drummyfish/less_retarded_wiki
5
1
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

Update

Browse source
This commit is contained in:
Miloslav Ciz 2023-05-08 15:05:15 +02:00
parent 879a37fd2b
commit 98ed4c7c84
14 changed files with 26 additions and 20 deletions
Download patch file Download diff file Expand all files Collapse all files

2
dynamic_programming.md
View file

@ -2,7 +2,7 @@
Dynamic programming is a programming technique that can be used to make many algorithms more efficient (faster). It works on the principle of repeatedly breaking given problem down into smaller subproblems and then solving one by one from the simplest and remembering already calculated results that can be reused later.
It is usually contrasted to the *[divide and conquer](divide_and_conquer.md)* (DAC) technique which at the first sight looks similar but is in fact quite different. DAC also subdivides the main problem into subproblems, but then solves them [recursively](recursive.md), i.e. it is a top-down method. DAC also doesn't remember already solved subproblem and may end up solving the same problem multiple times, wasting computational time. Dynamic programming on the other hand starts solving the subproblems from the simplest ones -- i.e. it is a **bottom-up** method -- and remembers solutions to already solved subproblems in some kind of a [table](lut.md) which makes it possible to quickly reuse the results if such subproblem is encountered again. The order of solving the subproblems should be made such as to maximize the efficiency of the algorithm.
It is usually contrasted to the *[divide and conquer](divide_and_conquer.md)* (DAC) technique which at the first sight looks similar but is in fact quite different. DAC also subdivides the main problem into subproblems, but then solves them [recursively](recursion.md), i.e. it is a top-down method. DAC also doesn't remember already solved subproblem and may end up solving the same problem multiple times, wasting computational time. Dynamic programming on the other hand starts solving the subproblems from the simplest ones -- i.e. it is a **bottom-up** method -- and remembers solutions to already solved subproblems in some kind of a [table](lut.md) which makes it possible to quickly reuse the results if such subproblem is encountered again. The order of solving the subproblems should be made such as to maximize the efficiency of the algorithm.
It's not the case that dynamic programming is always better than DAC, it depends on the situation. Dynamic programming is effective **when the subproblems overlap** and so the same subproblems WILL be encountered multiple times. But if this is not the case, DAC can easily be used and memory for the look up tables will be saved.