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Multi-point Directional Minimization for Conjugate Gradient Algorithm
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DOI：10.1007/978-3-032-03705-3_2.png)
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Abstract
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The conjugate gradient (CG) algorithm is a widely used approach for training neural networks. Its most computationally demanding step is directional minimization. This paper introduces a novel modification of the CG algorithm that accelerates directional minimization, leading to a significant reduction in computation time. The proposed modification was evaluated on selected test cases, and its performance was compared with the classical CG method.
Keywords:
feedforward neural network
supervised learning
conjugate gradient algorithm
parallel computation
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