# POGD: Gradient Descent with New Stochastic Rules

Source: [POGD: Gradient Descent with New Stochastic Rules](<https://www.sota2.com/research/paper/68fa6dba1e209131cea3b9c1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:41:22.038Z

Authors: Feihu Han, Sida Xing, Sui Yang Khoo

Published: 2022-10-15T12:31:02.000Z

## Abstract

There introduce Particle Optimized Gradient Descent (POGD), an algorithm based on the gradient descent but integrates the particle swarm optimization (PSO) principle to achieve the iteration. From the experiments, this algorithm has adaptive learning ability. The experiments in this paper mainly focus on the training speed to reach the target value and the ability to prevent the local minimum. The experiments in this paper are achieved by the convolutional neural network (CNN) image classification on the MNIST and cifar-10 datasets.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2210.10654>)

[PDF](<https://arxiv.org/pdf/2210.10654.pdf>)