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Algorithmic Solution for Systems of Linear Equations, in $\mathcal{O}(mn)$ time
We present a novel algorithm attaining excessively fast, the sought solution of linear systems of equations.
A Gradient Free Neural Network Framework Based on Universal Approximation Theorem
The proposed algorithm adheres to the underlying theory, is highly fast, and results in remarkably low errors when applied for regression and classification of complex data-sets, such as the Griewank function of multiple variables $\mathbf{x} \in \mathbb{R}^{100}$ with random noise addition, and MNIST database for handwritten digits recognition, with $7\times10^4$ images.
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