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An Adaptive Accelerated Derivative-Free Optimization Algorithm Based on Noncommutative Maps
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DOI：10.1109/TAC.2025.3641250.png)
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Abstract
En 中文
In this article, an adaptive accelerated derivative-free optimization algorithm is developed. A composition of noncommutative maps based on objective function evaluations is used to approximate an accelerated gradient descent algorithm with a momentum term. An adaptive step-size rule and an adaptive momentum term are introduced to improve the algorithm’s performance in terms of convergence speed and steady-state accuracy. Semi-global asymptotic stability of the proposed algorithm is proved for a class of convex objective functions under suitable assumptions. Simulation results are shown and compared to other derivative-free optimization algorithms.
Keywords:
Accelerated gradient methods
derivative-free optimization
extremum seeking (ES)
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W