Nonlinear Optimization
Grid search is a systematic method for hyperparameter optimization that involves defining a grid of possible values for each hyperparameter and evaluating the performance of a model across all combinations. This technique is widely used in machine learning and nonlinear optimization to identify the best set of parameters that minimize or maximize a certain objective function. The exhaustive nature of grid search makes it a reliable but potentially computationally expensive approach, especially with a large number of hyperparameters or a wide range of values.
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