Parameter tuning is the process of optimizing the parameters of a model to improve its performance on a specific task. In the context of optimization methods, particularly limited-memory quasi-Newton methods, parameter tuning is crucial because it helps in balancing convergence speed and computational efficiency. This practice ensures that algorithms not only find solutions effectively but also do so within acceptable time limits and resource usage.
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