SPGD algorithm optimization based on Adam optimizer

Zhou Fang; Xiangxiang Xu; Xin Li; Huizhen Yang; Chenglong Gong

doi:10.1117/12.2579991

5 November 2020 SPGD algorithm optimization based on Adam optimizer

Zhou Fang, Xiangxiang Xu, Xin Li, Huizhen Yang, Chenglong Gong

Proceedings Volume 11567, AOPC 2020: Optical Sensing and Imaging Technology; 115672S (2020) https://doi.org/10.1117/12.2579991
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

The SPGD (stochastic parallel gradient descent) is a control algorithm widely used in WFSless (wavefront sensorless) AO (adaptive optics) system.The gain is commonly set to a fixed value in the traditional SPGD algorithm.With the increase of the number of DM (deformable mirror) actuators, the optimization space of the algorithm becomes larger, which can easily lead to the slow convergence speed of the algorithm and the rise of the probability of falling into the local optima. Adam(Adaptive Moment Estimation) optimizer is an optimized stochastic gradient descent algorithm commonly used in deep learning with the advantage of achieving adaptive gain. Wavefront aberrations under different turbulence strength as correction objects, WFSless AO systems are built with 32, 61, 97 and 127 elements DM as wavefront corrector respectively. Results show that the optimized algorithm can converge faster than basic SPGD and the probability of falling into local optima decreases. The system’s convergence speed is increased by about 30%. The probability of falling into local optima is decreased by 29.8% , 30.3% , 32.6% and 35.9% respectively under D/r0=5 and by 28.8% , 30.5% , 33.3% and 34.5% respectively under D/r₀=15. The advantages of the optimized algorithm are more obvious as the number of DM actuators increases. Above results provide a theoretical basis for the practical application of the SPGD algorithm based on Adam optimization.

Citation Download Citation

Zhou Fang, Xiangxiang Xu, Xin Li, Huizhen Yang, and Chenglong Gong "SPGD algorithm optimization based on Adam optimizer", Proc. SPIE 11567, AOPC 2020: Optical Sensing and Imaging Technology, 115672S (5 November 2020); https://doi.org/10.1117/12.2579991

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Adaptive optics

Actuators

Control systems

Wavefronts

Turbulence

Optimization (mathematics)

Stochastic processes

Show All Keywords

Keywords/Phrases

Search In:

Publication Years