Summary of Minimal Conditions For Beneficial Neighbourhood Search and Local Descent, by Mark G. Wallace
Minimal Conditions for Beneficial Neighbourhood Search and Local Descent
by Mark G. Wallace
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the conditions under which neighbourhood locality and reduced cost probability towards the optimum support the finding of an improving solution through local search. It introduces a proof demonstrating the superiority of neighbourly search over blind search in achieving this goal, and applies these concepts to satisfiability problem classes and travelling salesman problems. The authors also propose a combination of blind search and local descent, called local blind descent, and prove that its expected number of steps to reach a better cost is lower than with blind search under certain conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how neighbourhoods can help find the best solution by searching nearby. It shows that if the neighbourhood has some special properties, like being close together and having a low chance of finding an even better solution near the best one, then searching just within this neighbourhood is more likely to find a better solution than just randomly searching anywhere. This idea is tested on two different types of problems: ones where you want to make sure all conditions are met, and ones where you have to travel between places. The authors also suggest a new way of combining these two approaches, called local blind descent, which can be used to find the best solution by switching from random search to targeted search when getting close to the optimal result. |
Keywords
» Artificial intelligence » Probability
