Even with favorable policy frameworks, green hydrogen has not been cost-competitive in Europe. Optimizing hydrogen production can reduce the Levelized Costs of Hydrogen (LCOH). In this research, an electrolysis system...
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Disruptions can render parts of the critical transportation systems unavailable, forcing both trains and passengers to adapt. This study addresses the integrated rescheduling problem in a high-speed railway network du...
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作者：
Gondzio, JacekUniv Edinburgh
Sch Math Peter Guthrie Tait Rd Edinburgh EH9 3FD Scotland Univ Edinburgh
Maxwell Inst Math Sci Peter Guthrie Tait Rd Edinburgh EH9 3FD Scotland
Interior point methods (IPMs) have hugely influenced the field of optimization. Their fast development has been triggered by the seminal paper of Narendra Karmarkar published in 1984 which delivered a polynomial algor...
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Interior point methods (IPMs) have hugely influenced the field of optimization. Their fast development has been triggered by the seminal paper of Narendra Karmarkar published in 1984 which delivered a polynomial algorithm for linear programming and suggested that it might be implemented into a very efficient method in practice. Indeed, this has been demonstrated within a few years after 1984 and has gained IPMs a status of exceptionally powerful optimization tool. linear programming (LP) is at the centre of many operational research techniques including mixed-integer programming, network optimization and various decomposition techniques. Therefore, any progress in LP has far-reaching consequences. IPMs certainly did not disappoint in this context: they have become a heavily used methodology in modern optimization and operational research. Their accuracy, efficiency and reliability have been particularly appreciated when IPMs are applied to truly large scale problems which challenge any alternative approaches. In this survey we will discuss several issues related to interior point methods. We will recall techniques which provide the building blocks of IPMs, and observe that actually at least some of them have been developed before 1984. We will briefly comment on the worst-case complexity results for different variants of IPMs and then focus on key aspects of their implementation. We will also address some of the most spectacular features of IPMs and discuss their potential advantages when applied in decomposition algorithms, cutting planes scheme and column generation technique.
Enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communication (URLLC) are two important wireless communication traffics to build a digital twin of physical reality. Therein, eMBB and URLLC traffics are...
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Internet-of-Things-enabled frameworks have eased the development of complex systems, but they throw a significant challenge for efficient resource utilization, thereby improving the system performance. An intelligent ...
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It is of great importance to calculate PV access capacity on different scene and different time division. Kmeans clustering method was used to cluster the scene, time division is carried out. Genetic algorithm is used...
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ISBN:
(数字)9798350389579
ISBN:
(纸本)9798350389586
It is of great importance to calculate PV access capacity on different scene and different time division. Kmeans clustering method was used to cluster the scene, time division is carried out. Genetic algorithm is used to solve the PV access capacity problem. Without network reconfiguration, there are three time divisions that exceed the constraints. Constraint overlimit for three exceedance period can be eliminated by network reconfiguration. The total PV access capacity increases and the active power loss decreases compared with optimization results without network reconfiguration.
With the increasing concern for sustainable agriculture and efficient resource utilization, the study of crop planting strategy optimization under resource constraints has become an important topic. In this paper, an ...
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ISBN:
(数字)9798331533113
ISBN:
(纸本)9798331533120
With the increasing concern for sustainable agriculture and efficient resource utilization, the study of crop planting strategy optimization under resource constraints has become an important topic. In this paper, an integrated framework combining simulation and optimization algorithms is proposed for developing sustainable crop cultivation strategies. A 0–1 linear programming model is constructed to maximize economic returns under resource constraints, taking into account constraints such as land area, crop rotation, and resource allocation. A simulated annealing algorithm was introduced to optimize the minimum planting area allocation of a single plot to ensure the rationality of spatial and temporal distribution. Based on the 2023 data of a rural area in the mountainous region of North China, the optimal planting strategies from 2024 to 2030 were predicted. The results show that the proposed method significantly improves resource utilization efficiency, ensures the stability of economic returns, and flexibly adapts to different resource constraint scenarios. This study provides theoretical support for sustainable agricultural development and useful reference for land use optimization.
This paper presents a new method of sizing the widths of the power and ground routes in integrated circuits so that the chip area required by the routes is minimized subject to electromigration and IR voltage drop con...
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This paper presents a new method of sizing the widths of the power and ground routes in integrated circuits so that the chip area required by the routes is minimized subject to electromigration and IR voltage drop constraints. The basic idea is to transform the underlying constrained nonlinear programming problem into a sequence of linear programs. Theoretically, we show that the sequence of linear programs always converges to the optimum solution of the relaxed convex optimization problem. Experimental results demonstrate that the proposed sequence-of-linear-program method is orders of magnitude faster than the best-known method based on conjugate gradients with constantly better solution qualities.
This paper proposes a two-timescale compressed primal-dual (TiCoPD) algorithm for decentralized optimization with improved communication efficiency over prior works on primal-dual decentralized optimization. The algor...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
This paper proposes a two-timescale compressed primal-dual (TiCoPD) algorithm for decentralized optimization with improved communication efficiency over prior works on primal-dual decentralized optimization. The algorithm is built upon the primal-dual optimization framework and utilizes a majorization-minimization procedure. The latter naturally suggests the agents to share a compressed difference term during the iteration. Furthermore, the TiCoPD algorithm incorporates a fast timescale mirror sequence for agent consensus on nonlinearly compressed terms, together with a slow timescale primal-dual recursion for optimizing the objective function. We show that the TiCoPD algorithm converges with a constant step size. It also finds an $\mathcal{O}(1/T)$ stationary solution after T iterations. Numerical experiments on decentralized training of a neural network validate the efficacy of TiCoPD algorithm.
In recent years, the increasing use of credit cards has led to a dramatic rise in fraudulent transactions. Identifying credit card fraud is crucial for preventing financial institutions and their customers from incurr...
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ISBN:
(数字)9798331508913
ISBN:
(纸本)9798331508920
In recent years, the increasing use of credit cards has led to a dramatic rise in fraudulent transactions. Identifying credit card fraud is crucial for preventing financial institutions and their customers from incurring substantial financial losses and for maintaining customer trust. Fraud detection systems play a key role in preventing unauthorized transactions. Auto-encoder networks, known for their ability to learn compact representations of data, can leverage this capability to identify anomalous transactions, which are typically indicative of fraud. However, their performance is often sensitive to hyperparameter choices, especially in imbalanced datasets. To address this, a genetic algorithm is utilized to dynamically optimize the hyperparameters of these networks, significantly improving fraud detection. This paper proposes using an optimized auto-encoder network, augmented with the genetic algorithm, to detect credit card fraud. On the Credit Card Fraud (CCF) dataset, the model achieved an F-score of 91.04 % and a Matthews correlation coefficient (MCC) of 0.8282, surpassing some baseline methods such as OCSVM, OCNN and COPOD. These results demonstrate the effectiveness of the genetic algorithm in improving the performance and robustness of fraud detection systems.
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