Deep Learning for Optimization (Feedforward Neural Network):

sphere function is tested

Deep learning algorithms are widely used for complex optimization tasks, particularly when the problem involves large amounts of data and complex patterns. These algorithms can be used in optimization tasks in areas such as function approximation, classification, and regression. The most common deep learning architectures for optimization are Feedforward Neural Networks (FNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs).

In this implementation, we'll focus on a Feedforward Neural Network (FNN) for solving an optimization problem. The goal of this example is to minimize a simple objective function (e.g., the Sphere function) using deep learning.