Linear convergence refers to a property of iterative optimization algorithms where the sequence of iterates converges to the optimal solution at a linear rate. This means that the error in the approximation of the optimal solution decreases by a constant factor in each iteration. In optimization, understanding linear convergence is essential because it provides insights into the efficiency and speed at which algorithms, like gradient methods, approach the solution.
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