Data Science Numerical Analysis
Superlinear convergence refers to a type of convergence in numerical methods where the error decreases at a rate faster than linear convergence, typically characterized by the fact that the error reduces significantly as iterations proceed. This is important because it indicates that the method is becoming increasingly efficient in approaching the solution, especially when close to the solution. This concept is crucial when analyzing the performance and efficiency of various algorithms, especially in optimization techniques.
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