Coordinate Descent ist ein iterativen Optimierungsalgorithmen used to minimize a multi-variable function. Instead of optimizing all variables simultaneously, it focuses on one variable at a time while keeping the others fixed. This approach simplifies the Optimierungsprozess, making it computationally efficient, particularly for high-dimensional problems.
The algorithm begins by selecting a coordinate (variable) and optimizing it, typically using techniques like Gradientenabstieg or line search. Once the optimal value for that coordinate is found, the algorithm moves on to the next coordinate. This process is repeated until convergence criteria are met, such as a specified number of iterations or when changes in function values fall below a defined threshold.
Coordinate Descent is particularly useful in scenarios where variables are independent or weakly correlated, as it can lead to faster convergence compared to more complex optimization methods. However, it may struggle with highly correlated variables, as the optimization of one variable can significantly affect the others. Variants of Coordinate Descent, such as stochastic coordinate descent, introduce randomness into the selection of coordinates, which can enhance performance in certain applications.
Diese Technik ist weit verbreitet im maschinellen Lernen, especially for training models involving large datasets and numerous features, where traditional optimization methods may be computationally prohibitive.