Answers.Body: <p>The difference between a local search algorithm (like beam search) and a complete search algorithm (like A*) is, for the most part, small.</p>
  
  <p>Local search algorithms will <strong>not</strong> always find the correct or optimal solution, if one exists. For example, with beam search (excluding an infinite beam width), it sacrifices completeness for greater efficiency by ordering partial solutions by some heuristic predicting how close a partial solution is to a complete one. Beam search is a greedy algorithm.</p>
  
  <p>Complete search algorithms <strong>will</strong> always find the correct or optimal solution if there is one, given enough time. An algorithm like A* uses heuristics to prune the tree as it goes along, but it will not converge to a sub-optimal solution. In a lot of practical cases, this is inefficient, but how much so is problem dependent.</p>
  
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Answers.CreationDate: 2017-09-23T20:56:12.350Z
Answers.LastActivityDate: 2019-05-02T13:59:14.627Z
Answers.LastEditDate: 2019-05-02T13:59:14.627Z
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Body: <p>What is the difference between local search and global (or complete) search algorithms?</p>
  
Tags: <algorithm><search><definitions><comparison><local-search>
Title: What is the difference between local search and global search algorithms?
UserDisplayName: Jayani Sumudini
ThreadText: # What is the difference between local search and global search algorithms?
  
  (Asked by: username_0 on 2017-09-23)
  
  What is the difference between local search and global (or complete) search algorithms?
  
  **Tags:** algorithm, search, definitions, comparison, local-search
  
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  ## 1 answers
  
  ### Answer by username_1 on 2017-09-23. Score: 4 | Accepted answer
  
  The difference between a local search algorithm (like beam search) and a complete search algorithm (like A\*) is, for the most part, small.
  
  Local search algorithms will **not** always find the correct or optimal solution, if one exists. For example, with beam search (excluding an infinite beam width), it sacrifices completeness for greater efficiency by ordering partial solutions by some heuristic predicting how close a partial solution is to a complete one. Beam search is a greedy algorithm.
  
  Complete search algorithms **will** always find the correct or optimal solution if there is one, given enough time. An algorithm like A\* uses heuristics to prune the tree as it goes along, but it will not converge to a sub-optimal solution. In a lot of practical cases, this is inefficient, but how much so is problem dependent.
  
  
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