Computing Pseudoinverse Matrices with Single-Layer Neural Networks

This work evaluates numerical results related to single-layer neural networks, which compute pseudoinverse matrices. This is possible due to the gradient descendent algorithms available in the machine learning TensorFlow framework, which optimizes training processes. Linear algebra principles support our formulations, which are proved at the end of the training stage.