Approximation quality refers to the accuracy and reliability of an approximate solution in numerical methods, particularly in the context of randomization techniques in linear algebra. It indicates how close the approximate solution is to the true or exact solution and is influenced by various factors, including the method used, the dimensionality of the problem, and the characteristics of the data involved. Ensuring high approximation quality is essential for effective and meaningful analysis, especially when dealing with large datasets where exact solutions are computationally expensive or infeasible.
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