Results 21 to 30 of about 522,654 (284)
IEEE Access, 2018 Reinforcement learning (RL) is an important machine learning paradigm that can be used for learning from the data obtained by the human-computer interface and the interaction in human-centered smart systems. One of the essential problems in RL algorithms
Dazi Li +3 more
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A sample decreasing threshold greedy-based algorithm for big data summarisation
Journal of Big Data, 2021 As the scale of datasets used for big data applications expands rapidly, there have been increased efforts to develop faster algorithms. This paper addresses big data summarisation problems using the submodular maximisation approach and proposes an ...
Teng Li +2 more
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Machines, 2021 In this paper, we consider the problem of selecting the most efficient optimization algorithm for neural network approximation—solving optimal control problems with mixed constraints.
Irina Bolodurina, Lyubov Zabrodina
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Greedy algorithms for dirac mixture approximation of arbitrary probability density functions
2007 46th IEEE Conference on Decision and Control, 2007 Greedy procedures for suboptimal Dirac mixture approximation of an arbitrary probability density function are proposed, which approach the desired density by sequentially adding one component at a time. Similar to the batch solutions proposed earlier, a distance measure between the corresponding cumulative distributions is minimized by selecting the ...
Hanebeck, Uwe D., Schrempf, Oliver C.
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Entropy, 2021 Estimation of the probability density function from the statistical power moments presents a challenging nonlinear numerical problem posed by unbalanced nonlinearities, numerical instability and a lack of convergence, especially for larger numbers of ...
Nives Brajčić Kurbaša +3 more
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A consistent approximation of the total perimeter functional for topology optimization algorithms [PDF]
ESAIM: Control, Optimisation and Calculus of Variations, 2022 This article revolves around the total perimeter functional, one particular version of the perimeter of a shape Ω contained in a fixed computational domainDmeasuring the total area of its boundary∂Ω, as opposed to its relative perimeter, which only takes into account the regions of∂Ω strictly insideD.
Amstutz, Samuel +2 more
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Efficient by Precision Algorithms for Approximating Functions from Some Classes by Fourier Series
Кібернетика та комп'ютерні технологіїIntroduction. The problem of approximation can be considered as the basis of computational methods, namely, the approximation of individual functions or classes of functions by functions that are in some sense simpler than the functions being ...
Olena Kolomys
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A Model for Demand Planning in Supply Chains with Congestion Effects
Logistics, 2021 This paper is concerned with demand planning for internal supply chains consisting of workstations, production facilities, warehouses, and transportation links.
Uday Venkatadri +2 more
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Randomized Strategies for Robust Combinatorial Optimization
, 2018 In this paper, we study the following robust optimization problem. Given an independence system and candidate objective functions, we choose an independent set, and then an adversary chooses one objective function, knowing our choice. Our goal is to find
Kawase, Yasushi, Sumita, Hanna
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Algorithms for DC Programming via Polyhedral Approximations of Convex Functions
, 2023 There is an existing exact algorithm that solves DC programming problems if one component of the DC function is polyhedral convex (Loehne, Wagner, 2017). Motivated by this, first, we consider two cutting-plane algorithms for generating an $ε$-polyhedral underestimator of a convex function g.
Pirani, Fahaar Mansoor, Ulus, Firdevs
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