including economic, environmental, and social aspects, based on an operations management perspective. However, current studies only consider part of efficiency, reliability, and sustainability as optimization objectives in MSC allocation models, and do not take them into account simultaneously in an integrated manner. Therefore, this study proposes a two-stage method integrating clustering and multi-objective optimization for reliable and sustainable MSC allocation. Specifically, in the first stage, the K-means clustering technique and the QoS stability-based service pruning mechanism are integrated into the service clustering process to improve the reliability of candidate services and reduce the search space of combinations. In the second stage, a multi-objective optimization model with maximizing QoS and sustainability is proposed to find the optimal MSC, and the fast non-dominated sorting genetic algorithm is adopted to solve the model. Finally, a case study of the actual production of a customized automated guided vehicle verifies the effectiveness of the proposed two-stage method.","PeriodicalId":15961,"journal":{"name":"Journal of Global Optimization","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A reformulation-enumeration MINLP algorithm for gas network design 用于燃气管网设计的重构-枚举 MINLP 算法

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-09-03 DOI: 10.1007/s10898-024-01424-x

Yijiang Li, Santanu S. Dey, Nikolaos V. Sahinidis

引用次数: 0

Budget-constrained profit maximization without non-negative objective assumption in social networks 社交网络中无非负目标假设的预算受限利润最大化

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-08-14 DOI: 10.1007/s10898-024-01406-z

Suning Gong, Qingqin Nong, Yue Wang, Dingzhu Du

{"title":"Budget-constrained profit maximization without non-negative objective assumption in social networks","authors":"Suning Gong, Qingqin Nong, Yue Wang, Dingzhu Du","doi":"10.1007/s10898-024-01406-z","DOIUrl":"https://doi.org/10.1007/s10898-024-01406-z","url":null,"abstract":"In this paper, we study the budget-constrained profit maximization problem with expensive seed endorsement, a derivation of the well-studied influence maximization and profit maximization in social networks. While existing research requires the non-negativity of the objective profit function, this paper considers real-world scenarios where costs may surpass revenue. Specifically, our problem can be regarded as maximizing the difference between a non-negative submodular function and a non-negative modular function under a knapsack constraint, allowing for negative differences. To tackle this challenge, we propose two algorithms. Firstly, we employ a twin greedy and enumeration technique to design a polynomial-time algorithm with a quarter weak approximation ratio, providing a balance between computational efficiency and solution quality. Then, we incorporate a threshold decreasing technique to enhance the time complexity of the first algorithm, yielding an improved computational efficiency while maintaining a reasonable level of solution accuracy. To our knowledge, this is the first paper to study the profit maximization beyond non-negativity and to propose polynomial-time algorithms with a constant bicriteria approximation ratio.","PeriodicalId":15961,"journal":{"name":"Journal of Global Optimization","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A two-phase sequential algorithm for global optimization of the standard quadratic programming problem 标准二次编程问题全局优化的两阶段顺序算法

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-08-12 DOI: 10.1007/s10898-024-01423-y

Joaquim Júdice, Valentina Sessa, Masao Fukushima

{"title":"A two-phase sequential algorithm for global optimization of the standard quadratic programming problem","authors":"Joaquim Júdice, Valentina Sessa, Masao Fukushima","doi":"10.1007/s10898-024-01423-y","DOIUrl":"https://doi.org/10.1007/s10898-024-01423-y","url":null,"abstract":"We introduce a new sequential algorithm for the Standard Quadratic Programming Problem (StQP), which exploits a formulation of StQP as a Linear Program with Linear Complementarity Constraints (LPLCC). The algorithm is finite and guarantees at least in theory a (delta )-approximate global minimum for an arbitrary small (delta ), which is a global minimum in practice. The sequential algorithm has two phases. In Phase 1, Stationary Points (SP) with strictly decreasing objective function values are computed. Phase 2 is designed for giving a certificate of global optimality for the last SP computed in Phase 1. Two different Nonlinear Programming Formulations for LPLCC are proposed for each one of these phases, which are solved by efficient enumerative algorithms. New procedures for computing a lower bound for StQP are also proposed, which are easy to implement and give tight bounds in general. Computational experiments with a number of test problems from known sources indicate that the two-phase sequential algorithm is, in general, efficient in practice. Furthermore, the algorithm seems to be an efficient way to study the copositivity of a matrix by exploiting an StQP with this matrix.","PeriodicalId":15961,"journal":{"name":"Journal of Global Optimization","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The limitation of neural nets for approximation and optimization 神经网络在近似和优化方面的局限性

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-08-09 DOI: 10.1007/s10898-024-01426-9

T. Giovannelli, O. Sohab, L. N. Vicente

{"title":"The limitation of neural nets for approximation and optimization","authors":"T. Giovannelli, O. Sohab, L. N. Vicente","doi":"10.1007/s10898-024-01426-9","DOIUrl":"https://doi.org/10.1007/s10898-024-01426-9","url":null,"abstract":"We are interested in assessing the use of neural networks as surrogate models to approximate and minimize objective functions in optimization problems. While neural networks are widely used for machine learning tasks such as classification and regression, their application in solving optimization problems has been limited. Our study begins by determining the best activation function for approximating the objective functions of popular nonlinear optimization test problems, and the evidence provided shows that ReLU and SiLU exhibit the best performance on both training and testing data. We then analyze the accuracy of function value, gradient, and Hessian approximations for such objective functions obtained through interpolation/regression models and neural networks. When compared to interpolation/regression models, neural networks can deliver competitive zero- and first-order approximations (at a high training cost) but underperform on second-order approximation. However, it is shown that combining a neural net activation function with the natural basis for quadratic interpolation/regression can waive the necessity of including cross terms in the natural basis, leading to models with fewer parameters to determine. Lastly, we provide evidence that the performance of a state-of-the-art derivative-free optimization algorithm can hardly be improved when the gradient of an objective function is approximated using any of the surrogate models considered, including neural networks.","PeriodicalId":15961,"journal":{"name":"Journal of Global Optimization","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Relaxed projection methods for solving variational inequality problems 解决变分不等式问题的松弛投影法

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-08-08 DOI: 10.1007/s10898-024-01398-w

Pham Ngoc Anh

引用次数: 0