Quadratic convergence refers to a situation in numerical optimization where the sequence of iterates generated by an algorithm converges to a solution at a rate proportional to the square of the distance to the solution. This means that once the iterates are sufficiently close to the solution, the number of correct digits approximately doubles with each iteration. This fast rate of convergence is particularly significant in optimization methods as it indicates efficiency and effectiveness in finding solutions.
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