Splitting algorithms are iterative methods used to solve optimization problems and variational inequalities by breaking down complex tasks into simpler, more manageable subproblems. They leverage the structure of the problem at hand, often separating constraints or components, allowing for a more efficient approach to finding solutions. This technique is particularly useful in scenarios where the original problem can be decomposed, facilitating convergence to a solution through successive approximations.
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