Update for 26-02-22 22:45

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Tyler Perkins 2022-02-26 22:45:01 -05:00
parent 9620f6f611
commit 5494b65eea
4 changed files with 24 additions and 2 deletions

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@ -56,6 +56,7 @@ Different ways to store and operate on data, with differing efficiency
* [[randomized_algorithm|Randomized Algorithms]] * [[randomized_algorithm|Randomized Algorithms]]
* [[genetic|Genetic Algorithms]] * [[genetic|Genetic Algorithms]]
* [[swarm|Swarm Inteligence]] * [[swarm|Swarm Inteligence]]
* [[machine_learning|Machine Learning]]
* [[neural|Neural Networks]] * [[neural|Neural Networks]]
== Common operations == == Common operations ==

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= Divide and conquer =

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2) An optimal solution can be formed from optimal solutions to the overlapping 2) An optimal solution can be formed from optimal solutions to the overlapping
subproblems derrived from the original problem. subproblems derrived from the original problem.
There are two ways to implement a DP algo Generally, problems requiring a DP algo ask for the optimum value (min/max), or
the number of ways to do something *AND* future/larger descisions depend on
earlier descisions. If they do not depend on earlier descisions, then consider
a [[greedy_algorithm]].
There are two ways to implement a DP algo:
== Implimentation == == Implimentation ==
=== Bottom up (Tabulation) === === Bottom up (Tabulation) ===
For tabulation we start at the lowest sub problem and work our way up to the For tabulation we start at the lowest sub problem and work our way up to the
desired solution. desired solution. Generally bottom up is the fastest way to implement a dp
algo, as there is no overhead of recursion.
Usually implemented as iteration of the problem space
=== Top down (Memoization) ===
For memoization we start at the problem we would like to solve then descend
into the lower and lower subproblems, using a system to store the results of
our compuations as we descend. This is to ensure we do not do any unnecassy
computations. T
Usually implemented as recurssion

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= Machine Learning =
Machine learning