Nonconvexity is a usually overlooked factor in economic dispatch (ED). Enhancing the nonconvexity of the objective function leads traditional convex optimization algorithms easily to fall into the local optimum. To ad...
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Nonconvexity is a usually overlooked factor in economic dispatch (ED). Enhancing the nonconvexity of the objective function leads traditional convex optimization algorithms easily to fall into the local optimum. To address the above problem, a penalty removal search algorithm (PRSA) is proposed for ED nonconvex optimization. It is composed of two distributed optimization algorithms embedded in a reinforcement learning framework. In Phase I of PRSA, a distributed optimization algorithm with projection operators is designed. It uses fewer variables to locate the region where the optimal solution belongs by the cooperative Q-learning. In Phase II of PRSA, the sigmoid function serves as a penalty function to form the second distributed optimization algorithm. This is used to skip the searched solutions and allow the algorithm to continue searching for more feasible solutions. The PRSA solves the problem that the algorithm misses feasible solutions when the nonconvex coefficients increase. Finally, the effectiveness of the PRSA is verified by numerical examples.
Increasing biological research indicates that the expression levels of circRNAs fluctuate during the onset of various diseases, making them potential biomarkers for multiple conditions. Although numerous artificial in...
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Increasing biological research indicates that the expression levels of circRNAs fluctuate during the onset of various diseases, making them potential biomarkers for multiple conditions. Although numerous artificial intelligence-based computational methods are currently employed for circRNA-disease associations prediction, these methods often rely on a single objective function, which can lead to suboptimal prediction accuracy. To date, no method has designed a set of multi-objective functions specifically for the circRNA-disease prediction problem and optimized it using a non-dominated sorting genetic algorithm. This paper introduces a novel approach by utilizing multi-objective functions and an improved non-dominated sorting genetic algorithm (ICDNSGA) to identify potential associations of circRNA-disease. The method constructs a solution space through matrix factorization and network community characteristics, designing four distinct objective functions optimized via the enhanced multi-objective non-dominated sorting genetic algorithm. ICDNSGA incorporates a population-based adaptive normalization strategy, improving algorithm convergence and solution diversity. Experimental results show that ICDNSGA outperforms pure matrix factorization methods, non-dominated sorting genetic algorithms and other machine learning techniques in predictive performance. Additionally, the prediction results can be validated through existing research and biological analyses, underscoring ICDNSGA’s potential as a valuable tool for biomedical experimentation.
Witzgall [8], commenting on the gradient projection methods of R. Frisch and J. B. Rosen, states: "More or less all algorithms for solving the linear programming problem are known to be modifications of an algori...
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We study two-sided many-to-one matching markets with transferable utilities in which money can exchange hands between matched agents, subject to distributional constraints on the set of feasible allocations. In such m...
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In recent times, the advancement in network devices has focused entirely on the miniaturization of services that should ensure better connectivity between them via fifth generation (5G) technology. The 5G network comm...
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In recent times, the advancement in network devices has focused entirely on the miniaturization of services that should ensure better connectivity between them via fifth generation (5G) technology. The 5G network communication aims to improve Quality of Service (QoS). However, the allocation of resources is a core problem that increases the complexity of packet scheduling. In this paper, a resource allocation model is developed using a novel deep learning algorithm for optimal resource allocation. The novel deep learning is formulated using the constraints associated with optimal radio resource allocation. The objective function design aims at reducing the system delay. The study predicts the traffic in a complex environment and allocates resources accordingly. The simulation was conducted to test the scheduling efficacy and the results showed an improved rate of allocation than the other methods.
There are numerous combinatorial algorithms for classical min-cost flow problems and their simpler variants like max flow or shortest path problems. It is well-known that many of these algorithms are related to the Si...
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There are numerous combinatorial algorithms for classical min-cost flow problems and their simpler variants like max flow or shortest path problems. It is well-known that many of these algorithms are related to the Simplex method and the more general circuit augmentation schemes: prime examples are the network Simplex method, a refinement of the primal Simplex method, and min-mean cycle canceling, which corresponds to a steepest-descent circuit augmentation scheme. We are interested in a deeper understanding of the relationship between circuit augmentation and combinatorial network flows algorithms. To this end, we generalize from primal flows to so-called pseudoflows, which adhere to arc capacities but allow for a violation of flow balance. We introduce ‘pseudoflow polyhedra,’ wherein slack variables are used to quantify this violation, and characterize their circuits. This enables the study of combinatorial network flows algorithms in view of the walks they trace in these polyhedra, and the pivot rules for the steps. In doing so, we provide an ‘umbrella,’ a general framework, that captures several algorithms. We show that the Successive Shortest Path Algorithm for min-cost flow problems, the Shortest Augmenting Path Algorithm for max flow problems, and the Preflow-Push algorithm for max flow problems lead to (non-edge) circuit walks in these polyhedra. The former two are replicated by circuit augmentation schemes for simple pivot rules. Further, we show that the Hungarian Method leads to an edge walk and is replicated, equivalently, as a circuit augmentation scheme or a primal Simplex run for a simple pivot rule.
Generative diffusion models, famous for their performance in image generation, are useful in various cross-domain applications. However, their use in the communication community has been mostly limited to auxiliary ta...
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Generative diffusion models, famous for their performance in image generation, are useful in various cross-domain applications. However, their use in the communication community has been mostly limited to auxiliary tasks like data modeling and feature extraction. These models hold greater promise for fundamental problems in network optimization compared to traditional machine learning methods. Discriminative deep learning often falls short due to its single-step input-output mapping and lack of global awareness of the solution space, especially given the complexity of network optimization's objective functions. In contrast, generative diffusion models can consider a broader range of solutions and exhibit stronger generalization by learning parameters that describe the distribution of the underlying solution space, with higher probabilities assigned to better solutions. We propose a new framework, diffusion model-based solution generation (DiffSG), which leverages the intrinsic distribution learning capabilities of generative diffusion models to learn high-quality solution distributions based on given inputs. The optimal solution within this distribution is highly probable, allowing it to be effectively reached through repeated sampling. We validate the performance of DiffSG on several typical network optimization problems, including mixed-integer non-linear programming, convex optimization, and hierarchical non-convex optimization. Our results demonstrate that DiffSG outperforms existing baseline methods not only on in-domain inputs but also on out-of-domain inputs. In summary, we demonstrate the potential of generative diffusion models in tackling complex network optimization problems and outline a promising path for their broader application in the communication community. Our code is available at https://***/qiyu3816/DiffSG.
Changes in electric network topology, such as line outages, alter fault currents, potentially causing mis-coordination among protective relays. This paper presents an adaptive protection scheme for coordinating overcu...
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This paper studies the distributed optimization problem over directed networks with noisy information-sharing. To resolve the imperfect communication issue over directed networks, a series of noise-robust variants of ...
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In this paper, we first propose a perturbation procedure for achieving dual feasibility, which starts with any basis without introducing artificial variables. This procedure and the dual simplex method are then incorp...
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In this paper, we first propose a perturbation procedure for achieving dual feasibility, which starts with any basis without introducing artificial variables. This procedure and the dual simplex method are then incorporated into a general purpose algorithm; then, a modification of it using a perturbation technique is made in order to handle highly degenerate problems efficiently. Some interesting theoretical results are presented. Nmerical results obtained are reported, which are very encouraging though still preliminary.
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