Description: Block Coordinate Descent is an optimization algorithm that seeks to minimize an objective function by iteratively optimizing over subsets of variables. Unlike traditional coordinate descent, which updates one variable at a time, this approach allows for multiple variables to be updated simultaneously, potentially leading to faster and more efficient convergence. This method is particularly useful in optimization problems where variables can be logically grouped, enabling the algorithm to explore the solution space more effectively. The technique is based on the idea that by optimizing a block of variables, interactions between them can be captured that might be lost if only one variable were optimized at a time. This makes it a valuable tool in the field of optimization, where the goal is to adjust multiple parameters of a model simultaneously to improve its performance. Its relevance has increased with the rise of machine learning and automated model tuning (AutoML), where efficiency in searching for optimal solutions is crucial for the success of predictive models.
