A local search algorithm iteratively improves a solution by exploring neighboring configurations within a defined search space. This algorithm starts with an initial solution and repeatedly moves to a better adjacent solution. It continues until no further improvement can be made within the immediate vicinity. Such algorithms are commonly used for optimization problems.
Context
While a general computer science concept, local search algorithms can find application in optimizing various processes within blockchain systems, such as transaction batching or resource allocation for validators. News might touch upon these algorithms when discussing efficiency improvements in protocol design or mining pool strategies. Their utility lies in finding efficient solutions, even if not globally optimal, for computationally intensive tasks.
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