A Note on the Optimal Convergence Rate of Descent Methods with Fixed Step Sizes for Smooth Strongly Convex Functions
Sign Up to like & getrecommendations! 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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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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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