Stochastic optimization, also known as stochastic gradient descent (SGD), is a widely-used algorithm for finding approximate solutions to complex optimization problems in machine learning and artificial intelligence (AI). It involves iteratively updating the model parameters by taking small random steps in the direction of the negative gradient of an objective function, which can be estimated using noisy or stochastic samples from the underlying dataset.

• Supply Chain Management
• Financial Portfolio Management
• Energy Systems
• Telecommunications Networks
• Healthcare Management
• Transportation Planning
• Environmental Modeling
• Manufacturing Process Control

For instance, in a neural network training scenario, stochastic optimization can be used to update the weights and biases of the network by taking random samples from the training data and using them to estimate the gradient of the loss function. By doing so, the algorithm can efficiently explore the vast space of possible solutions and find an optimal one.

• Building the data roadmap