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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… Click to show full 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 convergence analysis for general descent methods with fixed step sizes is presented. It covers general variable metric methods, gradient-related search directions under angle and scaling conditions, as well as inexact gradient methods. In all cases, optimal rates are obtained.
Keywords:
convergence;
descent;
convergence rate;
convex functions;
strongly convex;
smooth strongly
Journal Title: Journal of Optimization Theory and Applications
Year Published: 2022
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Journal Title: Journal of Optimization Theory and Applications
Year Published: 2022
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!