Local convergence refers to the behavior of an iterative algorithm that approaches a solution in the vicinity of an initial guess. This concept is crucial as it indicates how well an algorithm can find a solution close to an initial estimate, which is particularly relevant in optimization methods. Understanding local convergence helps identify the effectiveness and efficiency of algorithms, especially when working with modified approaches that adapt existing methods to improve performance.
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