Consensus Based Optimization

Consensus-based optimization (CBO) is a multi-particle optimization technique aiming to find global optima of complex, often non-convex, objective functions by iteratively averaging the solutions of multiple independent agents. Current research focuses on establishing theoretical convergence guarantees for CBO, particularly in the context of federated learning and applications like Generative Adversarial Networks (GANs), often leveraging mean-field analysis and connections to stochastic gradient descent. This approach offers advantages in handling high-dimensional, non-smooth problems and distributed settings, impacting fields like machine learning and signal processing by providing efficient and robust optimization methods.