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aliash98
reviewed
Oct 30, 2021
notebooks/5_local_search/index.md
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| ### 1.1. What is Local Search? | ||
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| Local Search is a heuristic method for solving computationally hard contraint satisfaction or optimization problems. The family of local search methods are typically used is search problems where the search space is either very huge or infinite. In such Problems classical search algorithms do not work efficiently. |
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- constrain not contraint
- P in problems in the last sentence should not be capital, in addition, a comma is required after the "problems"
Generally, I suggest that you check each of your paragraphs in Grammarly or sth like that.
| Local Search is a heuristic method for solving computationally hard contraint satisfaction or optimization problems. The family of local search methods are typically used is search problems where the search space is either very huge or infinite. In such Problems classical search algorithms do not work efficiently. | ||
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| Usually local search is used for problems which have a state as its solution, and for many problems the path to the final solution is irrelevent. The procedure of this method is quite simple, at first the algorithm starts from a solution which may not be optimal, or may not satisfy all the constraints, and by every step or iteration, the algorithm tries to find a slightly better solution. This gradual improvement of the solution is done by examining different neighbors of the current state, and chosing the best neighbor as the next state of the search. | ||
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I extremely recommend you use Grammarly because many errors exist in your text, including comma displacement, misused words, etc.
The Lecture Note starts with a proper explanation, though.
| Usually local search is used for problems which have a state as its solution, and for many problems the path to the final solution is irrelevent. The procedure of this method is quite simple, at first the algorithm starts from a solution which may not be optimal, or may not satisfy all the constraints, and by every step or iteration, the algorithm tries to find a slightly better solution. This gradual improvement of the solution is done by examining different neighbors of the current state, and chosing the best neighbor as the next state of the search. | ||
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| An example of the applications of local search is solving the TSP problem. In each step we may try to replace two edges by two other edges which may result in a shorter cycle in the graph. | ||
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It would be better to mention what TSP stands for. In addition, a helpful link would help the readers get a grasp of the problem.
notebooks/5_local_search/index.md
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| current <- neighbor | ||
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This picture and the following few images do not load properly. Check and see what the problem is.
notebooks/5_local_search/index.md
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| - $4$ when succeeding | ||
| - $3$ when getting stuck | ||
| - Expected total number of moves for an instance: | ||
| - $3\frac{(1-p)}{p} + 4 \approx 22$ moves needs |
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It seems that these equations do not appear correctly. Check for the problem.
notebooks/5_local_search/index.md
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| Probabilty function determines chance of going to new neighbour. The equation has been altered to the following: | ||
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The size of the picture is not convenient. Consider cropping it.
notebooks/5_local_search/index.md
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These images are good but not perfectly adjusted in the preview mode. Check the way all your pictures would look in the preview mode.
notebooks/5_local_search/index.md
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| With the propabilty of P, some genes of new children change. | ||
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Btw, what is this horrible string of characters?
notebooks/5_local_search/index.md
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| - **Negative points:** | ||
| - As shown in figure below, LNS takes longer runtime than hard constraint attitude. | ||
| - It seems that the initial solution found by LNS is very insufficent and moreover maybe the choices for better neighbourhoods are not well enough. It can be better if we combine some atrributes of backtrack and LNS in each step to reach |
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Consider replacing the name of the website and the subject of the article with the raw, messy link.
Readers do not need to know about the URL; a clickable text might suffice.
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| # Local Search | |||
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Well done with your Lecture Note!
I've mentioned a few comments on different parts of the LN. Consider them and apply needed changes to your whole article.
To summarize, + points about your LN are:
- Sufficient coverage of the topic
- Good delivery of concepts
- Enough Images and codes
On the other hand, - points are: - Errors in English writing (like word misuse, punctuation, sentence construction)
- Adjustment of images and formulas
- Quality and organization of pictures (including captions and, in some cases cropping, might help)
aliash98
reviewed
Nov 6, 2021
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| # Local Search | |||
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PS:
- Consider a clickable table of contents
- Consider moving the pictures to center
Fixed grammatical problems.
First image center allignment.
Test
Centralized image.
Table of Contents test.
Changed table of contents.
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