A Swarm Variant for the Schr\"odinger Solver

• URL: http://arxiv.org/abs/2104.04795v1
• Date: Sat, 10 Apr 2021 15:51:36 GMT
• Title: A Swarm Variant for the Schr\"odinger Solver
• Authors: Urvil Nileshbhai Jivani, Omatharv Bharat Vaidya, Anwesh Bhattacharya, Snehanshu Saha
• Abstract summary: This paper introduces application of the Exponentially Averaged Momentum Particle Swarm Optimization (EM-PSO) as a derivative-free derivative for Neural Networks. It adopts PSO's major advantages such as search space exploration and higher robustness endowed to local minima compared to gradient-descents such as Adam.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces application of the Exponentially Averaged Momentum Particle Swarm Optimization (EM-PSO) as a derivative-free optimizer for Neural Networks. It adopts PSO's major advantages such as search space exploration and higher robustness to local minima compared to gradient-descent optimizers such as Adam. Neural network based solvers endowed with gradient optimization are now being used to approximate solutions to Differential Equations. Here, we demonstrate the novelty of EM-PSO in approximating gradients and leveraging the property in solving the Schr\"odinger equation, for the Particle-in-a-Box problem. We also provide the optimal set of hyper-parameters supported by mathematical proofs, suited for our algorithm.

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