Adam SPGD algorithm in freeform surface in-process interferometry

Wu, Jinling; Hu, Chengwen; Liu, Renhu; Wu, Shengyang; Cao, Jiaquan; Cheng, Zhongtao; Yu, Benli; Zhang, Lei

doi:10.1364/oe.470007

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…The reason for choosing the Momentum-SPGD algorithm as a basis of comparison lies in the fact that Momentum-SPGD is not only a typical improvement on conventional SPGD (by introducing momentum components into the iterative procedure) but also a degraded version of CoolMomentum-SPGD (without cooling the momentum components). At the same time, Adam-SPGD is chosen as a benchmark since it is pervasively acknowledged as state-of-the-art [25,27]. Numerical simulations are performed under three different atmospheric turbulence strengths .…”

Section: Resultsmentioning

confidence: 99%

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CoolMomentum-SPGD Algorithm for Wavefront Sensor-Less Adaptive Optics Systems

et al. 2023

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Instead of acquiring the previous aberrations of an optical wavefront with a sensor, wavefront sensor-less (WFSless) adaptive optics (AO) systems compensate for wavefront distortion by optimizing the performance metric directly. The stochastic parallel gradient descent (SPGD) algorithm is pervasively adopted to achieve performance metric optimization. In this work, we incorporate CoolMomentum, a method for stochastic optimization by Langevin dynamics with simulated annealing, into SPGD. Numerical simulations reveal that, compared with the state-of-the-art SPGD variant, the proposed CoolMomentum-SPGD algorithm achieves better convergence speed under various atmospheric turbulence conditions while requiring only two tunable parameters.

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Section: Resultsmentioning

confidence: 99%

“…The effectiveness of these SPGD variants has been verified theoretically and experimentally. Among the adopted optimizers, Adam and its sibling Nadam achieve the best performance [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

CoolMomentum-SPGD Algorithm for Wavefront Sensor-Less Adaptive Optics Systems

et al. 2023

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“…However, the method typically requires the calculation and measurement of the parameters associated with the system model beforehand, which hinders its implementation as compared to the modal-free optimization algorithm. Mountain climbing, simulated annealing, augmented learning, particle swarm, genetic algorithm, and stochastic parallel gradient descent (SPGD) are among the most common modal-free algorithms [20][21][22][23][24][25]. Modal-free optimization algorithms are easier to implement because they do not rely on specific mathematical models.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Optical Closed-Loop Control Based on the Single-Dimensional Perturbation Descent Algorithm

Chen

Zhou

et al. 2023

Sensors

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Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics. In this study, two different algorithms, the single-dimensional perturbation descent algorithm (SDPD) and the second-order stochastic parallel gradient descent algorithm (2SPGD), are proposed for wavefront sensorless adaptive optics, and a theoretical analysis of the algorithms’ convergence rates is presented. The results demonstrate that the single-dimensional perturbation descent algorithm outperforms the stochastic parallel gradient descent (SPGD) and 2SPGD algorithms in terms of convergence speed. Then, a 32-unit deformable mirror is constructed as the wavefront corrector, and the SPGD, single-dimensional perturbation descent, and 2SPSA algorithms are used in an adaptive optics numerical simulation model of the wavefront controller. Similarly, a 39-unit deformable mirror is constructed as the wavefront controller, and the SPGD and single-dimensional perturbation descent algorithms are used in an adaptive optics experimental verification device of the wavefront controller. The outcomes demonstrate that the convergence speed of the algorithm developed in this paper is more than twice as fast as that of the SPGD and 2SPGD algorithms, and the convergence accuracy of the algorithm is 4% better than that of the SPGD algorithm.

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A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations

Han,

Fang,

et al. 2024

Science of The Total Environment

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Adam SPGD algorithm in freeform surface in-process interferometry

Cited by 9 publications

References 25 publications

CoolMomentum-SPGD Algorithm for Wavefront Sensor-Less Adaptive Optics Systems

CoolMomentum-SPGD Algorithm for Wavefront Sensor-Less Adaptive Optics Systems

Adaptive Optical Closed-Loop Control Based on the Single-Dimensional Perturbation Descent Algorithm

A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations

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