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  • Search: subject:"Stochastic gradient scheme"
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Year of publication
Subject
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Backtracking line search 4 Constant step size 4 Convergence analysis 4 Step size rule 4 Stochastic gradient scheme 4
Online availability
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Free 4
Type of publication
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Article 4
Type of publication (narrower categories)
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Article 4
Language
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English 4
Author
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Grieshammer, Max 4 Pflug, Lukas 4 Stingl, Michael 4 Uihlein, Andrian 4
Published in...
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Computational Optimization and Applications 4
Source
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EconStor 4
Showing 1 - 4 of 4
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The continuous stochastic gradient method: part II–application and numerics
Grieshammer, Max; Pflug, Lukas; Stingl, Michael; … - In: Computational Optimization and Applications 87 (2023) 3, pp. 977-1008
In this contribution, we present a numerical analysis of the continuous stochastic gradient (CSG) method, including applications from topology optimization and convergence rates. In contrast to standard stochastic gradient optimization schemes, CSG does not discard old gradient samples from...
Persistent link: https://www.econbiz.de/10015323484
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Cover Image
The continuous stochastic gradient method: part I–convergence theory
Grieshammer, Max; Pflug, Lukas; Stingl, Michael; … - In: Computational Optimization and Applications 87 (2023) 3, pp. 935-976
In this contribution, we present a full overview of the continuous stochastic gradient (CSG) method, including convergence results, step size rules and algorithmic insights. We consider optimization problems in which the objective function requires some form of integration, e.g., expected...
Persistent link: https://www.econbiz.de/10015323504
Saved in:
Cover Image
The continuous stochastic gradient method: part I–convergence theory
Grieshammer, Max; Pflug, Lukas; Stingl, Michael; … - In: Computational Optimization and Applications 87 (2023) 3, pp. 935-976
In this contribution, we present a full overview of the continuous stochastic gradient (CSG) method, including convergence results, step size rules and algorithmic insights. We consider optimization problems in which the objective function requires some form of integration, e.g., expected...
Persistent link: https://www.econbiz.de/10015403580
Saved in:
Cover Image
The continuous stochastic gradient method: part II–application and numerics
Grieshammer, Max; Pflug, Lukas; Stingl, Michael; … - In: Computational Optimization and Applications 87 (2023) 3, pp. 977-1008
In this contribution, we present a numerical analysis of the continuous stochastic gradient (CSG) method, including applications from topology optimization and convergence rates. In contrast to standard stochastic gradient optimization schemes, CSG does not discard old gradient samples from...
Persistent link: https://www.econbiz.de/10015403586
Saved in:
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