Iteratively Reweighted Least Squares (IRLS)

Iteratively Reweighted Least Squares (IRLS) is a method used to solve complex optimization problems by repeatedly adjusting weights assigned to data points. At each step, it solves a weighted least squares problem, giving different importance to points based on current estimates. This process refines the solution iteratively until it converges to an optimal fit. Think of it as piecing together a puzzle, where each step emphasizes certain parts more, helping to handle problems like outliers or specific error criteria more effectively than standard methods. IRLS is widely used in statistical modeling and machine learning for robust and efficient solutions.