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   Articles with "strongly convex" as a keyword



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A Note on the Optimal Convergence Rate of Descent Methods with Fixed Step Sizes for Smooth Strongly Convex Functions

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recommendations! Published in 2022 at "Journal of Optimization Theory and Applications"

DOI: 10.1007/s10957-022-02032-z

Abstract: Based on a result by Taylor et al. (J Optim Theory Appl 178(2):455–476, 2018) on the attainable convergence rate of gradient descent for smooth and strongly convex functions in terms of function values, an elementary… read more here.

Keywords: convergence; descent; convergence rate; convex functions ... See more keywords

A dual approach for optimal algorithms in distributed optimization over networks

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recommendations! Published in 2021 at "Optimization Methods and Software"

DOI: 10.1080/10556788.2020.1750013

Abstract: We study dual-based algorithms for distributed convex optimization problems over networks, where the objective is to minimize a sum of functions over in a network. We provide complexity bounds for four different cases, namely: each… read more here.

Keywords: algorithms distributed; algorithms; strongly convex; optimization ... See more keywords

New Inequalities for Strongly r-Convex Functions

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recommendations! Published in 2019 at "Journal of Function Spaces"

DOI: 10.1155/2019/1219237

Abstract: In this study, firstly we introduce a new concept called “strongly r-convex function.” After that we establish Hermite-Hadamard-like inequalities for this class of functions. Moreover, by using an integral identity together with some well known… read more here.

Keywords: new inequalities; convex functions; inequalities strongly; strongly convex ... See more keywords

Quantized ADMM design for distributed strongly convex optimization

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recommendations! Published in 2024 at "Transactions of the Institute of Measurement and Control"

DOI: 10.1177/01423312241297730

Abstract: This paper proposes a novel quantized alternating direction method of multipliers (ADMM) for distributed optimization problems where the strongly convex objective function contains smooth and non-smooth parts. When the objective function is strongly convex and… read more here.

Keywords: quantized admm; admm design; proposed algorithm; strongly convex ... See more keywords

Analysis of a Two-Step Gradient Method with Two Momentum Parameters for Strongly Convex Unconstrained Optimization

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recommendations! Published in 2024 at "Algorithms"

DOI: 10.3390/a17030126

Abstract: The paper is devoted to the theoretical and numerical analysis of the two-step method, constructed as a modification of Polyak’s heavy ball method with the inclusion of an additional momentum parameter. For the quadratic case,… read more here.

Keywords: method; analysis two; parameter; strongly convex ... See more keywords