We propose an exact method that finds a minimum complete Pareto front of the biobjective minimum length minimum risk spanning trees problem. The method consists in two algorithms. The first algorithm finds a single mi...
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In recent years, fog computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited Internet of Things (IoT) devices. The main feature of ...
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In recent years, fog computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited Internet of Things (IoT) devices. The main feature of fog computing is to push computation, networking, and storage facilities closer to the network edge. This enables IoT user equipment (UE) to profit from the fog computing paradigm by mainly offloading their intensive computation tasks to fog resources. Thus, computation offloading and service placement mechanisms can overcome the resource constraints of IoT devices, and improve the system performance in terms of increasing battery lifetime of UE and reducing the total delay. In this paper, we survey the current research conducted on computation offloading and service placement in fog computing-based IoT in a comparative manner.
The previously proposed Newton observer for nonlinear systems has fast exponential convergence and applies to a wide class of problems. However, the Newton observer lacks robustness against measurement noise due to th...
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ISBN:
(纸本)9798350328066
The previously proposed Newton observer for nonlinear systems has fast exponential convergence and applies to a wide class of problems. However, the Newton observer lacks robustness against measurement noise due to the computation of a matrix inverse. In this paper, we propose a novel observer for discrete-time system with sampled measurements to alleviate the impact of measurement noise. The key to the proposed observer is an iterative pre-conditioning technique for the gradient-descent method, used previously for solving general optimization problems. The proposed observer utilizes a nonsymmetric pre-conditioner to approximate the observability mapping's inverse Jacobian so that it asymptotically replicates the Newton observer with an additional benefit of enhanced robustness against measurement noise. Our observer applies to a wide class of nonlinear systems, as it is not contingent upon linearization or any specific structure in the plant nonlinearity. Its improved robustness compared to the prominent nonlinear observers is demonstrated through empirical results.
To address the issue of the JAYA algorithm becoming stuck in suboptimal solutions, this paper introduces the Lévy flight method and proposes a novel CLJAYA-LF algorithm. This new approach integrates the Lévy...
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We analyze a tree search problem with an underlying Markov decision process, in which the goal is to identify the best action at the root that achieves the highest cumulative reward. We present a new tree policy that ...
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We analyze a tree search problem with an underlying Markov decision process, in which the goal is to identify the best action at the root that achieves the highest cumulative reward. We present a new tree policy that optimally allocates a limited computing budget to maximize a lower bound on the probability of correctly selecting the best action at each node. Compared to widely used upper confidence bound (UCB) tree policies, the new tree policy presents a more balanced approach to manage the exploration and exploitation tradeoff when the sampling budget is limited. Furthermore, UCB assumes that the support of reward distribution is known, whereas our algorithm relaxes this assumption. Numerical experiments demonstrate the efficiency of our algorithm in selecting the best action at the root.
Recently, accelerated algorithms using the anchoring mechanism for minimax optimization and fixed-point problems have been proposed, and matching complexity lower bounds establish their optimality. In this work, we pr...
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Recently, accelerated algorithms using the anchoring mechanism for minimax optimization and fixed-point problems have been proposed, and matching complexity lower bounds establish their optimality. In this work, we present the surprising observation that the optimal acceleration mechanism in minimax optimization and fixed-point problems is not unique. Our new algorithms achieve exactly the same worst-case convergence rates as existing anchor-based methods while using materially different acceleration mechanisms. Specifically, these new algorithms are dual to the prior anchor-based accelerated methods in the sense of H-duality. This finding opens a new avenue of research on accelerated algorithms since we now have a family of methods that empirically exhibit varied characteristics while having the same optimal worst-case guarantee. Copyright 2024 by the author(s)
The Wei-Yao-Liu (WYL) Conjugate Gradient (CG) algorithm exhibits favourable attributes, notably sufficient descent and trust domain characteristics, in the context of solving unconstrained optimization problems. The e...
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We present a new algorithm based on posterior sampling for learning in Constrained Markov Decision Processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achieves near-optimal regret bounds while...
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We present a new algorithm based on posterior sampling for learning in Constrained Markov Decision Processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achieves near-optimal regret bounds while being advantageous empirically compared to the existing algorithms. Our main theoretical result is a Bayesian regret bound for each cost component of Õ(DS√AT) for any communicating CMDP with S states, A actions, and diameter D. This regret bound matches the lower bound in order of time horizon T and is the best-known regret bound for communicating CMDPs achieved by a computationally tractable algorithm. Empirical results show that our posterior sampling algorithm outperforms the existing algorithms for constrained reinforcement learning. Copyright 2024 by the author(s)
Incomplete data poses challenges in accurately assessing structural health and detecting damage. It limits the ability to capture the complete behavior and response of the structure, which may hinder the identificatio...
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Incomplete data poses challenges in accurately assessing structural health and detecting damage. It limits the ability to capture the complete behavior and response of the structure, which may hinder the identification and localization of potential damage or anomalies. Addressing the issue of incomplete data requires developing strategies and algorithms that can effectively handle missing or limited measurements. Using incomplete and noisy measurements, we propose an optimization-based damage detection method for laminated composite plates with closely-spaced eigenvalues. The proposed method consists of two stages. In the first stage, the most probable defective elements are identified by utilizing condensed mode shapes as incomplete noisy inputs for modal residual vectors. This approach significantly reduces the computational effort for damage estimation. The second stage introduces an objective function based on incomplete and noisy Condensed Frequency Response Functions (CFRFs). To optimize the damage quantification, the Improved Particle Swarm optimization (IPSO) algorithm is employed to minimize errors in the proposed objective function based on CFRFs of damaged and intact laminates. The proposed method is demonstrated on laminated composite plates with different lamination schemes, ply orientations, and multiple damaged elements in different damage scenarios. By evaluating the method on numerical results and comparing it with previous studies, its superiority is demonstrated. Furthermore, the proposed method exhibits robustness to changes in mass distribution in the system investigated by retrofitting extra masses to the plate structures that lead to worsening closely-spaced eigenvalues.
Robust optimization (RO) is one of the key paradigms for solving optimization problems affected by uncertainty. Two principal approaches for RO, the robust counterpart method and the adversarial approach, potentially ...
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