Results 11 to 20 of about 68,699 (263)
Probabilistic Optimization Techniques in Smart Power System
Energies, 2022 Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy.
Muhammad Riaz +4 more
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A New Stochastic Process of Prestack Inversion for Rock Property Estimation
Applied Sciences, 2022 In order to enrich the current prestack stochastic inversion theory, we propose a prestack stochastic inversion method based on adaptive particle swarm optimization combined with Markov chain Monte Carlo (MCMC).
Long Yin +6 more
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Effects of Probability Function on the Performance of Stochastic Programming [PDF]
Journal of Optimization in Industrial Engineering, 2018 Stochastic programming is a valuable optimization tool where used when some or all of the design parameters of an optimization problem are defined by stochastic variables rather than by deterministic quantities.
Mohammad Ebrahim Karbaschi +1 more
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Energy Reports, 2022 The deployment of a microgrid with renewable energy sources (RESs) generally considers two aspects i.e., the need for the energy storage and the presence of many uncertainties due to the intermittent nature of several RESs and the load demand variability.
Firmansyah Nur Budiman +6 more
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K-adaptability in stochastic optimization [PDF]
Mathematical Programming, 2022 AbstractWe consider stochastic problems in which both the objective function and the feasible set are affected by uncertainty. We address these problems using a K-adaptability approach, in which K solutions for a given problem are computed before the uncertainty dissolves and afterwards the best of them can be chosen for the realized scenario.
Malaguti E., Monaci M., Pruente J.
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Quantitative Stability of Optimization Problems with Stochastic Constraints
Mathematics, 2023 In this paper, we consider optimization problems with stochastic constraints. We derive quantitative stability results for the optimal value function, the optimal solution set and the feasible solution set of optimization models in which the underlying ...
Wei Ouyang, Kui Mei
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Advances in Difference Equations, 2020 The spread of an epidemic diseases is stochastic in nature. It is more realistic to include this stochasticity when modelling the dynamics of a communicable disease.
Kamaleldin Abodayeh +5 more
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A Stochastic Multiobjective Optimization Framework for Wireless Sensor Networks
EURASIP Journal on Wireless Communications and Networking, 2010 In wireless sensor networks (WSNs), there generally exist many different objective functions to be optimized. In this paper, we propose a stochastic multiobjective optimization approach to solve such kind of problem.
Shibo He +5 more
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Optimal Stochastic Planarization [PDF]
2010 IEEE 51st Annual Symposium on Foundations of Computer Science, 2010 It has been shown by Indyk and Sidiropoulos [IS07] that any graph of genus g>0 can be stochastically embedded into a distribution over planar graphs with distortion 2^O(g). This bound was later improved to O(g^2) by Borradaile, Lee and Sidiropoulos [BLS09]. We give an embedding with distortion O(log g), which is asymptotically optimal.
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An Isometric Stochastic Optimizer
CoRR, 2023 The Adam optimizer is the standard choice in deep learning applications. I propose a simple explanation of Adam's success: it makes each parameter's step size independent of the norms of the other parameters. Based on this principle I derive Iso, a new optimizer which makes the norm of a parameter's update invariant to the application of any linear ...
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