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An Improved Adam Optimization Algorithm Combining Adaptive Coefficients and Composite Gradients Based on Randomized

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Area of Science:

  • Computer Science
  • Machine Learning
  • Optimization Algorithms

Background:

  • The standard Adam optimization algorithm faces challenges including slow convergence, suboptimal solutions, and inefficiency with high-dimensional data.
  • Addressing these limitations is crucial for advancing machine learning model performance.

Purpose of the Study:

  • To propose an enhanced Adam optimization algorithm that overcomes the limitations of the original Adam.
  • To improve convergence speed, global optimization accuracy, and computational efficiency.

Main Methods:

  • Introduced adaptive coefficients to adjust gradient deviation and correct search direction.
  • Incorporated a composite gradient by combining current and momentum gradients for enhanced global optimization.
  • Utilized randomized block coordinate descent to determine gradient updates, reducing computational overhead.

Main Results:

  • Demonstrated superior convergence speed and accuracy compared to six other gradient descent methods on classification tasks.
  • Significantly reduced CPU and memory utilization.
  • Achieved high accuracy in reservoir porosity prediction using BP neural networks, with over 86% of data having an absolute error within 0.1%.

Conclusions:

  • The proposed Adam optimization algorithm offers significant improvements in speed, accuracy, and efficiency.
  • The method proves effective for both general machine learning tasks and specific applications like reservoir porosity prediction.
  • The enhanced algorithm presents a robust and computationally efficient alternative for various optimization challenges.