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# Dynamic Programming
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.
Dynamic programming is a [programming](programming.md) technique that can be used to make many [algorithms](algorithm.md) more efficient (usually meaning faster). It can be seen as an [optimization](optimization.md) technique that 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](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 is frequently 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.