Optimal Individual Convergence Rate of Heavy-Ball Based Momentum Methods Based on Adaptive Step-Size Strategy
HUANG Jianzhi1, LONG Sheng1, TAO Qing1
1. Department of Information Engineering, Chinese Academy of People's Liberation Army Artillery Air Defense Academy, Hefei 230031 terov Accelerated Adaptive Moment Estimation
Abstract Optimization techniques, including adaptive step-size and momentum, are both utilized in AMSGrad. Compared with the adaptive step-size algorithms, there is one more logarithm factor in AMSGrad for convergence analysis. To solve the problem, the non-smooth convex problems are studied in this paper. By selecting the time-varying step-size and momentum parameters correctly, it is proved that the Heavy-ball-based momentum methods with adaptive step-size obtain the optimal individual convergence rate. It is indicated that the Heavy-ball-based momentum methods with adaptive step-size hold the advantages in both adaptation and acceleration. Hinge loss problem under L1-norm constraint is solved to verify the correctness of theoretical analysis.
Corresponding Authors:
TAO Qing, Ph.D., professor. His research interests include pa-ttern recognition, machine learning and applied mathematics.
About author:: HUANG Jianzhi, master student. His research interests include convex optimization algorithm and its application in machine lear-ning.LONG Sheng, master student. His research interests include convex optimization algorithm and its application in machine lear-ning.
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2021, Vol. 34 
